Regione Toscana
Regione Toscana
Direzione Diritti di Cittadinanza e Coesione Sociale
Settore Consulenza giuridica e supporto alla ricerca in materia di salute BANDO RICERCA SALUTE 2018
Bando pubblico regionale per progetti di ricerca e sviluppo mirati al sostegno ai processi di innovazione clinica e organizzativa del Servizio Sanitario Regionale
CONVENZIONE PER LA REALIZZAZIONE DEL PROGETTO
“A nOvel accessibLe and wIdespread healthcare service Model based on technology innovation for objective (early) diagnosis and therapeutic monitoring of Parkinson’s Disease promoting contInuity of cAre - OLIMPIA”
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Xx XXXXXXX XXXXXXX con sede in Firenze, Palazzo Strozzi Sacrati, Piazza del Duomo n. 10,
C.F. e P. IVA n. 01386030488, rappresentata dal Dirigente regionale Xxxxxxxx Xxxxx nato a Pisa il 23/06/1959, domiciliato presso la sede dell'Ente, il quale interviene nella sua qualità di Dirigente del Settore “Consulenza giuridica e supporto alla ricerca in materia di salute”, struttura competente per materia, nominato con decreto n. 8071 del 04/06/2020 ed autorizzato, ai sensi dell'art. 54 della
L. R. 13/07/07 n. 38, ad impegnare legalmente e formalmente l’Ente medesimo con il presente atto, il cui schema è stato approvato con D.D n. 975 del 16/01/2020;
E
L'ente Scuola Superiore Sant'Xxxx, (di seguito denominato “Capofila”), con sede legale in Pisa, Xxxxxx Xxxxxxx xxxxx Xxxxxxx x. 00, C.F. 93008800505 e P. I. 01118840501, rappresentato dalla sig.ra Xxxxxx Xxxx, nata a Pisa il 06/06/1959, in qualità di legale rappresentante pro tempore, domiciliato per il presente atto presso la sede dell'ente o da persona eventualmente da egli delegata per giusta procura che si allega al presente Contratto, Soggetto Capofila e mandatario del raggruppamento ATS costituito a Pisa, in data 20/05/2020 con atto del Notaio Xxxxxx Xxxxxxx (repertorio n. 35, raccolta n. 23), registrato a Firenze il 22/05/2020, al n. 16786 Serie 1T tra i seguenti soggetti:
1. Scuola Superiore Sant'Xxxx - Xxxxxx Xxxxxxx xxxxx Xxxxxxx, x. 00 - Xxxx;
2. Azienda USL Toscana Nord Ovest - Xxx Xxxxxxx Xxxxxx, x. 0/0 - Xxx. Xxxxxxxxxxx, Xxxx;
3. Azienda Ospedaliera Universitaria Careggi - Xxxxx X.X.Xxxxxxxxx, x. 0 - Xxxxxxx;
4. Azienda USL Toscana Xxxxxx - Xxxxxx Xxxxx Xxxxx Xxxxx, x. 0 - Xxxxxxx;
5. Istituto di Fisiologia Clinica del Consiglio Nazionale delle Ricerche (IFC-CNR) - Xxx X. Xxxxxxx, 0 - Xxxx
PREMESSO CHE
• in data 10 ottobre 2018 sul SUPP n.170 al B.U.R.T, pIII, è stato pubblicato il Decreto Dirigenziale n. 15397 del 26 settembre 2018, di approvazione del “Bando Ricerca Salute 2018 - Bando pubblico regionale per progetti di ricerca e sviluppo mirati al sostegno ai processi di innovazione clinica e organizzativa del Servizio Sanitario Regionale”;
• con il D.D n. 16906 del 15/10/2019 sono stati approvati gli esiti della valutazione e con il
DD n. 975 del 16/01/2020 e si è provveduto alla identificazione dei progetti ammessi a finanziamento sulla base della disponibilità di fondi;
• il Progetto denominato “A nOvel accessibLe and wIdespread healthcare service Model based on technology innovation for objective (early) diagnosis and therapeutic monitoring of Parkinson’s Disease promoting contInuity of cAre - OLIMPIA”, numero CUP J44I20000760009, (d'ora in avanti denominato “Progetto”), risulta tra gli ammessi a contributo sulla base della disponibilità di fondi, come risulta dal citato decreto n. 975 del 16/01/2020;
• l’ammissione a contributo è condizionata alla verifica con esito positivo nonché al mantenimento dei requisiti previsti e dichiarati in sede di presentazione della domanda di partecipazione e ad ogni altra condizione necessaria prevista dalla normativa vigente e dal Bando;
VISTA
la normativa di riferimento ed, in particolare:
• la legge regionale n. 40 del 24 febbraio 2005 e s.m.,
• il Programma regionale di sviluppo 2016-2020 approvato dal Consiglio regionale con la risoluzione n. 47 del 15 marzo 2017;
• la deliberazione del Consiglio Regionale n. 54 del 31 luglio 2019 “Approvazione del Documento di Economia e Finanza Regionale (DEFR) 2020”;
• il “Piano Sanitario e Sociale Integrato Regionale 2018-2020”approvato con Deliberazione del Consiglio Regionale n. 73 del 09/10/2019;
• la “Strategia di Ricerca e Innovazione per la Smart Specialisation in Toscana” (DGR 1018/2014);
• la decisione G.R. n. 4 del 7 aprile 2014;
• la Delibera della Giunta Regionale n. 672 del 18 giugno 2018;
• il Decreto n. 15397 del 26 settembre 2018;
• la Delibera 648 del 13 maggio 2019;
• il Decreto 16906 del 15 ottobre 2019
TUTTO CIÒ PREMESSO
i contraenti, come sopra costituiti, convengono e stipulano quanto segue:
Art. 1 – Oggetto
La presente Convenzione ha per oggetto la realizzazione del Progetto “A nOvel accessibLe and wIdespread healthcare service Model based on technology innovation for objective (early) diagnosis and therapeutic monitoring of Parkinson’s Disease promoting contInuity of cAre” - Acronimo “OLIMPIA”.
Art. 2 – Durata
La presente Convenzione - sottoscritta ai sensi dell'art. 15 della L. n. 241/1990 e ss.mm.ii. - ha decorrenza dalla data di apposizione dell'ultima firma e della marca temporale della stessa e ha validità fino ai cinque anni successivi alla rendicontazione del progetto realizzato.
La data dell'ultima firma e della marca temporale apposta sulla convenzione costituisce la data di
avvio del progetto.
Il progetto deve essere completato entro 36 mesi dalla data di avvio del progetto.
La Regione, in accordo con l'art. 6.3 del Bando, può concedere una sola proroga delle attività del Progetto per un periodo massimo di 6 mesi, previa istanza del Capofila da presentarsi entro 60 giorni dalla data prevista di conclusione del Progetto.
La richiesta di proroga deve essere motivata e corredata da una relazione sullo stato di avanzamento del progetto e della spesa.
Art. 3 – Obblighi della Regione Toscana
La Regione Toscana si impegna a corrispondere al Capofila, nelle forme e modalità stabilite dalla presente Convenzione, un contributo fino ad un massimo di euro 736.896 (settecentotrentaseimilaottocentonovantasei) a fronte di un costo totale del progetto pari ad euro
921.120 (novecentoventunmilacentoventi) nella forma del contributo a fondo perduto. Il contributo è concesso con le seguenti modalità:
1. in anticipazione (facoltativa) fino al 40% del totale del contributo, previa presentazione di garanzia fideiussoria (tale garanzia non è richiesta nel caso di OR pubblici e di enti del Servizio Sanitario) da parte di ciascun componente dell’ATS di cui il Capofila è mandatario; la domanda di anticipo deve essere presentata direttamente a Regione Toscana entro 1 mese dalla data di sottoscrizione della presente convenzione;
2. per stato avanzamento lavori (d’ora in avanti “SAL”) – (obbligatoria), pari al 30% (proporzionalmente alle spese ammissibili rendicontate), da presentare entro 30 giorni dalla data di conclusione del primo periodo di rendicontazione (18 mesi dalla data di avvio del progetto).
La domanda a titolo di SAL deve essere presentata dal Capofila a Regione Toscana unitamente alla rendicontazione dei costi totali sostenuti e si compone di:
• relazione tecnica intermedia sullo stato di avanzamento del progetto, elaborata in base allo schema fornito dalla Regione Toscana;
• fatture o documenti contabili di equivalente valore probatorio, completi di documentazione relativa al pagamento, rappresentata dalla ricevuta contabile del bonifico o altro documento (bancario) relativo allo strumento di pagamento prescelto, in cui sia documentato il sottostante movimento finanziario, con indicazione nella causale degli estremi del titolo di spesa a cui il pagamento si riferisce (normativa antiriciclaggio D.Lgs. 231/07).
La mancata rendicontazione delle spese per almeno 30% del costo totale del progetto e/o la mancata presentazione della relazione tecnica intermedia sarà considerata come rinuncia implicita dei beneficiari alla realizzazione del progetto e, trascorsi ulteriori 30 giorni dalla scadenza dei termini, determinerà la revoca dell’intero contributo secondo le modalità e i termini stabiliti all'art. 17 del Bando.
La quota del SAL sarà erogato solo nel caso in cui sia il controllo sulla rendicontazione presentata che la valutazione sulla relazione intermedia sullo stato di avanzamento del progetto abbiano avuto esito positivo.
3. a saldo, pari alla quota restante di contributo; l’esatto ammontare del contributo da erogare verrà determinato sulla base delle spese ritenute ammissibili di cui all’art. 8 del Bando e alle “Linee guida per la rendicontazione” approvate con D.D. n. 17367 del 6/11/18.
La richiesta di pagamento saldo deve essere presentata dal Capofila, entro 30 giorni dalla conclusione del secondo periodo di rendicontazione (36 mesi dalla data di avvio del progetto o entro nuovo termine concesso dall'Amministrazione a seguito di proroga), unitamente alla relazione tecnica conclusiva.
Il saldo sarà erogato solo nel caso in cui sia il controllo sulla rendicontazione presentata che la valutazione sulla relazione finale del progetto abbiano avuto esito positivo.
L’erogazione del contributo è subordinata alla verifica del mantenimento da parte del Capofila e di ciascun componente dell'ATS dei requisiti per l’accesso al contributo di cui all’art. 5 del Bando.
Art. 4 – Obblighi del Capofila e di ciascun componente dell'ATS
Nel rispetto degli obblighi della normativa di riferimento, del Bando di cui alle premesse e della presente Convenzione, il Capofila e ciascun componente dell'ATS si impegnano a:
1. realizzare il progetto entro il termine indicato nella proposta progettuale, conformemente all’oggetto, agli obiettivi e ai risultati attesi della ricerca contenuti nel progetto approvato, ferme restando le eccezioni previste all’art. 16 del Bando;
2. comunicare, anticipatamente e tempestivamente, tutte le modifiche inerenti al progetto approvato;
3. rendicontare le spese effettivamente sostenute per la realizzazione del progetto come definito nell'art. 12 del Bando fornendo le relazioni tecniche per ciascun stato di avanzamento, al diciottesimo ed al trentaseiesimo mese dalla data di avvio progetto;
4. garantire la conservazione fino al quinto anno successivo all’erogazione del saldo della documentazione scientifica e contabile inerente la sua realizzazione;
5. consentire ai funzionari della Regione Toscana o a soggetti da essa incaricati, lo svolgimento di controlli o ispezioni;
6. rispettare gli obblighi di informazione e pubblicità previsti dall'art. 11 del bando.
Ciascun partner di progetto autorizza la Regione Toscana a pubblicare, anche per estratto, le relazioni intermedia e finale del progetto di ricerca e le relative valutazioni, nel rispetto della tutela dei dati personali e nel rispetto della tutela dei diritti di proprietà intellettuale inerenti ai risultati del progetto.
7. rispettare il divieto di xxxxxx impegnandosi per il futuro a non cumulare altri finanziamenti per le stesse attività progettuali;
8. mantenere i requisiti di ammissibilità di cui all'art. 5 del Bando per tutta la durata del progetto e comunque fino all’istanza di erogazione del saldo;
9. comunicare tempestivamente al Responsabile del procedimento, mediante PEC all'indirizzo xxxxxxxxxxxxxx@xxxxxxxxx.xxxxxxx.xx l'eventuale rinuncia al contributo.
Art. 5 – Obblighi del Capofila
Il Capofila opera in qualità di mandatario dell'ATS ammessa a finanziamento con il Progetto e, in quanto tale ha l’obbligo di:
1)assicurare il buon funzionamento e il raggiungimento degli obiettivi progettuali,
2)curare la conservazione di tutti gli elaborati tecnici, della documentazione amministrativa e contabile del progetto, separata o separabile mediante opportuna codifica dagli altri atti amministrativi generali; detta archiviazione deve essere accessibile senza limitazioni, ai fini di controllo, alle persone ed agli organismi aventi diritto e deve essere conservata per almeno cinque anni successivi all’erogazione del saldo del contributo;
3) fornire le informazioni e le documentazioni finanziarie, tecniche e amministrative del Progetto e dei partner dell’ATS richieste dalla Regione.
4) incassare le quote di contributo spettanti a ciascun partner e provvedere a liquidare, entro un massimo di trenta giorni, il contributo di competenza di ciascun partner di progetto, dando dimostrazione alla Regione Toscana dell’effettiva liquidazione ed esonerando la Regione da qualsiasi responsabilità per i pagamenti ad esso effettuati.
Art. 6 - Spese ammissibili e rendicontazione
Le spese ammissibili sono quelle indicate all’art. 8 del bando purché effettivamente sostenute dai beneficiari tra la data di avvio del progetto di cui all'articolo 2 della presente Convenzione ed i 36 mesi successivi, salvo proroga concessa ai sensi dell'articolo 2 della presente Convenzione ed all'art.
6.3 del Bando.
La rendicontazione delle spese sostenute deve essere presentata secondo le modalità stabilite negli articoli 12 e 13 del Bando e nelle “Linee guida per la rendicontazione”.
Art. 7 - Erogazione del contributo
L’erogazione del contributo è effettuata al Capofila di progetto secondo le modalità indicate all'articolo 12 del Bando e nelle Linee guida per la rendicontazione.
Art. 8 - Divieto di cumulo
Il contributo di cui al Bando ed alla presente Convenzione non è cumulabile con altri finanziamenti, contributi o incentivi pubblici concessi per le stesse iniziative ed aventi ad oggetto le stesse spese.
Art. 9 - Valutazione intermedia e finale
Il Progetto, oltre alla valutazione preliminare per accedere al finanziamento, è sottoposto a valutazione intermedia e finale dei risultati conseguiti.
La valutazione intermedia e finale verrà effettuata da valutatori individuati secondo i criteri e le modalità riportate nell'art. 13 del Bando.
Le suddette valutazioni sono effettuate sulla base delle informazioni fornite nelle relazioni tecniche intermedie e finali, allegate alle relative rendicontazioni, come specificato all'articolo 13 del Bando, e sono dirette ad accertare:
• la coerenza dell'oggetto, degli obiettivi e dei risultati conseguiti dal progetto realizzato rispetto a quello ammesso a finanziamento;
• per la sola valutazione intermedia, la potenzialità del progetto di perseguire gli obiettivi dichiarati in fase di presentazione della domanda che non sono stati ancora raggiunti;
• la congruità delle spese sostenute, il rispetto del cronoprogramma e degli altri elementi di progetto approvato.
Le relazioni tecniche intermedie e finali devono essere elaborate conformemente alle indicazioni fornite dall'Amministrazione regionale.
Le relazioni tecniche dovranno essere trasmesse - entro 30 giorni dalla scadenza rispettivamente del diciottesimo e del trentaseiesimo mese dall’inizio del progetto (o entro nuovo termine concesso dall'Amministrazione a seguito di proroga) - all’indirizzo pec xxxxxxxxxxxxxx@xxxxxxxxx.xxxxxxx.xx e contestualmente caricate in upload sul Sistema Unificato di Monitoraggio dei progetti in Toscana” (MoniToscana) all’indirizzo xxxxx://xxx.xxxx.xxxxxxx.xx/xxxxxxxxxxx.
Eventuali difformità, fra risultati attesi e risultati conseguiti, dovranno essere adeguatamente motivate.
Il Capofila dovrà fornire tutte le informazioni e le documentazioni finanziarie, tecniche e amministrative del Progetto richieste dalla Regione; dovrà inoltre fornire le attestazioni necessarie per la verifica del possesso e del mantenimento dei requisiti di cui al Bando ed eventuali integrazioni, entro un termine massimo di 10 giorni dalla richiesta, se non diversamente stabilito.
La mancata trasmissione delle relazioni intermedia e finale sullo stato di attuazione del progetto, la mancata motivazione di eventuali difformità rispetto al progetto approvato o la mancata rispondenza delle relazioni a quanto indicato nel bando comportano la sospensione delle erogazioni e l’eventuale revoca del contributo.
La Regione Toscana si riserva il diritto di richiedere, in qualsiasi momento, al Capofila una
relazione relativa allo stato di avanzamento del progetto e di organizzare incontri con il gruppo di ricerca.
Art. 10 - Proprietà intellettuale e diffusione dei risultati
I risultati, le invenzioni, il knowhow, gli eventuali dati o informazioni, compresi gli eventuali software realizzati ad hoc per la ricerca, brevettabili o meno, ed ogni altro diritto di proprietà intellettuale raggiunti o realizzati nel corso dell'attività di ricerca inerente al progetto (foreground, knowledge), appartengono congiuntamente ai soggetti beneficiari del progetto ed agli eventuali enti partecipanti, ai sensi dell’art. 4 del bando, in misura proporzionale al relativo contributo inventivo; i beneficiari e gli eventuali enti partecipanti coinvolti concluderanno un accordo atto a definire l'effettiva ripartizione e le condizioni di esercizio di tale comproprietà.
I diritti di proprietà intellettuale già sviluppati, al momento della stipula della convenzione (inizio del progetto), dai soggetti beneficiari e dagli eventuali enti partecipanti coinvolti nell’attività di ricerca (background, pre-existing know-how) rimangono di loro propria titolarità.
Ogni soggetto beneficiario e l’eventuale organismo partecipante ai sensi dell’art. 4 del bando, hanno il diritto di pubblicare i risultati del progetto di ricerca nella misura in cui derivino da ricerche da essi svolte, fermo restando l’obbligo di riservatezza nel trattamento dei risultati acquisiti, necessario per l’espletamento dell’attività relativa all’utilizzo ed allo sfruttamento degli stessi, ivi compreso l’eventuale deposito di titoli di proprietà intellettuale ad essi correlati.
Le pubblicazioni e ogni altro mezzo di divulgazione dei risultati derivanti dal progetto, dovranno riportare la seguente dicitura: “Il presente progetto di ricerca è stato realizzato grazie al contributo della Regione Toscana”- “This research project is funded by Tuscany Region”.
Ciascun partner di progetto autorizza la Regione Toscana a pubblicare, anche per estratto, le relazioni intermedie e finali del progetto di ricerca e le relative valutazioni, nel rispetto della tutela dei dati personali e nel rispetto della tutela dei diritti di proprietà intellettuale inerenti ai risultati del progetto.
Per ogni altro riferimento in merito a diritti di proprietà intellettuale e diffusione dei risultati, si rimanda a quanto previsto dallo specifico accordo, sottoscritto ed allegato alla presente Convenzione in copia conforme all’originale (Allegato 3).
Art. 11 - Ispezioni e controlli
La Regione Toscana si riserva di effettuare, in qualsiasi momento, ispezioni documentali presso i soggetti beneficiari allo scopo di verificare lo stato di esecuzione, il rispetto degli obblighi previsti dalla normativa vigente e dal bando e la veridicità delle informazioni fornite dai soggetti beneficiari stessi.
L’Amministrazione regionale procederà a controlli effettuati su tutti i soggetti finanziati ed a controlli a campione secondo le modalità stabilite all'articolo 15 del Bando.
Art. 12 - Sospensione delle erogazioni e revoche
È disposta la sospensione del contributo qualora emerga la mancata o ritardata attuazione del progetto e delle relative spese e l’inottemperanza agli obblighi di cui all'art. 4 della presente convenzione.
Il contributo sarà revocato nei seguenti casi:
a) rinuncia del soggetto beneficiario;
b) mancato rispetto degli obblighi di cui all'art. 4 della presente convenzione; per gli obblighi di cui all'art. 4 punto 2, la Regione Toscana si riserva, prima di procedere a revoca, una valutazione a proprio insindacabile giudizio della rilevanza del mancato rispetto;
c) inadempienze dei soggetti beneficiari rispetto ai requisiti soggettivi ed oggettivi di cui agli art. 3, 5 e 6 del bando, nonché tutte le altre violazioni della normativa di riferimento;
d) mancata attuazione degli adempimenti successivi all’ammissione a finanziamento;
e) esito negativo dei controlli svolti nei 180 giorni successivi alla pubblicazione sul BURT del decreto di approvazione della graduatoria.
La Regione Toscana, qualora si verifichino le circostanze che danno luogo alla revoca del contributo, comunica agli interessati l’avvio del procedimento con indicazioni relative all’oggetto del procedimento promosso, all’ufficio e alla persona responsabile del procedimento, presso i quali si può prendere visione degli atti, e assegna ai destinatari un termine di 30 giorni, decorrente dalla ricezione della comunicazione stessa, per presentare eventuali controdeduzioni o scritti difensivi, redatti in carta libera, nonché altra documentazione ritenuta idonea. La presentazione degli scritti e della documentazione di cui sopra deve avvenire con la stessa modalità utilizzata dalla Regione Toscana per la notifica dell’avvio del procedimento.
I contributi indebitamente percepiti dovranno essere restituiti dai soggetti beneficiari interessati.
Art. 13 - Difforme e/o parziale realizzazione del progetto
Costituiscono difforme e/o parziale realizzazione del progetto la:
1. non completa/parziale realizzazione del progetto e/o non corretta rendicontazione finale del progetto;
2. rideterminazione del contributo per irregolarità riscontrate a seguito di controlli a qualsiasi titolo effettuati, per le quali non si procede a revoca totale.
Nei casi di cui al comma precedente la Regione Toscana, previo contraddittorio con il Capofila, potrà procedere alla revoca parziale dell’agevolazione.
La difforme o parziale realizzazione del progetto costituisce ipotesi di adempimento difforme/parziale della Convenzione e, come tale sarà sottoposta all'approvazione del Dirigente responsabile del settore Consulenza giuridica e supporto alla ricerca in materia di salute.
Nel caso in cui vi sia stata erogazione da parte della Regione Toscana, con il provvedimento di revoca è disposta la restituzione delle somme erogate, maggiorate degli interessi maturati al Tasso Ufficiale di Riferimento (d’ora in avanti “TUR”).
Nel caso in cui alla data della revoca parziale le erogazioni siano in corso, l’ammontare da recuperare sarà detratto a valere sull’erogazione ancora da effettuare. Nel caso in cui le erogazioni ancora da effettuare risultino di ammontare inferiore a quello da recuperare o nel caso in cui si sia già provveduto all’erogazione a saldo, sarà avviata una procedura di recupero (anche coattivo secondo quanto disposto dalla legge di contabilità della Regione e dal regolamento di attuazione) nei confronti dei componenti dell’ATS interessati.
Art. 14 - Trattamento dei dati personali
I dati dei quali la Regione Toscana entra in possesso a seguito della partecipazione al Bando Ricerca Salute 2018 e per la sottoscrizione della presente Convenzione, verranno trattati nel rispetto della vigente normativa di cui al D.Lgs. 196/2003 e successive modifiche ed integrazioni e al GDPR (Regolamento UE 2016/679).
A tal fine si fa presente che:
• La Regione Toscana- Giunta regionale è il titolare del trattamento (dati di contatto: X.xxx Xxxxx 00 - 00000 Xxxxxxx; xxxxxxxxxxxxxx@xxxxxxxxx.xxxxxxx.xx)
• Il conferimento dei dati, che saranno trattati dal personale autorizzato con modalità manuale e informatizzata, è obbligatorio ed il loro mancato conferimento preclude i benefici derivanti dal Bando. I dati raccolti non saranno oggetto di comunicazione a terzi, se non per obbligo di legge.
• I dati saranno conservati presso gli uffici del Responsabile del procedimento (Settore Consulenza giuridica e supporto alla ricerca in materia di salute) per il tempo necessario alla conclusione del procedimento stesso, saranno poi conservati in conformità alle norme sulla conservazione della documentazione amministrativa.
• L’interessato ha il diritto di accedere ai dati personali che lo riguardano, di chiederne la rettifica, la limitazione o la cancellazione se incompleti, erronei o raccolti in violazione della legge, nonché di opporsi al loro trattamento per motivi legittimi rivolgendo le richieste al Responsabile della protezione dei dati (urp xxx@xxxxxxx.xxxxxxx.xx).
• L’interessato può inoltre proporre reclamo al Garante per la protezione dei dati personali, seguendo le indicazioni riportate sul sito dell’Autorità (xxxx://xxx.xxxxxxxxxxxxxx.xx/xxx/xxxxx/xxxx/xxxxxx/-/xxxxxx-xxxxxxx/xxxxxx/0000000)
Art. 15 - Registrazione e oneri fiscali
La presente Convenzione sarà registrata solo in caso d'uso ai sensi del D.P.R. n. 131/1986 a cura e spese della parte richiedente.
Ogni altra spesa relativa alla presente Convenzione, in qualunque tempo e a qualsiasi titolo accertate, è a carico del Capofila.
Art. 16 - Foro competente
Per qualsiasi controversia derivante o connessa alla presente Convenzione, ove la Regione Toscana sia attore o convenuto, è competente il Foro di Firenze, con espressa rinuncia a qualsiasi altro.
Art. 17 - Norme di rinvio
Per tutto quanto non espressamente previsto dalla presente Convenzione e dal Bando, si richiamano le norme comunitarie, nazionali e regionali vigenti in materia.
LETTO, APPROVATO E SOTTOSCRITTO
REGIONE TOSCANA IL Capofila
Il dirigente Il legale rappresentante
Firmato digitalmente da:XXXX XXXXXX Data:08/07/2020 10:14:59
ALLEGATI:
1) Scheda tecnica di Progetto;
2) Piano finanziario di Progetto;
3) Accordo di proprietà intellettuale definitivo;
PROJECT DATA SHEET SECTION 1 – GENERAL INFORMATION
Project title
A nOvel accessibLe and wIdespread healthcare service Model based on technology innovation for objective (early) diagnosis and therapeutic monitoring of Parkinson’s Disease promoting contInuity of cAre |
Project acronym
OLIMPIA |
Project coordinator (Principal investigator of the Lead Partner)
Dr. Xxxxxxx Xxxxxxx |
Term (in months – max 36 months)
36 months |
□ 1. Precision Medicine
✓ 2. Organizational and management research
□ 3. Research in oncology:
□ 3.1 Biomedical research
□ 3.2 Translational and clinical research
□ 3.3 Epidemiologic research and prevention
□ 3.4 Complementary and integrated medicine
□ 3.5 Organizational and management research
□ 3.6 Rare tumors
Indicate thematic line (indicate only one line)
Project keywords
Parkinson Disease, Early Diagnosis, Artificial Intelligence, Healthcare Service Model, Continuity of care |
Abstract EN/IT (max 3000 characters, spaces included)
N.B. By signing this document, the legal representative of the project leader authorises the Region of Tuscany to publish this summary.
The General Objective of the OLIMPIA Proposal is to identify and demonstrate the clinical and technological relevance, cost effectiveness and acceptability by stakeholders of novel patient- centred healthcare models, based on the use of wearable sensors, advanced interfaces, intelligence algorithms and cloud platforms, for prevention, monitoring and management of Parkinson's disease (PD). This novel model aims to properly manage PD patients defining new assistance paths through the use of Day Service, which can help to access diagnostic and therapeutic path and to promote the continuity of care. |
The OLIMPIA proposal is built as follow-up of previous DAPHNE project (Regione Toscana FAS SALUTE 2014, CUP J52I16000170002), where bracelet and ring shaped wearable sensor systems were developed for objective diagnosis and monitoring of PD patients and experimented on more than 300 subjects in collaboration with an Italian network of neurology created within the project. Thanks to the DAPHNE experience, XXXXXXX proposal begins from a substantial work that could guarantee an immediate integration of technologies in the current Regional healthcare processes and xxxxxx real experimentation and evaluation for concrete deployment at the end the project. The OLIMPIA proposal, particularly, aims to estimate the relative health and economic outcomes of the technological innovations, such as the use of novel ICT, Cloud, mobile and AI technologies, in the health and care sector relative to hospital and domiciliary PD services. In parallel, the XXXXXXX proposal will address specific scientific clinical challenges to improve diagnosis, monitoring and therapy of PD across its progress. Through the collaboration with general practitioner (GP), XXXXXXX model will combine an olfactory screening with wearable sensors to find patients in preclinical phase, beyond the monitoring of other PD patients. This will be pursued by 1) validating the proposed technologies as objective and useful medical device, 2) identifying methods and advanced machine learning techniques to recognize early PD symptoms from the prodromal phase. The ambition and distinctive characteristic of the OLIMPIA proposal is to develop and integrate a comprehensive technological platform that will facilitate the collaboration between all the stakeholders involved in the project (patients, specialists, GPs, caregivers, volunteers, researchers, bioengineers, healthcare managers, governmental entities and policy makers) facilitating the possibility to study and define innovative service models, overcome barriers for exploitation, i.e. ethical and social issues and cybersecurity, and improve quality of service and life of patients. For real deployment within the Tuscany healthcare services at the end of the project, the OLIMPIA will join the Rete Telematica Regionale Toscana (RTRT) and will build a Software as a Service (SaaS) solution fully interoperable with the Tuscany Internet Exchange (TIX) datacenter. L’obiettivo generale della proposta OLIMPIA è quello di dimostrare la rilevanza clinica e tecnologica, l’adeguatezza economica e l’accettabilità da parte di tutti gli stakeholders di un nuovo modello sanitario incentrato sul paziente, basato sull’utilizzo di sensori indossabili, interfacce avanzate, algoritmi intelligenti e piattaforme cloud per la prevenzione, monitoraggio e gestione della malattia di Parkinson (MP). Questo modello innovativo propone di applicare un’appropriata gestione dei pazienti con MP definendo nuovi percorsi assistenziali attraverso il Day Service, che può coadiuvare l’accesso a percorsi diagnostici e terapeutici specifici promuovendo la continuità della cura. La proposta OLIMPIA nasce come follow-up del progetto DAPHNE (Regione Toscana FAS SALUTE 2014, CUP J52I16000170002), dove sistemi di sensori indossabili sono stati sviluppati e testati su oltre 300 soggetti in collaborazione con una rete Italiana di Neurologie costituita all’interno del progetto. Grazie alla precedente esperienza, la proposta OLIMPIA parte da un’ottima base che può garantire l’immediata integrazione delle tecnologie nel Servizio Sanitario Regionale (SSR) attuale e promuovere sperimentazioni reali per il concreto utilizzo del sistema proposto al termine del progetto. In particolare, la proposta OLIMPIA mira ad analizzare e stimare le ricadute economiche e sanitarie delle innovazioni tecnologiche, come l’utilizzo di nuove tecnologie ICT, Cloud, mobile e AI nel settore sanitario e assistenziale relativo ai servizi per la MP. Parallelamente, XXXXXXX affronterà specifiche sfide cliniche e scientifiche, per migliorare la diagnosi, il monitoraggio e la |
terapia dei pazienti. Grazie alla collaborazione con i medici di medicina generale (MMG), XXXXXXX combinerà uno screening olfattivo con i sensori indossabili per individuare pazienti in fase preclinica. Questo sarà raggiunto attraverso 1) la validazione delle tecnologie proposte come dispositivi medici oggettivi; 2) l’identificazione di metodi e tecniche di machine learning avanzato per riconoscere i sintomi della MP sin dalla fase prodromica della malattia. L’ambizione e la caratteristica distintiva di OLIMPIA è quella di sviluppare ed integrare una piattaforma tecnologica che faciliterà la collaborazione tra tutti gli stakeholders coinvolti nel progetto (pazienti, specialisti, MMG, caregivers, volontari, ricercatori, bioingegneri, dirigenti sanitari, enti governativi e responsabili politici) facilitando la possibilità di studiare e definire nuovi modelli di servizi, superando le barriere per il trasferimento tecnologico, come ad esempio questioni etiche e sociali e cybersecurity, e migliorare la qualità dei servizi e la qualità di vita dei pazienti. Per la reale applicazione nel SSR al termine del progetto, XXXXXXX usufruirà della Rete Telematica Regionale Toscana (RTRT) e fornirà una soluzione Software as a Service (SaaS) completamente interoperabile con il centro dati Tuscany Internet Exchange (TIX). |
Total project cost
921’120 euros |
SECTION 2 – MASTER DATA (this Section 2 must be filled in Italian)
LIST OF PARTNERS
N° | Responsabile Scientifico1 Scientific Leader2 | Aziende USL - AOU – enti del SSR – organismi di ricerca Regional Healthcare System organization (AUSL / AOU) – Research Organization | Ruolo (*) Role | Acronimo |
1 | Xxxxxxx Xxxxxxx | Istituto di BioRobotica, Scuola Superiore Sant’Xxxx | Capofila | IBR-SSSA |
2 | Xxxxx Xxxxxxxxx | Ospedale delle Apuane, AUSL Toscana Nord Ovest | Partner | OA- AUSLTNO |
3 | Xxxxxx Xxxxx | Azienda Ospedaliera Universitaria Careggi | Partner | AOUC |
4 | Xxxxx Xxxxx | Xxxxxxxx Santa Xxxxx Xxxxxxxxxx, AUSL Toscana Centro | Partner | OSMA- AUSLTC |
5 | Xxxxxxx Xxxxxxxxx | Istituto di Fisiologia Clinica, CNR Pisa | Partner | IFC-CNR |
LIST OF PARTICIPANTS EXTERNAL RESEARCH ORGANISATIONS (art. 4 of the Call)
N° | Denominazione Organismo Ricerca / Name of Research Organization | Acronimo |
1 | University of Palermo | UNIPA |
2 | Iwate Prefectural University | IWT |
3 | Technical University of Košice | TUKE |
(*) Nella ricerca possono essere coinvolti soggetti con i seguenti ruoli (art. 3 del Bando):
a) capofila :
b) partner
the research project may involve subjecs with the following roles (art. 3 of the Call): a) leader b)partner
FORMA ASSOCIATIVA DEI PARTNERS SCELTA:
ASSOCIATIVE FORM CHOSEN BY PARNERS:
ATS costituita/constitued
✓ ATS da costituire/to be consitued
Altro (specificare)/Other (specify)………………………………………………………….
1 Il responsabile scientifico individuato dal capofila assume il ruolo di Coordinatore Scientifico del progetto 2 The scientific leader identified by the lead partner assumes the role of Scientific Coordinator of the project
SOGGETTO CAPOFILA/LEAD PARTNER
Ente/Organization | Scuola Superiore di Studi Universitari e di Perfezionamento Sant’Xxxx |
Rappresentante Legale/Legal Representative | |
Nome e Cognome / First Name and Surname | Xxxxxxxx Xxxxxx |
Ruolo nell’Ente / Role in the Organization | Rettore |
Responsabile Scientifico – Coordinatore / Scientific Leader – Coordinator | |
Nome e Cognome / First Name and Surname | Xxxxxxx Xxxxxxx |
Ruolo nell’Ente / Role in the Organization | Ricercatore |
Telefono / Phone Number | 0000000000 |
Curriculum Vitae del responsabile scientifico / Curriculum Vitae of the Scientific Leader | Allegare il CV in inglese / attach CV in English |
PARTNER
Ente/Organization | Ospedale delle Apuane – Azienda USL Toscana Nord Ovest |
Rappresentante Legale/Legal Representative | |
Nome e Cognome / First Name and Surname | Xxxxx Xxxxxxx |
Ruolo nell’Ente / Role in the Organization | Direttore Generale |
Responsabile Scientifico – Coordinatore / Scientific Leader – Coordinator | |
Nome e Cognome / First Name and Surname | Xxxxx Xxxxxxxxx |
Ruolo nell’Ente / Role in the Organization | Direttore facente funzione della Unità operativa di Neurologia |
Telefono / Phone Number | 0000000000; 0000000000 |
Curriculum Vitae del responsabile scientifico / Curriculum Vitae of the Scientific Leader | Allegare il CV in inglese / attach CV in English |
Ente/Organization | Azienda Ospedaliera Universitaria Careggi |
Rappresentante Legale/Legal Representative | |
Nome e Cognome / First Name and Surname | Xxxxx Xxxxxx Xxxxxx |
Ruolo nell’Ente / Role in the Organization | Direttore Generale |
Responsabile Scientifico – Coordinatore / Scientific Leader – Coordinator | |
Nome e Cognome / First Name and Surname | Xxxxxx Xxxxx |
Ruolo nell’Ente / Role in the Organization | Dirigente medico in Neurologia |
ALLEGATO B BANDO RICERCA SALUTE 2018 | |
Telefono / Phone Number | x00-000-0000000 |
Curriculum Vitae del responsabile scientifico / Curriculum Vitae of the Scientific Leader | Allegare il CV in inglese / attach CV in English |
Ente/Organization | Ospedale Santa Xxxxx Xxxxxxxxxx – Azienda USL Toscana Centro |
Rappresentante Legale/Legal Representative | |
Nome e Cognome / First Name and Surname | Xxxxx Xxxxxxx Xxxxxxxx |
Ruolo nell’Ente / Role in the Organization | Direttore Generale |
Responsabile Scientifico – Coordinatore / Scientific Leader – Coordinator | |
Nome e Cognome / First Name and Surname | Xxxxx Xxxxx |
Xxxxx nell’Ente / Role in the Organization | Dirigente Medico primo livello e Responsabile dell’ambulatorio della malattia di Parkinson e disturbi del movimento dell’ospedale SMA di Firenze |
Telefono / Phone Number | 000 0000000; 000 0000000 |
Curriculum Vitae del responsabile scientifico / Curriculum Vitae of the Scientific Leader | Allegare il CV in inglese / attach CV in English |
Ente/Organization | Istituto di Fisiologia Clinica – CNR Pisa |
Rappresentante Legale/Legal Representative | |
Nome e Cognome / First Name and Surname | Xxxxxxx Xxxxxxx |
Ruolo nell’Ente / Role in the Organization | Direttore facente funzioni |
Responsabile Scientifico – Coordinatore / Scientific Leader – Coordinator | |
Nome e Cognome / First Name and Surname | Xxxxxxx Xxxxxxxxx |
Ruolo nell’Ente / Role in the Organization | Ricercatore III livello II Fascia |
Telefono / Phone Number | x00 000 000 0000 - 3313 |
Curriculum Vitae del responsabile scientifico / Curriculum Vitae of the Scientific Leader | Allegare il CV in inglese / attach CV in English |
ALLEGATO B BANDO RICERCA SALUTE 2018 |
ORGANISMO DI RICERCA NAZIONALE/INTERNAZIONALE PARTECIPANTE EXTERNAL RESEARCH ORGANIZATION
Ragione sociale / Name of organization | Clinica Neurologica, Dipartimento di Biomedicina Sperimentale e Neuroscienze, Cliniche, Sezione di Neurologia, Università di Palermo |
Partita IVA /C.F (o analogo) / VAT number / Fiscal Code (or the like) | VAT number 00605880822 / Fiscal Code 80023730825 |
Indirizzo/Address | Xxx xxx Xxxxxx xx 000, 00000 Xxxxxxx (Xxxxx) |
Telefono/Phone number | 0000000000 |
PEC | |
Rappresentante legale / Legal representative | |
Nome e cognome / First name and surname | Xxxxxxxx Xxxxxxx |
telefono/Phone number | 0000000000 |
PEC | |
Referente per il progetto se diverso dal rappresentante legale / Contact person for the project (if different from the legal representative) | |
Nome e cognome / First name and surname | Xxxxxxx Xxxxxxxxx |
telefono/Phone number | 0000000000 |
PEC | |
Ragione sociale / Name of organization | Intelligent Software Systems (Fujita Laboratory), Iwata Prefectural University |
Partita IVA /C.F (o analogo) / VAT number / Fiscal Code (or the like) | N/A |
Indirizzo/Address | 152-52 Sugo, Takizawa-shi, Iwate-ken 020- 0611, Japan |
Telefono/Phone number | |
PEC | N/A |
Rappresentante legale / Legal representative | |
Nome e cognome / First name and surname | Xxxxxx Xxxxxx |
telefono/Phone number | |
PEC | N/A |
Referente per il progetto se diverso dal rappresentante legale / Contact person for the project (if different from the legal representative) | |
Nome e cognome / First name and surname | |
telefono/Phone number | |
PEC | |
ALLEGATO B BANDO RICERCA SALUTE 2018 | |
Ragione sociale / Name of organization | Center for Intelligent technologies Department of Cybernetics and AI Faculty of EE & Informatics, Technical University of Kosice |
Partita IVA /C.F (o analogo) / VAT number / Fiscal Code (or the like) | N/A |
Xxxxxxxxx/Xxxxxxx | 0, Xxxxx Xxxxxx, Xxxxxx (00000), Xxxxxxxx |
Telefono/Phone number | x000000000000 |
PEC | N/A |
Rappresentante legale / Legal representative | |
Nome e cognome / First name and surname | Xxxxx Xxxxxx |
telefono/Phone number | x000000000000 |
PEC | N/A |
Referente per il progetto se diverso dal rappresentante legale / Contact person for the project (if different from the legal representative) | |
Nome e cognome / First name and surname | |
telefono/Phone number | |
PEC | |
SECTION 3 – PROJECT DESCRIPTION
1. Idea Originating the Project: This proposal would describe the OLIMPIA project that aims at developing a novel organizational model for diagnosis and management of Parkinson’s Disease (PD). In OLIMPIA the PD would be addressed and managed in each aspect, providing a system that can offer a service model able to take care of PD patients since the beginning of the pathology and across its development. This model will be developed according to the guidelines expressed in the “National Plan for Chronicity” (i.e. “Piano Nazionale della Cronicità”), with particular attention to the main criticisms and objectives recommended for Parkinson’s Disease [1]. In this context, the proposed OLIMPIA service should be designed to offer real support to all the involved stakeholders (i.e., general practitioners, specialists, caregivers, patients) improving the quality of care and, consequently, patients’ quality of life (QoL). The proposal would encourage the approach proposed by the “Initiative Medicine” (“Medicina d’Iniziativa”), based on the Chronic Care Model [2], that promotes a proactive assistance model where the patient is taking in charge by the local services (“servizi territoriali”) to favour both primary, secondary, and tertiary prevention throughout a widespread model coordinated by the Day Service. The idea for PD, indeed, is to identify the pathology in the prodromal stage to timely intervene with neuroprotective therapy that can slow down the disease progression. At the same time, for already diagnosed PD patients, the system would offer a structured healthcare path for objective pathology assessment. Such path can include also an home monitoring service to improve the continuity of care through the patients’ empowerment and the cooperation between different healthcare providers that work both in hospital centres and local services. The home monitoring will be possible through the implementation of a telemedicine service that will favour the continuity of care enabling the full availability and accessibility of the acquired and managed data. 2. The Clinical Background: Parkinson’s Disease PD is a complex neurodegenerative disorder, which afflicts approximately more than 1.2 million of European people [3] and about 6.5 millions of people worldwide, with increasing incidence rate by 2040 [4]. Moreover the pathology is highly disabling for affected patients, with high related costs, thus, an optimal care service for that is needed. The incidence of this disease is higher in men than women and increases with age [5], but an estimated 4% of people with PD are diagnosed before 50 years old. PD is a consequence of both genetic and environmental factors, it has a usually asymmetric onset, becoming bilateral during advanced stages. Its cause is unknown, and it results in a loss of the dopamine innervation of the basal ganglia. The pathology shows a deficiency of pigmented cells in the pars compacta of the substantia nigra; these cells contain neuro-melanin and produce neurotransmitters of dopamine. When cell loss exceeds 60% there is a critical deficiency of dopamine in the forebrain, which results in typical cardinal motor symptoms [6] such as tremor, postural instability, muscle rigidity, and bradykinesia. The pathology is characterized also by many non-motor manifestations (NMMs) (e.g., sleep disorders, bladder disturbances, olfactory symptoms), which are widely disabling for PD patients [7]. Recent studies demonstrate that several NNMs (e.g., rapid eye movement sleep behaviour disorders, hyposmia, constipation, depression) are correlated to the neuropathological changes in the brain and they can anticipate the motor manifestations of the disease by 5–7 years. Thus, an increasing interest is emerging also toward NMMs, with the aim to investigate and treat |
the pathology since its beginning, when small neurological areas are damaged and neuroprotective therapies could slow down the PD progression [8]. Over the past three decades, the knowledge of PD has increased significantly, with particular interest on the premotor phase and novel therapeutic and diagnostic approaches [9]. Currently, experts recognize the need to redefine the research criteria for the diagnosis of this complex disease by considering clinical features, pathological findings, and genetics or molecular mechanisms [10]. The wide variability in clinical expression, as well as in the progression of somatic symptoms [11], make the pathology difficult to adequately identify and treat [12]. Particularly, as the pathology onset appears unilaterally, with specific impairments, and it develops differently among patients [13], it makes sense that a personalized and adapted therapy should be administered based on the individual needs of PD patients to enable optimal care. Finally, the long-term development of the disease, expression of symptomatic complications (e.g., motor fluctuations) during specific times of day, long waiting lists, and traveling costs (particularly for people who live in rural areas) are just a few reasons that support the need to move Parkinson’s care into the home and to develop new care models [14]. 2.1 The Current Diagnostic Clinical Practice Today, PD diagnosis is based on the assessment of motor and non-motor symptoms, typically during neurological visual examinations, according to diagnostic criteria [15] but the diagnostic methods and disease progression monitoring approaches remain suboptimal for PD management [16]. This is particularly true when co-factors such as greater age, poor cognition, and worse mobility are manifested [17]. During the test for PD diagnosis and evaluation, in fact, the neurologist watches the patient perform specific tasks and assigns scores for each of them as required and defined in the Unified Parkinson’s Disease Rating Scale (UPDRS) [18], or its updated version, the Movement Disorder Society-sponsored revision of the UPDRS (MDS-UPDRS) [19]. Another typical assessment tool is the Xxxxx and Yahr scale (HY) [20] that is used to assign an overall score to the patient on the basis of the pathological progress, according to the following stages: • HY = 0: No signs of disease; • HY = 1: Unilateral disease; • HY = 2: Bilateral disease, without impairment of balance; • HY = 3: Mild to moderate bilateral disease; some postural instability; physically independent; needs assistance to recover from the pull test; • HY = 4: Severe disability; still able to walk or stand unassisted; • HY = 5: Wheelchair-bound or bedridden unless aided. Further, to confirm PD diagnosis, nuclear medicine tomographic imaging techniques are currently mandatory, and these methods are costly, lengthy processes, which require specially-trained dedicated staff. Among them, the most used exam is the single proton emission computed tomography—SPECT—that is a cerebral scintigraphy performed using the DaTSCAN®, a substance developed by General Electric Healthcare to reveal the presence of dopamine transmitters in the brain. Since PD is caused by a critical deficiency of dopamine in the forebrain, scanners of PD patients, for instance, are able to show low level of dopamine [21]. Nevertheless, this method is expensive and highly invasive because exposes people to radiations. Alternatively, not invasive, but expensive, techniques are Transcranial Sonography (TCS) that can reveal structural midbrain and striatal changes in PD but it has low specificity (< 82%) [22], and nuclear |
magnetic resonance with diffusion tensor imaging (NMR-DTI) that investigates the PD pathophysiology studying the white fibre integrity [23], enabling the identification of lesions even in the early clinical stages of the disease [24]. 2.2 Current Diagnostic Limitations and Criticisms The correct diagnosis of PD is of vital importance for adequate prognosis and treatment, although a study reveals that approximately 25% of diagnoses are incorrect, particularly when essential tremor, vascular Parkinsonism, and atypical Parkinsonian syndromes are manifested [25]. An exhaustive study of the pathology, including a more accurate knowledge of its clinical appearance and other tests such as olfactory exam and magnetic resonance imaging (MRI), could guide the correct diagnosis [25]. Currently, Parkinson’s disease is diagnosed when the bio-pathological process has already started and the neurodegenerative process has yet compromised wide brain areas. This happens because patients usually go to the clinician when motor symptoms are clearly evident and begin to influence their common activities. Further, the clinical scales adopted during neurological examinations, that were described above, are semi-quantitative, and the assigned scores are subjective. This fact leads to high interrater variability among different neurologists or different medical centres, as well as high intra-rater variability over time. Specifically, in the MDS-UPDRS section III motion is evaluated and patients are asked to perform detailed tasks (e.g., finger and foot tapping, gait), which are visually observed and assessed by neurologists. However, small worsening in subjects’ performance are difficult to detect also for expert clinicians, thus PD is identified when motor impairments are manifested. Moreover, neuroimaging techniques, that are currently needed to confirm PD diagnosis, are invasive (i.e., expose people to radiation), expensive, lengthy processes, requiring specially trained dedicated staff, thus they can be reasonably applied only when neurologists suppose a probable PD diagnosis. Ultimately, the treatment for PD is still a matter of debate, especially in the early phases. Common sense says that the therapy must be personalised and adapted to the individual needs of PD patients to provide the best medical care and treat the predominant symptoms [26]. 3. The Socio-Clinical Hypotheses of OLIMPIA Starting from the above considerations, the research on PD can be pursued based on five main socio-clinical hypotheses: • HP1 – Subclinical latent phase - In PD latency between the beginning of the neurodegenerative process and the appearance of the typical motor symptoms of the disease that lead the clinician to the diagnosis is about 5 years [27, 28], so the diagnosis is expressed when the disease is already widely involved. It may reasonably be supposed that subjects show a progressive worsen of motor skills over the time that could be early identified by an accurate instrumentation, allowing an anticipation on PD diagnosis of 5-7 years compared to date. • HP2 – Idiopathic Hyposmia - Hyposmia is a PD co-morbidity in more than 95% of PD patients [22], and, in healthy subjects, idiopathic hyposmia (IH) is associated with an increased risk of developing PD of at least 10% [29, 30]. Combining this information, subjects with IH are supposed a convenient reference group in a prodromal state to test tools and procedures to early diagnose the coming PD. • HP3 – Pharmacological benefits - Pharmacological therapies are essential to slow down the disease and to reduce the effects of its typical symptoms so they are periodically assigned and modulated by clinicians according to the status of patients. Therapies assume a fundamental role for PD treatment from the onset throughout pathology development. In particular, |
neuroprotective therapies give the best benefits when administered in the early stages of the disease [8] allowing a delay in the administration of levodopa and increasing the phase of compensation of the disease, while for advanced stages long-term monitoring can be needed in order to observe therapy response over the time. • HP4 – Interventionist non-homogeneity – At National level the interventions for PD management are very varied [1]. Even if there are some centres of excellence for PD highly specialized, the common reality is more fragmented. Only a few of Regions provided specific diagnostic, therapeutic, assistance pathways for PD (i.e., the so-called “PDTA”). Moreover, generally healthcare professionals, general practitioners (GPs) and specialists do not have an efficient communication, and hospital centres and local services are not well integrate to ensure to patients the necessary continuity of care. • HP5 – Home monitoring – The home care is particularly deficient in Italy, especially in some Regions [1]. The home monitoring service can be very useful both for mild to moderate PD patients, empowering them in self-management of own disease, and for advanced PD who can benefit from a telemedicine support assistance. 4. The Challenges of OLIMPIA In the above described context, specific key challenges have been identified to optimize the PD management from different points of view, including clinical, scientific, and technical topics. Clinically, the most important issues are surely related to the opportunity to achieve a quantitative objective PD diagnosis (Clinical Challenge 1 – CLCh1), with particular interest towards early diagnosis (Clinical Challenge 2 – CLCh2). To address the clinical challenges, the doctors have to be provided with a reliable system able to acquire, measure and return the motor performance of the analysed patients, to support the clinicians in objective diagnosis. For this reason novel appropriate algorithms will be developed and consolidated (Scientific/Technological Challenge 1 – STCh1) for extracting parameters of interest in accurately identifying motor impairments caused by the pathology onset and progression. The large amount of data to manage (included clinical data, raw data acquired by sensors, elaborated data as biomechanical features) surely require the investigation of advanced Machine Learning techniques (Scientific/Technological Challenge 2 – STCh2) that can be applied for novel analysis based on raw data processing as well as for providing aggregate results and improving data usability and availability. However, these clinical/scientific challenges have to be synergic addressed by clinical and technical staffs to lay the basis for a reorganization of the current healthcare model for PD. According to the guidelines expressed by the Initiative Medicine, within the Chronic Care Model, and following the recommendations of the National Plan for Chronicity, the main challenge of OLIMPIA is related to the healthcare service, with the need to reorganize the current model (Healthcare Service Challenge 1 – HSCl1) in order to improve the quality of care, with great benefit for patients (and their caregivers/relatives) Quality of Life (QoL). Finally, an important topic to address for increasing the efficacy and the continuity of care is directly engaging the patients in their healthcare path, moving part of the care at home (Healthcare Service Challenge 2 – HSCl2), providing a telemedicine service for assistance and monitoring that can be useful also to better monitor the response to pharmacological therapy changes and relatedly adjust the treatment. Summarizing, XXXXXXX aims to deal with the following challenges: • CLCh1 – Objective Diagnosis: the possibility to accurately measure the motor performance of the PD patients can provide a valid support to clinicians for quantifying the |
PD level and progression over the time. Moreover the objective assessment can overcome the high variability that currently characterises the PD diagnosis, reducing also misdiagnosis. Accurate evaluations are fundamental to assess the pathology and consequently treat it as the best as possible. • CLCh2 – Early Diagnosis: currently there is not a standard procedure for early PD diagnosis. The PD identification in a prodromal stage when typical symptoms are not yet clearly expressed but the neuropathological process is already started can allow timely interventions, acting in a proactive way, providing the patients with opportune guidelines and neuroprotective therapy to slowdown the disease. • STCh1 – Innovative Algorithms: dedicated innovative algorithms can ensure accuracy of extracted measurements to give to the clinicians a decision support system reliable and precise for objective diagnosis. The automation of such algorithms can also furnish an automated assessment of the motor performance, offering timely feedbacks both to doctors and patients. Furthermore new parameters can be investigated and measured to provide a complete motion evaluation taking into account also specific impairments (e.g., freezing of gait, hesitations). • STCh2 – Machine Learning: the application of innovative machine learning techniques is mandatory to analyse, compute and aggregate the big amount of data acquired and collected in OLIMPIA. Additionally to the traditional machine learning approaches (i.e., supervised and unsupervised), deep learning can supply new information from handled data, as well as can allow the management of Big Data. • HSCh1 – Healthcare Service Reorganization: specific diagnostic, therapeutic, assistance pathways (i.e., the so-called “PDTA”) for PD patients have to be defined and followed to ensure the best care possible for every subjects on the basis of their pathology stage, according to the Regional, National and International guidelines of the Initiative Medicine based on the Chronic Care Model. Cooperation between different healthcare professionals, caregivers and patients should be encouraged, as well as between hospital centres and local services. The model proposed in OLIMPIA would address all these issues in order to create a service suddenly available within the Regional Healthcare Service and, at the same time, easily exportable at National and International levels. • HSCh2 – Home Monitoring and Therapy Adjustment: the opportunity to move the care also at home can have positive feedbacks on quality of care and patients QoL. Since PD is a long-term pathology, PD patients have to be engaged to improve self-management. According to the disease level they could periodically measure their motor performance at home avoiding travelling costs and long waiting lists for neurological examinations in hospital. Telemedicine support can be provided, and clinicians can remotely observe and monitoring the patients through adequate interfaces that enable the access to the Cloud platform where data are stored and processed. Furthermore, specific times of the day can be more significant for disease assessment (e.g., ON/OFF phases), thus the PD patients can measure their impairments when they have really need. Motor performance changes can be controlled and related therapy adjustments can be prescribed timely. In particular, the clinicians will be provided with an accurate wearable system composed by inertial sensor devices to acquire motor data from PD patients during the performance of the tasks typically executed during the traditional neurological examinations. These data will be stored in a dedicated database, online processed (STCh1, STCh2), and useful feedbacks about the results of the patients motion analysis and related clinical data will be automatically updated to the |
neurologist. Therefore, the technological system will be able to accurately quantify the motor performance of the analysed subjects providing the clinician with an useful support for objecting the clinical diagnosis of Parkinson’s disease, overcoming the current problems and misdiagnosis due to high intra- and inter-rater variability assessment (CLCh1). Moreover, the system will be able to identify subtle worsening in motor performance that currently are not evident upon expert traditional clinical exams, thus it could be used to analyse motion of people at risk for developing the pathology (i.e., subjects affected by idiopathic hyposmia) for those typical cardinal symptoms are not already expressed (CLCh2). Furthermore, the neurologists could identify specific PD patients that are appropriate to use the wearable system also at home (i.e., patients with motor fluctuations, patients that require frequent adjustments in pharmacological therapy), so the OLIMPIA system will be tested also in unstructured environment, through the support of a telemedicine service. (HSCh2). High cooperation level will be encouraged between GP and specialists, since the first ones will be involved in the local screening for olfactory disturbance and they will address the suspected PD patients towards the specialists in the hospital centre and the appropriate PDTA (i.e., Day Service). Moreover, they will be the primary contact for diagnosed PD patients that will benefit from the home monitoring service. Finally, XXXXXXX will improve the management of Parkinson's disease throughout its course, i.e., from the preclinical phase of the disease to the clinical phase, with important benefits for the patient QoL and significant social repercussions in terms of reducing the costs incurred for patient care. 5. The OLIMPIA Service Model 5.1 The current Regional Healthcare service care model For patients with chronic diseases it is important to improve the clinical picture, the functional status, and the QoL. These targets can be reached through a proper management of patients made by the definition of new assistance paths, which have to take care of people guaranteeing a continuum of care and the integration of the healthcare sector. For this reason, the “Piano Nazionale delle Cronicità” proposed an opportune system for the management of chronic disease basing, among others, on the Chronic Care Model (CCM), which enhances the interactions between the patients and all the healthcare actors. The CCM has been broaden into the Expanded CCM (ECCM) integrating in the model the public health system going to a community oriented primary care. The ECCM is based on four areas of focus, which are: • Self-Management Support: patients should be encouraged to become active in the management of his or her health and health care, monitoring their own conditions among other actions; • Delivery System Design: patients with chronic diseases should have an appropriate path for their assistance different from the one for acute illness. The clinical and assistance activity should be integrated with programmed follow-up interventions according to the level of disease; • Decision Support: help clinicians and patients during the care process of chronic condition through adoption of guidelines based on evidence to ensure an optimal assistance. This area of focus should be active not only in the management of the disease, but also in promoting strategies for wellbeing; |
• Information System: data should be organised to facilitate efficient and effective care, including sharing the information between all the persons involved in the care process and the patient. Also information regarding the community should be added to improve the planning of programs, policies and other initiatives [31]. The ECCM describes a new model for healthcare where the whole system is focused on integrating the “population health promotion” into the prevention and management of chronic disease. Regarding the management of chronic disease, the whole healthcare system should be focused on providing the best continuity of care for patients with chronic diseases. According to this idea, all the persons involved in the process of care, from the primary care to the specialized doctor, are part of this system where patients are followed in the process of care also thanks to the Diagnostic Therapeutic and for Assistance Path (so called “Percorsi Diagnostici Terapeutici Assistenziali, i.e. PDTA). The definition and the implementation of such path should ensure the avoidance of any discontinuity in the different layer of the assistance (primary assistance, specialized examinations and hospitalization). The PDTA is based on the capillarity and the integration of all the persons involved in the care process, from the specialized doctor to the patient, which have to be trained and empowered in the management of his/her health. According to the “Piano Nazionale delle Cronicità”, to properly manage chronic diseases it is important to have a predefined path that involves a continuous multidisciplinary assistance, to allow long term personalized care project and improve the QoL. It is important therefore to organize all the services also according to the level of disease and related needs, guaranteeing full path for every patient. These services have to be arranged integrating all the stakeholder of the care process, from the specialist to the GP to the formal and informal caregivers, starting from the first access, when the patient begins the PDTA. In order to guarantee the appropriate service to each patient, it is necessary to create diversified and personalized path according to the different level of the disease and related needs. The patients themselves have to be part of the process of care having the ability to manage the disease so to improve their QoL. An integrated management is therefore crucial to guarantee appropriate care of chronic diseases. This integration starts with the definition and the activation of appropriate PDTA that have to be personalized according to the gravity of the disease and the complexity of the patient, considering also a standardization in terms of costs and quality of care. From this perspective, it is necessary to realize a continuity in the assistance that allow an easy access to diagnosis and therapy through all the stakeholders and that tries to go towards the persons with procedures, which can permit an early diagnosis of the disease. The Day Service is the best tool to develop this integrated service In this context. The Day Service, in fact, provides an easy and fast access to specialist examinations to people in need of diagnosis or treatments (care). People can access to the diagnostic and therapeutic path through a simple prescription made by a specialist or by the practice where the citizen is firstly visited sent by the GP. The Day Service represents an innovative assistance model that is placed between the “Day Hospital” hospitalisation and the traditional examination. The Day Service is focused on the management of those diseases, which request clinical multidisciplinary examinations. These tests should be part of a diagnostic and therapeutic path focused on the whole clinical problem of the patient and not only on the single examination. It is important that the patient is able to easily access to this path through a management system for the reservation of the examination consistently with the diagnostic path. Through the Day Service the patient can have access to all those examination defined before in the Complex and Coordinated Clinique Paths (so called “Percorsi Ambulatoriali Complessi e |
Coordinati”, i.e. PACC). The PACC represent the operative instrument for the assistance in the Day Service. The PACC have to be defined according to specific needs of each pathology and are focused on giving a fast and appropriate answer to the patient avoiding unnecessary fragmentation. The Day Service become a bridge between the primary care, the local services and the hospital. The patient is therefore the centre of the care and assistance process where all the stakeholders are involved. 5.2 OLIMPIA proposed innovative approach Due to its multidisciplinary organization, the Day Service is the most appropriate unit to take in charge the PD patients, from the preclinical to the clinical phase. Moreover it represents the best tool to apply the OLIMPIA service, whose aim is to provide a service focused on the diagnosis and monitoring of PD patients during all the phases of the disease improving the quality of care and the QoL for them. In practice, the Day Service for the PD in the OLIMPIA project is made of: • a clinic (“ambulatorio”) for diagnosis and monitoring: the patient access to this clinic sent by the GP (in case of positive results to the olfactory screening) or from the specialist. Here the patients can have access to the examinations they need. For PD patients, the day service will allow them to have an easy access to the examination with the neurologist, who will use the wearable sensors for an objective evaluation of the motor performance. For persons that are positive to the olfactory test the Day Service will give them access to all the examinations needed to verify whether they have already the PD or not. Among these examinations, there will be the test with the wearable sensors to evaluate in an objective and accurate way the motor performance and evaluate potential difference with healthy people. After all the examinations, the GP and the local service will take care of the patients; • administrative/ health call center: once the patient is taken in charge by the Day Service, it will help them to plan the path, managing medical reports and information exchange with GP and local services; • local and at home services: this team will be integrated in the OLIMPIA service interacting both with the GP and the specialist to take care of the more complex patients that need care even at home; • a multidisciplinary team: this team will be made of several specialists (neurologist, neuropsychologist, otolaryngologist, radiologist) that will be involved in different ways according to the level of disease of the patient; • cloud based infrastructure: all the data and information about the patient will be uploaded on this cloud based infrastructure where the biomedical parameters will be also evaluated and used by the neurologist to have an objective diagnosis and monitoring of the PD patients. These information will be available to all the stakeholders with different level of accessibility according to role of the stakeholder. The access to these data to the patient could be made by the use of clinical records such as the “Fascicolo Sanitario Elettronico” already enabled by the Regione Toscana. The service proposed by the OLIMPIA project has several advantages for all the stakeholders of the healthcare process. In particular, the patient is at the center of the care process: thanks to the support of the GP, the specialists and the local services, he/she can quickly enter and complete the diagnostic process, avoiding multiple examination and beginning the therapy as soon as possible. The specialist can use all the diagnostic examinations included the use of wearable sensors for an objective and accurate measurement and is part of an integrated system where all |
the stakeholders can contribute, including GP, who become more integrated in the care process. Finally, also the healthcare system can have benefits from this service that avoid duplication of examinations that sometimes happens in the ordinary clinic path.
Beyond the use of the Daily Service, the OLIMPIA service foresees also the use of the wearable sensors at home to provide more frequent monitoring of the patient and consequently to adjust the therapy more frequently according to the assessment made by the patient. The OLIMPIA system will enable the patient to autonomously (or with the help of a caregiver) make the tasks necessary for the automatic evaluation of the motor parameters and send the results to the specialist that can modulate the therapy also in collaboration with the GP and other relevant actors of the care process. The OLIMPIA project aims to develop an user friendly tele-monitoring system that allows to remotely monitor some motor parameters to better manage the PD patient. This system will allow to quickly identify subtle worsening of the disease, enabling to the neurologist to quickly intervene. In this way, not only the overall care service will be more integrated, but also the patient will be able to monitor himself, being thus more empowered. The telemedicine will be therefore used to extend the monitoring of the patients out of the hospital and will make the patient aware and feel more assisted improving therefore its QoL.
In order to provide the best assistance to every patient, the OLIMPIA service has been evaluated for each level of disease, as detailed in Table 1.
Table 1. The proposed personalized service in OLIMPIA according to the pathology level of PD patients taken in charge.
KIND OF PATIENT
PERSONALIZED PATH
Asymptomatic Patient – Subject with Idiopathic Hyposmia (55-
69 years old)
Enrolled by the GP and resulted positive to the olfactory screening, he/she is taken in charge by the Day Service to evaluate whether he/she has the PD. In particular, the patient will undergo different examinations, included the motor assessment with wearable sensors to evaluate also motor symptoms still not visible by the neurologist. In case some of the evaluated parameters results different from reference range values calculated on healthy people, the person will undergo to the dopaminergic challenge test with levodopa drug, which is specific for identifying idiopathic PD. A positive response to the test will justify the use of a SPECT-DaTSCAN to confirm or not the PD. Whether the SPECT-DaTSCAN confirms the PD, the patient (in preclinical phase) will start the neuroprotective pharmacological therapy.
HY 1 Patient + Preclinical Patient
The Day Service takes in charge the patient and, through the network composed by the GP, specialist and local service, monitor the onset of the disease and the pharmacological therapy. In this case, during the examination with the specialist, the wearable sensors are used to evaluate the motor pattern and quickly identify worsening.
ALLEGATO B BANDO RICERCA SALUTE 2018 | |||
HY 2/3 Patient | • The Day Service takes in charge the patient and, through the network composed by the GP, specialist and local service, monitor the progress of the disease and the pharmacological therapy. In this case, during the examination with the specialist, the wearable sensors are used to evaluate the motor pattern and quickly identify worsening. • Home monitoring thanks to the wearable sensors and user-friendly app. The patients, adequately trained, can use the sensors at home to monitor more frequently the motor performance and allow the neurologist to properly intervene. Moreover the wearable sensors can be used to better analyse the on-off phases that affected these patients during the day. | ||
HY 4 Patient | • The Day Service takes in charge the patient and, through the network composed by the GP, specialist and local service, monitor the progress of the disease and the pharmacological therapy. In this case, during the examination with the specialist, the wearable sensors are used to evaluate the motor pattern and quickly identify worsening. • Home monitoring thanks to the wearable sensors and user-friendly app. The patients can use the sensors at home to monitor more frequently the motor performance and allow the neurologist to properly intervene. Moreover the wearable sensors can be used to better analyse the on-off phases that these patients have during the day | ||
HY 5 Patient | • The Day Service takes in charge the patient and, through the network composed by the GP, specialist and local service, monitor the progress of the disease and the pharmacological therapy. The patients mainly stay in bed, thus they can use the telemedicine service of OLIMPIA in terms of monitoring and assistance, without the use of the wearable sensors. • The patient is assisted by the local service to help him/her during the day with basic ADLs. | ||
Figure. 1 OLIMPIA service-care model. In blue the services currently provided by the Regional Healthcare services whereas in green the proposed integration of OLIMPIA services. The OLIMPIA care services are consequence of some real clinical cases already identified by the Consortium through their experience in working with several patients with PD or PD pre-clinical markers. Three possible concrete scenarios of use of the OLIMPIA system are following presented with a preliminary analysis of stakeholders' needs (Table 2). a. SCENARIO 1: Early Diagnosis: From the territory to the Hospital To allow the diagnosis of the PD in a preclinical phase and start the pharmacological treatment before the onset of motor symptoms, the GPs will be fundamentally involved in the OLIMPIA model. The GPs will be asked to enrol subjects whose age ranges from 55 to 69 years old to administer them an olfactory screening, such as the Italian Olfactory Identification Test (IOIT) [30]. After having signed the appropriate documents they will undergo to identify the ones affected by idiopathic hyposmia. Two groups of people will be therefore identified: the normosmic and the hyposmic. These two groups will follow two different paths. On one hand, the normosmic persons will be involved in OLIMPIA project as healthy subjects of control (HC). They will be taken in charge by the Day Service that will manage the compilation of the Case Report Forms (CRF), neurological examination, the neuropsychological tests and the motor assessment through wearable sensors. All the results will be uploaded on the Cloud database and, in particular, the outcome of the HC motor assessment will be used to consolidate the information about the motor pattern of healthy people, in order to define normative range of reference for the motor performance. On the other hand, the hyposmic people will be taken in charge by the Day Service that thanks to the intra and extra hospital network will manage all the examinations that this |
group of people will have to do (i.e., compiling of the Case Report Forms (CRF), neurological examination, neuropsychological tests, motor assessment through wearable sensors, visit with otolaryngologist, encephalon MRI scan or cranium CT scan). The hyposmic people that after these exams will result affected by idiopathic hyposmia (IH), will be involved in OLIMPIA project as group of reference for investigating the possible PD development in early stage. All the outputs of these tests will be uploaded on a Cloud database, where all the information will be collected and the data from the wearable sensors will be analysed through intelligent algorithms to extract significant parameters that can help the specialist to have an objective measurement of the motor performance in IH subjects. In case one of the performed tests, and in particular the outcomes of the motor assessment through wearable sensors, will show, for IH subjects, some deviation from the normal parameters (based on HC performance), a dopaminergic challenge test with Levodopa (L- dopa) will be administered them [32]. This sub-acute test, which foresees the gradual increment, achieving of maximum dose, and then gradual decrement of the L-dopa drug over a month will be periodically monitor with the wearable sensors. A positive response to this test (i.e., improving in performance at the maximum dose and decreasing when the drug is stopped) can induce the neurologist to suspect a PD diagnosis. Then, a SPECT DaTSCAN test will be used to confirm whether the persons is affected by PD. People, which after the SPECT DaTSCAN will be diagnosed with PD in a preclinical phase, will start the neuroprotective pharmacological treatment and will then follow the monitoring path described in the next scenario. Synthesising, the early PD could be diagnosed through a four-step approach, which includes: • Identification of IH through a low-cost standard olfactory test; • Measurement of an altered motor pattern, compared to the normal data assessed in an HC group; • Improvement of motor performance due to dopaminergic stimuli with the pharmacological test with L-dopa; • Demonstration of an alteration of the xxxxx-striatal dopaminergic system using the diagnostic nuclear medicine method SPECT DaTSCAN. b. SCENARIO 2: PD objective assessment: An improved and uniform assistance service for patients Due to the nature of the PD (chronic with a progressive worsening), patients with PD should periodically undergo through neurological examination also for adapting the drug therapy. In order to improve the current clinical evaluation (subjective evaluation), an objective measurement system will be used in OLIMPIA to help the neurologist to evaluate also subtle changes in the motor performance and change the therapy accordingly. In particular, after the enrolment in the OLIMPIA Project, PD patients in preclinical and clinical phase will be taken in charge by the Day Service to manage all the necessary tests (i.e., compiling of the Case Report Forms (CRF), neurological examination, neuropsychological tests, motor assessment through wearable sensors, olfactory screening, visit with otolaryngologist, encephalon MRI scan or cranium CT scan). All the data will be uploaded on a database, where all the information will be collected and the data from the wearable sensors will be analysed to extract significant parameters that can help the specialist to have an objective measurement of the motor performance, thus improving the objective diagnosis of PD as |
well as the level assessment of the pathology over the time. Even on these parameters a statistical analysis will be carried out to increase the correlation between the obtained parameters and the gravity of the disease. The patients, thanks to the integrated system provided in OLIMPIA (wearable sensors, Day Service and cloud infrastructure), will be at the centre of the care process and the clinical staff will be provided of a decision support system that can enable an objective measurement of the motor performance able to identify also subtle changes and therefore empower the neurologist to administer an optimal drug therapy. c. SCENARIO 3: Monitoring @ home: From the hospital to the territory enabling self management and continuous monitoring Since the neurological examination in the hospital is not sufficient to monitor the on-off phase of the PD patients (i.e., motor fluctuations during the day), the use of the wearable sensors at the patients’ place will help to record and examine the fluctuation phase to better adapt the pharmacological therapy. The home monitoring will help the specialist to monitor the disease progression and the drug efficiency in a more continuous way. In practice, some patients (30 subjects) in phase HY2-3 with on-off phases will be enrolled for a pilot study at home. Half of the group will be in charge of the Day Service with regular examinations. The other half of the group (that must have a good internet connection to guarantee the telemedicine requirements), apart from being in charge of the Day Service, will be provided with the OLIMPIA wearable sensor kit. The patient himself, or the caregiver, will be properly trained to use autonomously and correctly the system. In this way, the patients can have a regular assessment of the motor performance and the data can be uploaded to the database, for storing, extraction of the parameters, and remote analysis by the neurologist. The information will be also sent to the GP. Whether the neurologist notices a significant worsening in the motor performance that request for a change in the therapy, the specialist will send the new pharmacological therapy to the patient and to the GP. If some doubts about the incorrect use of the sensors arise due to a malfunctioning or a bad positioning, the Day Service will contact the territorial team to send an operator at the patients place to check the sensors and the correct use of them. So the unique difference between the two groups will be based on the frequency of measurements and the consequent change in the pharmacological therapy. This will allow to have a first evaluation of the effect of more frequent assessment of the motor performance and consequently of a less sporadic monitoring of the patient by the neurologist. The use of wearable sensors at home, beyond enabling the specialist to have more frequent measurement of the motor performance, allow to have also a comparison between the measurement taken at the hospital and the ones taken at home (in the following days with respect to the hospital visit). In this way the reliability of the measurement will be further demonstrated thanks to the possibility to compare the data obtained with a measurement made at the hospital by expert people and the ones obtained at home by the patients or the caregiver, overcoming the variability linked to different operators. |
Table 2. Preliminary analysis of stakeholders' needs
STAKEHOLDERS’ NEEDS | |
Patients | Relatives and informal caregivers |
- To detect the disease very early and to begin promptly the care. | - To avoid the early worsening of the patient's motor abilities so to limit the need of |
- To slowdown the worsening of the motor abilities. - To be constantly monitored with personalized therapy. | assistance services. - To avoid the necessity to accompany frequently the patient to specialized centres for medical examination and physical activity. |
- To avoid the necessity to frequently go to specialized centres for medical examination | - To diminish the costs for medical examination and transport to clinical centres. |
and physical activity. - To improve QoL as well as to sustain and improve the PD patients' level of social | - To follow the patient's physical and social activity, and provide remote support. |
activity. | |
Service providers - To detect the disease very early and to begin promptly the care. - To prescribe the right therapy on the base of the current patients conditions. - To remotely monitor the patient. - To decrease the time to spend for a patient thus to increase the number of patients to treat. | Local community, society and healthcare system: - To maintain longer the motor abilities in PD patients. - To diminish need of assistance for PD patients. - To diminish waiting lists for medical examinations in clinical centres. - To diminish the costs for PD care (therapy, rehabilitation, examinations,…) augmenting the efficiency of PD care system. |
6. The OLIMPIA system
6.1 The wearable system
The wearable system for OLIMPIA is an evolution of the devices developed and preliminary tested within the DAPHNE project (see Figure 2) for the analysis of the motor performance both in upper and lower limbs.
Figure 2. DAPHNE wearable system, composed by SensHand (on the left and the center) for upper limb motion analysis and SensFoot (on the right) for lower limb motion analysis.
• SensFoot
The SensFoot device consists of an IMU integrated into the iNEMO-M1 board based on MEMS sensors, equipped by the inertial module LSM9DS1 (3-axis digital gyroscope, 3-axis
accelerometer, 3-axis digital magnetometer) and dedicated ARM-based 32-bit microcontroller STM32F103RE (STMicroelectronics, Italy). The LSM9DS1 has a full scale for linear acceleration of ±2g / ±4g / ±8g, a full scale for the magnetic field of ±4 / ±8 / ±12 / ±16 gauss and a full scale of ±245 / ±500 / ±2000 degrees per second for the angular velocity. The LSM9DS1 includes an I2C serial bus interface that supports standard and fast modes (100 kHz and 400 kHz) and a standard SPI serial interface. The scales were set respectively equal to 2000 degrees/sec for the gyroscope, 8 g for the accelerometer and 8 gauss for the magnetometer. The system is integrated with a Bluetooth module (BMD-350, 2.4GHz transceiver, Rigado, Oregon, USA) which wirelessly transmits data acquired to a remote personal computer for offline analysis. The device is placed on the dorsum of the subject’s foot within a Velcro strap to ensure no movements occur between foot and sensor. The stability of the device is additionally guaranteed by tongue that locks into the strings of the shoe itself. • SensHand The SensHand device consists of four modules, each of them equipped with an iNEMO STMicroelectronics board, with inertial module LSM9DS1 which is an integrated system that presents a 3D digital sensor of linear acceleration, a sensor 3D digital angular speed and a 3D digital magnetic sensor. The LSM9DS1 has a full scale for linear acceleration of ±2g / ±4g / ±8g, a full scale for the magnetic field of ±4 / ±8 / ±12 / ±16 gauss and a full scale of ±245 / ±500 / ±2000 degrees per second for the angular velocity. The LSM9DS1 includes an I2C serial bus interface that supports standard and fast modes (100 kHz and 400 kHz) and a standard SPI serial interface. The scales were set respectively equal to 2000 degrees/sec for the gyroscope, 8 g for the accelerometer and 8 Gauss for the magnetometer. The module placed on the wrist is the coordinator of the system and is equipped with a Bluetooth card (BMD-350, 2.4GHz transceiver, Rigado, Oregon, USA) for the wireless transmission of data to a remote computer. The other modules are placed on the distal phalanges of thumb, index and middle fingers. Modules coordination and data synchronisation are implemented and guaranteed by the Controller Area Network (CAN-bus) standard. • The OLIMPIA Wearable System The idea in OLIMPIA is to refine the existing system above described to address the main issues regarding their wearability and usability. In particular possible solutions for making the wearable sensors completely wireless will be investigated, avoiding the restriction of movements due to the spiral cables that currently enable the coordination and synchronization between the different units. Another criticism is related to the silicon finger stalls that can result heavy during the exercise motor execution, thus alternative solutions to fix the sensors to the finger tips will be tried. The acquired data will be made available to the OLIMPIA Cloud by means of the development of dedicated application programming interfaces (API). 6.2 The Cloud Infrastructure OLIMPIA forecasts the diffusion of services by means of web applications managing by a Cloud platform structured in three fundamental layers (Figure 3) dedicated to the development of macro- functions tasks regarding data storage, analysis and monitoring. Additionally OLIMPIA cloud services aims to be compliant with the Rete Telematica Regionale Toscana (RTRT) which connect AUSLs, AOU s and public administrations. Furthermore the proposed cloud services will be realized to be integrated and fully interoperable with the cloud Tuscany Internet exchange (TIX) in |
order to be respondent to the requests of public entities (i.e. AUSL, AOU). During the project the following layer will be defined, designed and developed: • Data-Layer: it will allow the implementation of a database for data collection and persistence through the definition of the storage, access and management modalities considering open source approach such as MySQL or JDBC. The database will be designed to be compliant with OLIMPIA Data Management Plan (OO6.1). The Data-Layer will allow the PD and clinical staff to access the results of the biomechanical tests performed allowing the evaluation of the therapeutic treatment and the tracking of disease course. Within the project TIX platforms will be studied to find the best solutions. • Core-Layer: it will allow the data treatment through the formulation of reasoning algorithms able to aggregate data giving back a quantitative and qualitative measure of the disease. The Core-Layer will exploit a server structure, i.e. a software platform such as Apache Tomcat Server able to execute web applications. • Presentation-Layer: it will define the end-users' data visualization modality by means of a server-side approach such as Java Server Pages and a web server implementation. The interfaces should be designed and developed during the project and should be able to manage: o The acquisition of motor parameters of the patients. o The data storage and data retrieve from OLIMPIA database. o The creation of different user profiles according to the care process (i.e. GP, clinicians, specialist ad family). These different user profiles guarantee different privileges in the use of the interfaces and in the access to the data, o The different visualization of the acquired and the stored data by the GP, clinicians, specialist and families which can monitor the motor parameter over time. The communication with the devices or software external to the OLIMPIA Cloud platform will be possible by means of secure connection systems using transmission protocols for application layers such as HTTPS. The implementation of the platform will exploit the Software as a Service (SaaS) Cloud Computing paradigm. Figure 3. The three layers of OLIMPIA Cloud platform |
7. State of the Art and Preliminary Data PD is a disabling pathology that affects millions of people worldwide. Since the disease heavily influences the QoL of patients, raising the burden of care on their relatives and the costs for health and care for the society, an optimal solution for the management and treatment of PD is needed. These people need long term treatments, therapy adjustments, and monitoring, but often, clinical examinations in hospitals are not sufficient for optimal management of the pathology due to long waiting lists, high traveling distance, working hours lost, etc. The possibility to monitor the patients at home enables the evaluation of many aspects that are not always evident or are infeasible to assess during neurological examinations in clinic, including motor fluctuations and dyskinesias [33–39], freezing events [40, 41], response to therapy adjustments before and after medical intakes [37, 42, 43], and eventually correlated pathologies (e.g., cardiac activities) [44]. Furthermore, the use of a monitoring system in the home environment could eliminate the ‘‘white coat effect,’’ which is responsible for better performance in the hospital rather than during daily living activities [44, 45]. Recently, advances in wearable sensors, information, and communication technologies, as well as data mining, are promoting the design and the implementation of e-Health systems that allow to provide novel therapeutic and monitoring solutions for PD patients [46]. These systems aim at maximizing the efficiency of healthcare, enhancing its quality without increasing costs. This is accomplished through augmented contacts between patients and clinicians and sharing information between the different stakeholders. These systems also promote the empowerment of patients to actively manage their health and to adopt healthy behaviours.[47]. In the same vein, internet of things (IoT) systems for healthcare are emerging [48]. They can allow the collection of huge amounts of patients’ data through wearable sensors that are connected to a medical database through mobile devices. These data are analysed by intelligent algorithms to obtain useful information for discriminating relevant health conditions, adjusting therapy, monitoring disease progression, and supporting both clinicians and patients in decision-making. 7.1 The State of the Art of Wearable Systems for PD In recent years, different research groups have worked to find a method to objectively measure the motor performance of the patients, since the motor symptoms are those that generally lead the neurologist to the diagnosis. The idea is to quantify the motor skills of the patients, finding a way to measure the items proposed in MDS-UPDRS III. A method to objectify the motion can lead to a quantitative diagnosis of the PD, overtaking the problems linked to the subjectivity and to the inter- rater and intra-rater variability, thus increasing the accuracy of the diagnosis. A study revealed that the overall classification rate is not only limited by technical accuracy but also by clinical accuracy [49]. In this direction, different works proposed well-defined experimental protocol to replicate the MDS-UPDRS III items, looking for a close correlation between the parameters measured with the technological solution adopted and the clinical scores assigned by the neurologist. System cost and portability are two important characteristics that must be considered when developing a novel diagnostic tool. To measure the motor performance of the patients, the most appropriate way seems the use of wearable devices based on inertial sensors, which can acquire data with a high sample rate (e.g., 100Hz, [50–52]), and examination of the results on board or transmittal of results to a control station for offline data processing. Thanks to the recent advances in MEMS technology, this type of device is portable, light weight, unobtrusive, easy to use, inexpensive, and accurate in the measurements. Thus, wearable devices with inertial sensors can represent an optimal solution in healthcare applications, for use in both clinical |
infrastructure and the home environment. Similarly, traditional motion capture tools such as multi- camera retro-reflective motion analysis systems, while potentially effective at finely measuring the motor performance of the subjects, are nonetheless difficult to bring out of a laboratory setting because of their cost and non-portability. Although features extracted from 3-D motion data are slightly more accurate than features extracted from inertial sensor data, inertial sensors are non- invasive and can be used continuously and in uncontrolled environments [53]. A significant limitation of most studies reported in literature is the small dataset adopted to test the technological solutions proposed. The small sample size reduces the generalizability of the results, which should be verified in a larger sample [54, 55] and, specifically, longitudinal and large sized validation is needed to prove clinical applicability of the developed technologies [49]. Another open issue concerns the optimal number of sensors to use for recording patient activities. The literature has a lack of consensus regarding the optimal number of sensors, and the optimal site for their placement, for the assessment of PD motor symptoms [38]. The sensors should guarantee that the subjects can perform the movements without restrictions. If a reduction in the number of sensors may lead to loss of potentially relevant information [56], to avoid a high invasiveness of the system, a restricted number of sensors must be used [57], because using up to three on-body sensors together with a phone decreases the acceptance of the system [58]. Thus, a trade-off must be found and adopted. Moreover, the features to extract from the inertial signals and to provide to the clinical staff are a matter of debate. Generally, they are selected by using statistical techniques such as ANOVA or the Xxxx–Whitney test, which can identify the features that best discriminate between different subjects’ groups (i.e., patients and healthy controls). Even if this is mathematically correct, the features proposed are not always easy to understand by the clinicians. It is important, instead, to provide the clinical staff with a reduced and comprehensive set of features to avoid misleading in clinical practice [59]. Finally, to provide automatic assessment of the performances measured, recent works implemented different machine learning approaches [60], such as Support Vector Machine [61], Naïve Xxxxx, k-NN, Random Forest [62], Decision Tree, and Linear Discriminant Analyisis [63, 64]. All of these classifiers were used to discriminate between PD patients and patients with similar symptoms but different pathologies (e.g., subjects with ET) [65] or to distinguish between PD and HC, or to identify different stages of the disease, or to evaluate the medication and DBS effects [66]. In various works, the Principal Component Analysis (PCA) is implemented as well, generally for the dataset reduction and feature selection [59, 67], or for visual inspection analysis, often in combination with the discussed machine learning techniques [49, 67–70]. Alternatively, a recent work proposed the use of deep learning as a promising method to analyse wearable sensor data in place of machine learning approaches [71]. Advantages of deep learning are: i) there is no need to rely on expert defined features that may or may not represent the information content of the signal that is subjected to classification; ii) the analysis procedure resembles what human experts do, since the whole signal segment is rated with one continuous and clinical scale-like output; iii) an adaptation of the network to an individual patient is possible; iv) deep learning based frameworks, in particular, can be expected to produce better results with the growing amount of data that will become available. However, to manage these data, high quality labels are required from clinical experts. Deep learning may provide higher accuracy than machine learning (e.g., AdaBoost.M1, RF, kNN, SVM) for correctly classifying symptoms and subjects, but further and extended studies are required to validate this theory. In conclusion, the rising idea from literature is to use unobtrusive and accurate systems that could monitor the disease progression since its beginning, throughout its development. Appropriate |
technological solutions, in fact, could improve the management of the PD, enhancing the patients QoL through an early and objective diagnosis of the pathology, as well as monitoring the effects of the pharmacological therapy during the disease progression. 7.2 The State of the Art of Telemedicine services for PD Acceptability [72–74], usability [75–77], and wearability [78] are all considerations that require particular attention to have an efficacious system that will actually be used by patients, without affecting their daily activities, both physically, avoiding impairments due to obtrusive heavy devices, and socially, avoiding devices that could be embarrassing and invasive for the users when they are in the community. For these reasons, the use of smartphones [44, 79], or jewellery-like wearable sensors [80, 81], which are common technological tools, seems to be the best solution to have a portable inexpensive instrument, which is socially accepted and easy to use. Particularly, using internal sensors and algorithms of a smartphone prevents the need for additional hardware, almost for some kind of assessment. Although usability and acceptability from users’ perspective are important issues for an operative and effective telemedicine system, just a few works in literature reported quantitative results about them using Likert scales. Another matter of debate is the optimal number of sensors to use, because reducing them may lead to loss of potentially relevant information, especially for PD patients, who show a large variability in movements. However, in accordance with literature, generally less than four sensor devices were used [82]. Anyway, energy harvesting approaches should be investigated because devices with long battery life are mandatory for long-term monitoring in unconstrained environments. [83, 84]. Moreover, to guarantee an optimal management during the home monitoring, it is mandatory to develop and provide user friendly interfaces that allow the clinicians and the patients to stay in contact [85], adopting a sort of telemedicine service that permits exchanging of information, consulting service, and therapy adjustments. The use of a tele-system for automated assessment of PD symptoms could support the neurologist in remote differential diagnosis, as well as through decision-making support systems [86, 87]. While the test results are automatically uploaded into patient medical records, the system could provide instantaneous feedback to the users [75, 79, 88], allowing the patients to obtain immediate results about their current condition without the direct involvement of any clinics [89]. Since telehealth systems acquire and manage a wide amount of data, machine learning techniques are needed for their processing, analysis, and aggregation; thus, the results could be appropriately showed to patients and/or clinical staff, through smart user interfaces [90, 91]. Technically, the management of a large amount of data requires attention to data loss and correct transmission of data [33, 83, 92]. Ethically, since sensitive data are acquired and processed, adequate measurements for data protection should be applied, including restricted and authenticated access to data [89, 90, 93], secure encrypted data transmission (e.g., SSL, SSH, VPN, and TLS protocols) [60, 74, 83, 87, 92], and anonymized personal data [94]. As limitation, most of the articles in literature involve a limited number of subjects in the experimental sessions and have a lack of randomization, potential recall bias, and likely selection bias. Thus, the clinical validation of the proposed systems cannot be addressed [84], and further investigations are required. In addition, sociocultural factors are usually not investigated in these works; therefore, there is a lack of information concerning the influence of relatives on telemedicine services and how gender, education, and working condition could affect their design and provision. Finally, the development of telehealth systems is a step beyond the simple use of wearable |
sensors at home, because it means actively including patients and caregivers in the healthcare path [33], promoting their empowerment in the management of their health status and disease progression through a conscious involvement [86]. The concept, indeed, is to transform the patients from end users to the main actors of the healthcare process, favouring the participation and cooperation of patients, caregivers, and clinical staff [33] to provide the best care available for each patient according to the Initiative Medicine approach. Appropriate training sessions would be organized to enable people to correctly use the system. The possibility to have a more personalized therapy [39] seems also to increase the feeling of assurance of the patients regarding the appropriate healthcare path to follow [87]. Generally, the adoption of telemedicine should be accompanied with the transformation of healthcare sector and overcome specific barriers. In terms of organization of the healthcare sector, reimbursement profiles should be defined considering which patients may benefit most and understanding the optimal frequency of telemedicine visits as replacement for in-person encounters [95]. Furthermore, a uniform regulation is missing in the domain of medical liability, at National and International level, thus hampering the development of telemedicine market in health services [96]. Telemedicine requires ubiquitous, adequate affordable broadband to support health information exchange to increase access to quality care for all individuals at the right place and the right time when it is needed [95]. Although they raised limitations, the use of telemedicine systems has the potentiality to enhance the PD management and treatment, supporting clinicians in remote monitoring and promoting the active engagement of the patients and their caregivers in the healthcare path. This aims to improve both patients’ QoL and clinicians’ quality of care toward an optimal personalized therapy. 7.3 Beyond the Current Limitations: the Innovations in OLIMPIA The discussed limitations from the SoA analysis can be summarized as in Figure 4, where are reported also the opportunities that arise from current barriers and can be addressed in this Proposal. |
Figure 4. Current limitations found in the SoA analysis and consequent opportunities for XXXXXXX proposal. Starting from the promising results obtained in DAPHNE, OLIMPIA want to address the main remained issues in order to offer a reliable system that can be integrate, applied and used in the day-to-day clinical practice. For this purpose, OLIMPIA deal with the following key-points: • To enlarge the dataset of analysed subjects involving: PD patients, healthy subjects of control (HC), subjects with idiopathic hyposmia (IH), De-Novo PD patients as detailed in the methodology paragraph (see par 8). The recruitment of these different classes of people is fundamental to ensure: ▪ Definition of normative data for motor assessment, on the basis of the healthy group performance, to quantitatively identified the motor impairment measured in PD patients and IH subjects, objecting the clinical diagnosis. ▪ Research and identification of prodromal symptoms of the disease to early detect the pathology in IH subjects. ▪ Analysis of levodopa therapy response in De-Novo PD patients subjected to the challenge dopaminergic test in the initial phase of the disease. ▪ Implementation and assessment of an home-monitoring service for PD patients to improve the quality of care. • To study and apply novel Artificial Intelligence (AI) algorithms, proposing the use of |
supervised classifiers, unsupervised techniques and deep learning approaches. The implementation of these techniques can allow to improve the extraction of features of interest for identifying the PD motor signs and to automate their processing and results visualization. Novel algorithms can be developed to measure specific impairments that are characteristic of the pathology, such as the freezing of gait and the hesitations. Machine learning methods can support the analysis of Big Data (including clinical data, olfactory test, biomechanical assessment, etc.) resulting an efficacious tool for data management. • To refine the technology to design minimally-invasive ease-to-use devices that can allow the unobtrusive collection of motor data from the patients both in hospital and at home. The wearable system has to be improved in terms of wearability, and the user-friendly interfaces should be developed to guarantee the easy use of the system from the different stakeholders, at hospital as well as at home, favouring the self-management of the patients and the cooperation between the healthcare professionals to ensure the continuity of care. • To integrate the technological system proposed into a service model that place the patient at its centre involving all the stakeholders of the healthcare system, from the GP to the specialists to the local services and formal and informal caregivers, to guarantee a continuity of care. The proposed service has to be widespread and accessible to the patients and this can be reached thanks to the use of the Day Service. The OLIMPIA service model will include also a cloud infrastructure for the storage and elaboration of data. The use of the cloud infrastructure will allow also to enhance the integration of the different stakeholders, since all the involved persons can have access to the data of the patient (with the proper limitations linked to privacy and security). The implementation of the service model based the Day Service and of this Software as a Service (SaaS) solution fully interoperable with the Tuscany Internet Exchange (TIX) datacentre will help the deployment within the Tuscany healthcare services. This strategy will allow to provide accessible and available data from the research, ensuring the extensiveness of the service model, favouring its exploitation at National and International level, thus promoting the standardization of the care path for PD. 7.4 The Basis of OLIMPIA Proposal The proposal OLIMPIA is the natural follow-up of the DAPHNE project, previously funded by Tuscany Region within the Bando FAS Salute 2014, about the objective diagnosis of Parkinson’s disease (April 2016 – October 2018). The lessons learnt from the previous experience with XXXXXX represent an added value for this proposal from both technical and clinical points of view. Therefore, in this proposal we can refine the critical issues of the previous project and go beyond them aiming at implementing a novel concrete care model service for PD management. Moreover, this proposal would represent the consolidation of a previous established collaboration between the Scuola Superiore Sant’Xxxx (SSSA) and a network of Operative Units of Neurology in Tuscany. In particular, the cooperation between SSSA and ASL1 of Massa and Carrara (recently become part of the AUSL Toscana Nord-Ovest) began in 2011 with a project about the development of an inertial system for the biomechanical analysis of PD patients motor performance. Since then, a Joint Lab has been established by SSSA and O.U. Neurology of Massa at the Hospital of Massa in 2011, different prototypes of the system were developed by SSSA (Figure 2), and hundreds among PD patients, healthy subjects of controls, subjects with idiopathic hyposmia, |
and De-Novo patients were analysed using the wearable system for the analysis of lower and upper limbs motion. These studies obtained the approval of the Local Ethical Committee (n°1148/ 12.10.10) and their results were published on peer-reviewed journals or international conferences (see the next paragraph for the complete lists of publications). The ASL1 of Massa and Carrara was also responsible for the IPMP-MS project that consisted of the screening for the idiopathic hyposmia in a sample of the local population ranging between 50- 69 years old to study the correlation between olfactory impairment and development of Parkinson’s Disease. The IPMP-MS project (2013-2016) consisted of two phases: i) an olfactory screening on a population sample (1500 subjects) that included clinical, neuro-radiological (brain NMR and NMR- DTI), and neurophysiological exams on those subjects identified during the screening as “hyposmics”; ii) a 5-years follow-up on the selected sample. XXXXXX and the current proposal take into account the IH subjects recruited by IPMP-MS study and analyse their motor performance to investigate the initial worsening of motion capabilities, monitoring a subjects over different years. • List of publications of the subjects involved in OLIMPIA related to the research project The consolidated collaboration between different partners of XXXXXXX proposal allowed in the recent years the publication of several works about the use of wearable sensors for objecting the diagnosis of PD. The list is reported as follows: • X. Xxxxxx, X. Xxxxxxxxx, X. Xxxxxxxxx, X. Xxxxxxxx & F. Cavallo. (2018). “Comparative motor pre-clinical assessment in Parkinson's disease using supervised machine learning approaches”. Annals of Biomedical Engineering xxxxx://xxx.xxx/00.0000/x00000-000-0000-0 In this work, a wearable inertial device, named SensFoot V2, was used to acquire motor data from 30 healthy subjects, 30 people with IH, and 30 PD patients while performing tasks from the MDS-UPDRS III for lower limb assessment. The most significant and non-correlated extracted parameters were selected in a feature array that can identify differences between the three groups of people. A comparative classification analysis was performed by applying three supervised machine learning algorithms. The system resulted able to distinguish between healthy and patients (specificity and recall equal to 0.967), and the people with IH can be identified as a separate class within a three-group classification (accuracy equal to 0.78). • X. Xxxxxx, X. Xxxxxxxxx, & F. Cavallo. (2018). “Automated systems based on wearable sensors for the management of Parkinson's disease at home: a systematic review”. Telemedicine Journal and e-Health. xxxxx://xxx.xxx/00.0000/xxx.0000.0000. This review addresses automated systems based on wearable/portable devices for the remote treatment and management of Parkinson’s disease. The idea is to obtain an overview of the telehealth and automated systems currently developed to address the impairments due to the pathology to allow clinicians to improve the quality of care for Parkinson’s disease with benefits for patients in QoL. • X. Xxxxxx, X. Xxxxxxxxx, & F. Cavallo. (2017). “How wearable sensors can support Parkinson's Disease diagnosis and treatment: a systematic review”. Frontiers in Neuroscience, 11:555. xxxxx://xxx.xxx/00.0000/xxxxx.0000.00000. This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON–OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at |
each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring). • C. Xxxxxxxxx, X. Xxxxxxx, X. Xxxxxxx, X. Xxxxx, X. Xxxxxxxxx, X. Xxxxxx, X. Xxxxxxxx, X. Xxxxxxx, X. Xxxxx, X. Xxxxx, X. Xxxxxxx, & X. Xxxx. (2018). “Combining olfactory test and motion analysis sensors in Parkinson’s disease preclinical diagnosis: A pilot study”. Acta Neurologica Scandinavica. 137:204–211. xxxxx://xxx.xxx/00.0000/xxx.00000. In this pilot study, we investigated a step-by-step method to achieve preclinical PD diagnosis combining a validated olfactory test (i.e., the IOIT) and the biomechanical assessment using the SensHand V1 and SensFoot V2 devices. 20 subjects with idiopathic hyposmia were identified and tested with the sensors at baseline and 1 year follow up. One subject showed significant worsening in motor measurements after 1 year. In this subject, a dopaminergic challenge test monitored with the same sensors was conducted and, finally, he underwent [123I]-FP/CIT (DaTscan) SPECT brain imaging. The results showed that he is probably affected by preclinical PD. This pilot study suggests that the combined use of an olfactory test andmotor sensors for motion analysis could be useful for a screening of healthy subjects to identify those at a high risk of developing PD. • A.H. Butt, X. Xxxxxx, X. Xxxxxxxx, X. Xxxxx, C. Xxxxxxxxx, & F. Cavallo (2017). “Biomechanical parameter assessment for classification of Parkinson disease on clinical scale”. International Journal of Distributed Sensor Networks, vol. 13(5). xxxxx://xxx.xxx/00.0000/0000000000000000. In this study machine learning–based computerized assessment methods were introduced and investigated to classify the motor performance of patients with PD. Patients were divided in two groups: slight–mild patients and moderate–severe patients according to the average rating assigned by the neurologists during tasks performance. Selected features were trained in SVM, logistic regression, and neural network to classify the two groups of patients. The highest classification accuracy was obtained by neural network classifier (83.1% classification accuracy and 0.889 area under the curve). • X. Xxxxxx, X. Xxxxxxxxxx, X. Xxxxxxxx, C. Xxxxxxxxx, & F. Cavallo. (2017). “XXXXXX: a novel e-Health system for the diagnosis and the treatment of Parkinson’s Disease”. In Proceedings 8° Forum Italiano per l'Ambient Assisted Living (ForItAAL), June 14-15, 2017, Genova, Italy. In this paper DAPHNE system is proposed, aimed to implement innovative and sustainable services for the early diagnosis, for the therapy and for the management of PD by using wearable devices, information and communication technologies (ICTs), such as mobile Health (mHealth) apps and Internet of things (IoT) protocols. • X. Xxxxxxx, X. Xxxxxxxxxx, X. Xxxxxx, X. Xxxxxxxx, X. Xxxxxx, X. Xxxxxxxx, X. Xxxxxxx, C. Xxxxxxxxx, X. Xxxxxxxx, & F. Cavallo (2017). “Preliminary studies for the evaluation of a novel wearable sensor for biomechanical analysis of upper limbs in Parkinson Disease”. In Gait & Posture, vol. 57, supp. 3, p. 38. xxxx://xx.xxx.xxx/00.0000/x.xxxxxxxx.0000.00.000. This pilot study focuses on the upper limbs in healthy subjects and presents preliminary values measured with SensHand V1 against those measured with an optical motion analysis system (BTS SMART-DX), assumed as the gold standard. Results show a low error rate in each task with better accordance in number of repetitions. A good correlation coefficient (>0.9) was obtained both for frequency and number of repetitions in each task. • X. Xxxxxx, X. Xxxxxxxx, C. Xxxxxxxxx, X. Xxxxxxxxxx, & F. Cavallo. (2016). “Empowering |
Patients in Self-Management of Parkinson's Disease through Cooperative ICT Systems”. In: Y. Xxxxx, X. Xxxxxx, & X. Xxxxxxx (Eds.) Optimizing Assistive Technologies for Aging Populations (pp. 251-277). Hershey, PA: Medical Information Science Reference. xxxxx://xxx.xxx/00.0000/000-0-0000-0000-0.xx000. Re-edited in: Wearable Technologies: Concepts, Methodologies, Tools, and Applications (pp. 637-663). IGI Global. The objective of this chapter is to demonstrate the technical feasibility and medical effectiveness of personalized services and care programs for Parkinson’s disease, based on the combination of mHealth applications, cooperative ICTs, cloud technologies and wearable integrated devices, which empower patients to manage their health and disease in cooperation with their formal and informal caregivers, and with professional medical staff across different care settings, such as hospital and home. • X. Xxxxxx, X. Xxxxxxxx, C. Xxxxxxxxx, X. Xxxxxxxxxx, & F. Cavallo. (2014). “Using wearable sensor systems for objective assessment of Parkinson's disease”. In Proceedings of the 20th IMEKO TC4 Int. Symp. and 18th Int. Workshop on ADC Modelling and Testing Research on Electric and Electronic Measurement for the Economic Upturn, Xxxx 00-00, Xxxxxxxxx, Xxxxx. This paper presents a novel wearable sensor system based on the integration of miniaturised IMUs for fine hand movement analysis. The system, named SensHand V1, is used for the objective diagnosis of PD, which is commonly assessed by neurologists through visual examination of motor tasks and semi-quantitative rating scales. Here, these motor tasks are also assessed using the SensHand V1, and then compared with the subjective metrics. Results demonstrate that the system is adequate to support neurologists in diagnostic procedures and allows for an objective evaluation of the disease. • F. Xxxxxxx, X. Xxxxxxxx, X. Xxxxxx, X. Xxxxxxxx, M.C. Xxxxxxxx, X. Xxxxx, X. Xxxxxxxxx, & X. Xxxxxxxxxx. (2013). “Preliminary evaluation of SensHand V1 in assessing motor skills performance in Parkinson Disease”. In Proceedings IEEE 13th International Conference on Rehabilitation Robotics (ICORR), Xxxx 00-00, 0000, Xxxxxxx, XX, XXX. xxxxx://xxx.xxx/00.0000/XXXXX.0000.0000000 This paper presents a system for hand motion analysis based on 9-axis complete inertial modules and dedicated microcontroller which are fixed on fingers and forearm. The technological solution presented is able to track the patients’ hand motions in real-time and then to send data through wireless communication reducing the clutter and the disadvantages of a glove equipped with sensors through a different technological structure. The device proposed has been tested in the study of PD. • C. Xxxxxxxxx, X. Xxxxxxxxxx, X. Xxxxxxx, X. Xxxxxxxx, X. Xxxxxx, X. Xxxxxxxx, M.C. Xxxxxxxx, & X. Xxxxx. (2013). “Preliminary evaluation of Sensorfoot V1 and Senshand V1 in assessing motor skills performance of Parkinson's disease patients”. Journal of the Neurological Sciences, 333: e67. This work aims to evaluate the tasks of the MDS-UPDRS through biomechanical exercises objectively measured by means of a wireless and wearable device for motion recognition, composed by SensorFoot V1 (placed on the foot) and SensHand V1 (fixed on fingers and forearm). Generally, healthy people showed significantly (p < 0.05) more constant repetitive movements and better performances in comparison to patients; good correlations (Xxxxxxx'x index > 0.7) with PD patients' clinical scores were found. • C. Xxxxxxxxx, X. Xxxxx, X. Xxxxxxxx, B. Xxxxxxx, X. Xxxxxxx, X. Xxxxx, X. Xxxxxxxxxx, X. Xxxxx, X. Xxxxx, X. Xxxxxxxxx, X. Xxxxxxx, X. Xxxxxxxx, X. Xxxxxx, X. Xxxxxxx, X. Xxxxxxx, X. Xxxxx, P. Xxxxxxxxx, X. Xxxx. (2012) The validity and reliability of the Italian Olfactory |
Identification Test (IOIT) in healthy subjects and in Parkinson's disease patients. Parkinsonism Relat Disord. 2012 Apr 15.
This paper aims to investigate a new tool for detecting olfactory deficit in Italian subjects we developed a multiple-choice identification test prepared with odorants belonging to the Italian culture. The Italian Olfactory Identification Test (IOIT) was developed with 33 microencapsulated odorants with intensity of odors and headspace Gas Chromatography being tested. Test-retest reliability of the IOIT was evaluated. The IOIT was administered to 511 controls and 133 Parkinson's patients. In healthy subjects the number of IOIT errors increased with age for both females (p < 0.0001) and males (p < 0.0001), while in the Parkinson's disease group the number of IOIT errors was significantly greater where compared to healthy subjects (p < 0.0001 in all age groups). The reference limits applied to all age groups revealed an IOIT sensitivity of 93% and a specificity of 99%. The test-retest reliability was excellent. The IOIT is highly reliable, disposable, easy to administer, not fragile, and has a long shelf-life. All these features make the IOIT suitable for clinical use as well as for population screening and to discriminate Parkinson's patients from healthy subjects.
8. General goal of the Project and related strategy / experimental design:
The General Objective of the OLIMPIA Proposal is to identify and demonstrate the clinical and technological relevance, cost effectiveness and acceptability by stakeholders of a novel patient- centred healthcare model, based on the integrated use of wearable sensors, users’ interfaces, intelligence algorithms and cloud platforms, for detection, monitoring and management of Parkinson's disease (PD). This novel model aims to apply a proper management of PD patients defining new assistance paths through the use of Day Service, which can help to access diagnostic and therapeutic path and to promote the continuity of care.
The actions planned in OLIMPIA to address the call outcomes are described in Table 3.
Table 3. OLIMPIA Strategy.
From regional call How OLIMPIA addresses the challenge
Support the qualitative growth of assistance care process and enable processes of resources optimization guaranteeing safety and appropriateness in the delivery of health and pharmaceutical assistance.
increase the ability to protect the intellectual property generated by the research system through the enhancement of the technology transfer
OLIMPIA aims to provide a management of PD, taking charge of the patients from the preclinical phase to the most advanced stages. The idea is to support each patient with appropriate and personalised PDTA that allow the best care possible. Adequate PDTA, through the use of the Day Service will improve the care path, avoiding useless or double exams that can sometimes can occur.
The take in charge by the Day Service will guarantee to the patient an easy access to an integrated multidisciplinary team that will follow him/her during the disease.
XXXXXXX promotes IPRs protection of the results coming from the project. The OLIMPIA exploitation activities will include the identification of opportunities for services and technology transfer in Regional (and eventually National and International) Healthcare Service and society. All partners will be involved and will be allowed to cooperate with partners, companies and organizations external to the project. OLIMPIA researchers will study how the service and business models will be imported in the real contexts, considering the obtainable advantages, the needed changes both
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in term of costs, social services and employees, and comparing these information with current services. | |||
produce usable and reproducible research results in order to facilitate their transfer within the NHS, in order to make available to citizens access to innovations in sustainable cost schemes | OLIMPIA aims to realize an innovative care model service for PD, enabling the cooperation between different stakeholders. One of the main objective of the OLIMPIA proposal is to build a Software as a Service (SaaS) solution fully interoperable with the Tuscany Internet Exchange (TIX) datacenter, thus enhancing the connectivity and the accessibility to the information by all the stakeholders, improving the management process, and making easier for the patients to access their own data, as promoted by the “Piano Sanitario e Sociale Integrato Regionale 2012-2015”. The proposed system is sustainable, as it will provide a reduction of costs for SSR, and in perspective for the National Healthcare System. | ||
increase the development of the subjects skills operating in the NHS | OLIMPIA, through the cooperation between local services, Day Service, and hospital aims at involving in the integrated PDTA a multidisciplinary team, which is appropriately trained for offering the best care to the PD patients. The patients (and the caregivers) are also engaged and empowered, to manage their disease autonomously at home. | ||
encourage scientific collaboration between Tuscan research institutions and the NHS to xxxxxx synergies and the sharing of technological infrastructures, such as organizational and research platforms; | In OLIMPIA the scientific collaboration between the clinical ORs of Regional Healthcare Service and the excellent scientific ORs (i.e. IBR-SSSA, IFC-CNR) is well consolidated and synergic. This can allow to improve the homogeneity of the care service model, through the use of an unique platform (i.e., RTRT) that enable the exchange of information and easy access to the data. The possibility to have a shared database will improve the monitoring of the patients over the time, as well as it will reduce the variability that currently affected the diagnosis. | ||
promote scientific collaborations with national and international research groups in order to facilitate the access of research products to national, European and international funding procedures; | In OLIMPIA external collaboration with ORs national and international is addressed. These collaborations enable the full exchange of knowledge between complementary ORs which have expertise in specific field of applications of the proposal (e.g., clinical experience in PD for UNIPA, cloud computing experience for TUKE, and artificial intelligence experience for IWT). Furthermore, the external collaborations can be useful for exploitation of the OLIMPIA model outside the Regional Healthcare Service. Moreover, these collaborations are preparatory for next partnerships to submit other Proposal at National and International levels. | ||
8.1 The Clinical Methodology The implementation of a such system requires, from a clinical point of view a well- defined methodology. In OLIMPIA project, the idea of the clinical methodology is already detailed since it would be the follow-up, improvement, and enlargement of the DAPHNE project experimentation (and its clinical protocol, approved by the Ethical Committee of the AUSL Toscana Nord-Ovest, named CASANOVA-PD Study). | |||
8.1.1 Clinical Study Objective ▪ To identify normative data of objective motor measures of those body movements that are usually investigated to establish the presence and severity of Parkinson's disease. ▪ To identify patients in the preclinical phase of the disease, through a four-step approach: i) identification of IH through a low-cost standard olfactory test ii) Measurement of an altered motor pattern in IH subject, compared to the normal data assessed in an HC group; iii) Improvement of motor performance due to dopaminergic stimuli with the pharmacological test with L-dopa; iv) Demonstration of an alteration of the xxxxx-striatal dopaminergic system using the diagnostic nuclear medicine method SPECT DaTSCAN). ▪ To use OLIMPIA wearable sensors for the objective analysis of the motor pattern in a clinical environment to assess PD patients, in addition to the usual clinical procedure. ▪ To use OLIMPIA wearable sensors for the objective analysis of the motor pattern at home through the telemedicine service. The instrumentation will also be adapted for use by the patient and / or caregiver. The data of the objective motor pattern will be uploaded on a Cloud database where they will be analysed and the neurologist will verify the correspondence between these data and what is detected by the usual clinical practice. 8.1.2 Clinical Study Endpoints Primary Endpoints ▪ To acquire motor pattern through the use of OLIMPIA wearable sensors for motion analysis in HC, IH subjects, PD patients, and patients with extrapiramidal syndrome “De- Novo”. ▪ To obtain normative data of the motor parameters that are clinically relevant. The motion features that are statistically significant in distinguishing between HC and PD patients will be identified and their repeatability will be evaluated. ▪ To correlate normative data with measures obtained by IH subjects. The demonstration of a subtle deficit in motor performance that is not clinically evident could anticipate the disease cardinal symptoms by some years. The preclinical diagnosis will be investigated through the biomechanical analysis provided by the OLIMPIA wearable sensors in IH subjects, which are at risk for developing PD. ▪ To correlate the clinical total data with parameters obtained by the nuclear medicine diagnostic (i.e., SPECT DaTSCAN). Secondary Endpoints ▪ Clinical control (in hospital) through objective motor measures. ▪ Clinical control (at home) via telemedicine with wearable wireless instrumentation. Correlate the motor measures measured at the hospital with those detected by patients at home. 8.1.3 Recruitment ▪ 150 healthy subjects (HC), [75 female, 75 male], as control group, enrolled by the GPs, as normosmic at the standard olfactory test IOIT. ▪ 100 IH subjects, recruited by the GPs through the administration of the standard olfactory test IOIT. |
▪ 50 PD patients [25 F, 25 M], of which 15 PD patients will be assessed with the wearable system also at home during the final months of the experimentation.
▪ 30 De-Novo PD patients drug-naïve, who will be subjected to the sub-acute challenge dopaminergic test with L-dopa.
8.1.4 Sample size
According to the 2010 guidelines of the Clinical and Laboratory Standard Institute (CLSI) [97] the minimum sample size required to estimate 95% reference limits (2.5th and 97.5th percentile) and 90% (5th and 95th percentile) is 120 subjects. This dimension makes it possible to calculate the specificity or sensitivity of 97.5% and 95%. By the way CLSI guidelines also consider situations in which sample sizes are not optimal. In many fields, in fact, these dimensions are not easily accessible and it is therefore necessary to use methods suitable for small samples. In this study, considering the need to maintain a sustainable sample size compared to the expected clinical examination costs, a sample of 100 healthy subjects will be used as an estimation group to estimate the reference limits, while a further sample of 50 healthy subjects will be used as validation group to validate the reference limits. A sample of 50 healthy subjects can however allow an estimate of the specificity with a sufficiently small 95% confidence interval. The following are the 95% confidence intervals for three specificity values:
Specificity | 95% confidence interval |
98% | 89.4-100 |
96% | 86.3-99.5 |
94% | 83.5-98.8 |
Similarly, a sample of 50 individuals with Parkinson's disease will be used to obtain a sufficiently accurate estimate of the sensitivity of the reference limits with a sufficiently small 95% confidence interval.
In order to evaluate the reproducibility of the measurements of the motor parameters, three repeated measurements at a distance of 15 minutes will be performed on 50 healthy subjects and 50 subjects with Parkinson's disease. For this purpose the intra-class correlation coefficient (ICC) will be used. A sample of 50 subjects will be able to estimate an ICC sufficiently accurately. The following are the 95% confidence intervals for 4 possible ICC values:
ICC | 95% confidence interval |
0.95 | 0.91-0.97 |
0.90 | 0.83-0.94 |
0.85 | 0.75-0.91 |
0.80 | 0.67-0.88 |
0.75 | 0.60-0.85 |
8.1.5 Statistical analysis
To verify the normality of the distribution of the motor parameters a Kolmogorov-Smirnov test will be performed, while the Xxxxxx test will allow to check the homogeneity of the variance. The presence of outliers will also be evaluated, any anomalous values will be excluded from the analysis.
Values that fall outside the following limits will be considered as anomalous values:
Upper limit = upper quartile + 1.5 * (interquartile range) Lower limit = lower quartile - 1.5 * (interquartile range) To verify the discriminating capacity of the motor parameters, an Analysis of Variance analysis (ANOVA) will be performed to compare the group of healthy subjects with those with Parkinson's disease. In case of failure of the normality test, the analysis will be performed through the Xxxx-Xxxxxxx U test. To take into account multiple comparisons, the p-value of statistical significance will be corrected following the Benjamini and Xxxxxxxx procedure. For the motor parameters resulting significantly discriminating the reference limits of 95% and 90% will be calculated, corresponding respectively to the 2.5th and 97.5th percentile and to the 5th and 95th percentile. The reference limits will be calculated using both the parametric method (mean ± 1.96 * SD for reference limits at 95% and mean ± 1.64 * SD for 90% reference limits) and no parametric (2.5 and 97.5 percentile for reference limits to 95% and 5 to 95 percentiles for the 90% reference limits). For the calculation of the reference limits, the bootstrap method with 1000 bootstrap samples will be used. To evaluate the predictive value of the reference limits, the specificity calculated on the validation sample consisting of healthy subjects will be used, the sensitivity calculated on subjects with Parkinson's disease and the area under the ROC curve (AUC) calculated using the validation sample of healthy subjects and the sample of subjects with Parkinson's disease. The 95% confidence interval of both specificity, sensitivity and AUC will also be reported. The specificity observed in the validation sample will also be compared with the theoretical one of 97.5% or 95% depending on the significance limits used for the determination of the reference limits (95% or 90%). The comparison will be made using the test z for the comparison of a sample proportion with a theoretical proportion. The ICC calculated using ANOVA with 2 mixed-effect classification criteria will be used to evaluate the reproducibility of motor parameters. The concordance force will be evaluated according to the Xxxxxx and Xxxx approach (0.00 = poor, 0.01-0.20 = slight, 0.21-0.40 = fair, 0.41-0.60 = moderate, 0.61-0.80 = substantial, 0.81-1.00 = almost perfect). To highlight the slight clinically undetectable motor deficit, in subjects with idiopathic hyposmia, as a group of subjects potentially at high risk of developing Parkinson's disease, on the basis of the reference limits of the motor parameters of clinical interest the hyposmic subjects will be assigned to 2 categories: subjects with no motor deficit and subjects with suspected motor deficit. Under the null hypothesis that the hyposmic subjects are free of motor deficit, it is expected that in subjects with hyposmia the percentage of subjects assigned as subjects with no motor deficit is equal to the theoretical one of 97.5% or 95%, depending on the limits of significance used for the determination of the reference limits (95% or 90%). The comparison between the percentage of subjects with absence of motor deficit observed in the group of hyposmic subjects with the theoretical percentage will be made using the test z for the comparison of a sample proportion with a theoretical proportion. The hyposmic subjects will then be followed over time to verify if among |
those with suspected motor deficit the onset of Parkinson's disease is greater than those without motor deficit. The correlation between the clinical data and the parameters detected by the DaTSCAN SPECT will be evaluated through parametric correlation coefficients (Xxxxxxx'x r) or no parametric (Xxxxxxxx'x Xxx) as appropriate. The total score of MDS-UPDRS part III will be used to evaluate changes in disease severity during the l-dopa test in individuals with extrapyramidal de novo syndrome and the values of motor parameters of clinical interest will be also considered. To compare the values at T0, T20 and T50 of the total score of the MDS-UPDRS part III and of the motor parameters of clinical interest, the ANOVA for repeated measurements or the no parametric Xxxxxxxx test will be used when appropriate. The positivity of the l-dopa test will also be examined using both the total MDS-UPDRS III score and the value of the different motor parameters of clinical interest. The l-dopa test will be considered positive when the score at the maximum dopaminergic stimulus time (T20) improves> 20% from baseline (T0) for the MDS- UPDRS III scale and when a change of 20% is observed in the single motor parameter considered. The percentage of subjects positive to the l-dopa test when using the total score of MDS-UPDRS III will be compared with the percentage of subjects positive to the test at l-dopa on the value of the single motor parameter by means of the McNemar test. Regarding the objective motor measures taken at the patient at the hospital and those detected by the patient at his home, reproducibility will be evaluated by ICC. 9. Evaluation and validation of services, metrics and protocols OLIMPIA aims to validate and evaluate the provided services, the clinical methodology and devices including a high level assessment phase focusing on the quantitative measurement of indicators to demonstrate the potential success of the developed services and their impact on PD patients healthcare programs. This evaluation will be carried out using mainly proven and standard assessment tools that in case will be adapted to the specific cases. It will be also taken into account the possibility to develop ad-hoc evaluation instruments if the Consortium will consider it worthwhile and necessary. The OLIMPIA system assessment can be evaluated in several areas: • Quality of life (QoL) of users: the aim is the evaluation of the influence of the OLIMPIA services on the QoL of PD patients and caregivers. QoL is a multidimensional concept that includes physical, cognitive and psychological factors, but also social relationship and environment, including subjective and objective elements. Individuals perceive and evaluate objectively similar condition of life differently and individual’s perception of QoL can vary between life domains. In OLIMPIA different parameters will have to be considered in order to assess the PD's QoL, for example asking them to answer specific questions about different fields of their life. Possible assessment tools can be: EuroQoL (EQ-5D), Parkinson’s Disease Questionaire (PDQ-39), Activities of daily living (ADL), Instrumental activities of daily living (IADL) Caregiver burden inventory (CBI), Scale for the Assessment of Positive Symptoms and WHO (Five) Well- Being Index (WHO-5). These are quantitative scales that could be also self-administered by means of the remote service. • Quality of services (QoS): this aspect is strongly related to the customer satisfaction and the perception of QoS. In general this is an issue investigated in different fields (information technology, call centres, healthcare services, etc.) adopting various approaches. OLIMPIA |
combines ICT-based services provided both in clinical and domestic settings so, during the project, the most appropriate assessment tools that could evaluate all these aspects will be analyzed and identified starting from the following possible ones: SERVQUAL, JCAHO, KQCAH, Patient Satisfaction Questionnaire III (PSQ-III), Quality from Patients’ Perspective (QPP), Emotional Stress Reaction Questionnaire (ESRQ), ISO/IEC 29341 and ISO/IEC TR 13243. • Usability / acceptability assessment of the ICTs: the aim is to evaluate how attractive the system is for end users and stakeholders in terms of usability and acceptance. The acceptability includes usability, ethical factors, social acceptance, perceived value of the system and stigmatization issues. The evaluation will be carried repeatedly out over the entire course of the project, beginning during the development of services and devices in co-creation with users and stakeholders. Existing evaluation means and theories will take into account to define tools, metrics and questionnaires suited for the evaluation of OLIMPIA system, such as the ISO 3241 on usability, the Technology acceptance model (TAM), the Quality of Life Scale (QoLS) and the Unified Theory of Acceptance and Use of Technology (UTAUT). In particular UTAUT is based on four constructs that determine user acceptance and user behaviour analysing performance expectancy (the extent to which an individual believes that a technology work performance improves), effort expectancy (the extent of effort that is associated with the use of technology), social influence (the extent to which an individual thinks important people find that he/she should use the technology) and facilitating conditions (the extent to which an individual thinks that the organizational and technical infrastructure support the use of the technology). • Medical treatment / assessment: the aim is to evaluate from a clinical point of view, how many benefits entails the use of the system on PD patients. The evaluation will take place during the field tests also using statistical data taken from literature and other research projects, in order to evaluate both cognitive and motor patients' status: o Neurological evaluation: different clinical scales for PD patients' assessment will be: MDS- UPDRS, Xxxxx and Yahr (HY), Schwab&England (S&E), Wearing-off Questionnaire-19, Freezing of Gait Questionnaire (FOGQ), Xxxxxxx Sleepiness Scale (ESS), Scala di Xxxxxx for constipation evaluation and Insomnia Severity Index. o Neuropsychological evaluation: Mini Mental State Examination (MMSE), Addenbrooke’s Cognitive Examination (ACE), Xxxxxxxx'x perceptual Maze Test. o Psychological evaluation: the diagnostic criteria and scales of psychological assessment for PD patients will be: Self-rating Depression Scale, Self-rating Anxiety Status Inventory , Apathy Evaluation Scale, Questionnaire for Impulsive-Compulsive Disorders in PD-Rating Scale. o Biomechanical evaluation: data acquired by wearable ICT-based devices will be analyse to identify subjects' motor pattern. Techniques of data processing will be implemented developing specific algorithms able to objectively measure biomechanical parameters that are indicative for subjects' motor capabilities assessment. These features will be identified through events detection processes both in spatiotemporal and frequency domain and they will be available to the clinicians in terms of values characterizing the movements (e.g. frequency, velocity, amplitude, variability) for a quantitative evaluation of the motion. Data Mining and Machine Learning techniques will be used in order to analyse the motor extracted features and to manage the wide amount of data acquired. Innovative AI algorithms for data aggregation and classification will be investigated and applied included Supervised classifiers, Unsupervised approach and Deep learning algorithms. Accurate |
statistical analysis tools as Analysis of Variance (ANOVA), Xxxxxx'x test, T-test, Univariate and Multivariate Linear Discriminant Analysis (LDA), Xxxxxxx'x correlation coefficient and Multiple Linear Regression. The aim of the process is to identify a reduced number of parameters highly significant to discriminate PD patients from healthy subjects and, for the patients, to assess with high sensitivity, specificity and accuracy the level of the pathology from the early to mild and advanced phases. • Clinical trial and methodology validation: the scientific and clinical validation of the methodology for PD diagnosis proposed will follow an approach based on the “Good Clinical Practise” (standard GCP), i.e. an international standard of ethics and quality necessary for planning, conducting, recording clinical trials which involves human subjects. In particular, the protection of human rights will be discussed providing assurance about the reliability of the data of the clinical trials. Guidelines will be drafted to specify how clinical trials will be conducted, defining roles and responsibilities. A quality management activity will be carried out during the whole experimentation assessing the respect and the compliance with the GCP standard. • Economic / financial assessment of services: the aim is to evaluate the sustainability of the OLIMPIA system in terms of cost-benefit. It is needed to include different stakeholder groups that could participate and benefit from the use, sale and operation of the system, e.g. insurances, doctors, care persons, family members, PD patients, active and healthy older persons. The evaluation will take place in several phases of the project by using MAFEIP tool with an improving degree of accuracy. Possible assessment tools are: Cost-benefit analysis, Conjoint analysis (to determine how people value different features of the system), Competitive analysis (to find out the unique selling proposition, specific target groups and marketing channels). 10. Exploitation Strategy The estimation of the health and economic outcomes and impacts of OLIMPIA project will be crucial to certify the effectiveness and sustainability of the proposed intervention. To do this, the MAFEIP tool (Monitoring and Assessment Framework for European Innovation Partnership on active and healthy ageing), one of the most innovative tools made available to companies and research institutions, will be used. MAFEIP is a web-based tool that aims to estimate the health and economic outcomes of a wide variety of ICT-enabled social and health innovations, including new care pathways, devices, surgical techniques and organizational models. Currently MAFEIP tool is also used as reference for defining the impact assessment of European projects. The probability to reach the supposed impact levels will be assessed taking into account all the aspects concerning the project, indeed the assessment will address health, economic and social benefits actually achievable through the innovations introduced by the project. Being able to simulate changes in the interventions with the aim of identifying the key factors that determine its effectiveness, MAFEIP analysis will also influence the decision-making process. In order to exploit the potential of the tool, a rigorous analysis, partly already presented in this document (see Section 4D), will be carried out on the current care situation and the new scenario introduced by the innovations of the project. After choosing a probabilistic model based on the characteristics of the faced pathology and the type of intervention, the inputs will be defined: characteristics of the selected population, current and expected costs, mortality and transition rates between different disease states and many other utilities scores. According to the selected model and inputs the MAFEIP tool will provide as outputs the factual impact of the project scenario and an analysis of benefits in economic and health terms. |
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Operational Objectives: (up to 12 operational objectives) Operational Objective 1 (OO1): Project Management and Dissemination - Activity 1.1: Coordination of activities - Activity 1.2: Administrative and financial management - Activity 1.3: Dissemination Operational Objective 2 (OO2): Guidelines, Requirements, and Design for Services - Activity 2.1: Stakeholders’ requirements analysis and guidelines - Activity 2.2: Clinical protocol consolidation - Activity 2.3: Service model investigation and definition - Activity 2.4: Experimental methodology consolidation Operational Objective 3 (OO3): Healthcare Infrastructure Development and System Integration - Activity 3.1: Definition of System Architecture - Activity 3.2: Wearable Sensors Refinement - Activity 3.3: Design and Implementation of the Cloud Platform - Activity 3.4: Web App interfaces in Cloud for patients and clinicians - Activity 3.5: Algorithms transferring into libraries and Cloud resources - Activity 3.6: System Integration and Lab Tests Operational Objective 4 (OO4): Artificial Intelligence for Parkinson’s Disease diagnosis and assessment - Activity 4.1: Machine Learning and Deep Learning for motor assessment. - Activity 4.2: Automated algorithms for clinical parameters extraction - Activity 4.3: Machine Learning development for PD early diagnosis and stage classification. Operational Objective 5 (OO5): Clinical Experimentation and Evaluation - Activity 5.1: Pilot sites management, recruitment, and training - Activity 5.2: Clinical Experimentation - Activity 5.3: Final Evaluation Operational Objective 6 (OO6): Exploitation and Impact Assessment - Activity 6.1: Data management plan - Activity 6.2: Innovation Management - Activity 6.3: Socio-Economic Impact Assessment - Activity 6.4: Exploitation Plan and IPR Management |
For each Operational Objective, provide the required information: Operational Objective no. (1) Name: Project Management and Dissemination Description of the operational objective: The main objective of this OO is to guarantee the smooth coordination of the research activities carried out by the Consortium, in accordance to the |
project work plan. This includes an accurate and effective management of the administrative and financial matters, and proper reporting to the Regione Toscana. Additionally, it aims to disseminate and transfer the results of scientific research and the technological innovations at national and international level. Expected Results: deliverables e milestones Explain the expected results during the operational objective, including whether specific deliverables and milestones are foreseen for the implementation of the project. X specific measurable and verifiable results will be produced during the course of the objective (deliverables) If yes, please indicate in which activity(ies): Activity 1.1 Activity 1.3 □ the objective includes check points (milestones) If yes, please indicate in which activity(ies): Timing: M1 – M36 Total cost of the objective: € 97.398,00 List of activities envisaged under the Operational Objective: Activity no.1.1 - Name: Coordination of activities - Cost: € 36.993,60 Activity no.1.2 - Name: Administrative and financial management - Cost: € 23.993,60 Activity no.1.3 - Name: Dissemination - Cost: € 36.410,80 For each activity, provide the required information: Activity no. 1.1 - Name: Coordination of Activities The following activities will be carried out: - contacts with the Regione Toscana on the scientific and technical issues; - setting-up the Steering Committee and the IPRs Responsible, and their rules of operation; - organising kick-off (month 2) and technical review meetings (month 5, 10, 16, 22, 30, 34); - creating and managing technical reports, i.e. yearly periodic activity reports, every 12 months, since month 12; - risk analysis of the relevant aspects of OLIMPIA project; - periodic evaluation of results at OO level and at overall level; - assessment of progress results of the project at each important phase and milestone; - promoting project follow-up; - promoting IPR protection of the results coming from the project; - analysis of the dissemination actions carried out at each phase of the project; - the consolidation of a Stakeholders Advisory Board in the first 6 months, composed of the external OR. |
Tools/equipment: N/A Human resources: -Staffes personnel (full time person month): 1.2 PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 4.8 PMs -Total Personnel (full time person month): 6 PMs Subcontracts: ATS and guarantee policy Expected results: Deliverables and/or Milestones D1.1 – Kick-off meeting (M2, IBR-SSSA) D1.3 – Annual report 1 (M12, IBR-SSSA) D1.4 – Annual report 2 (M24, IBR-SSSA) D1.4 – Annual report 3 (M36, IBR-SSSA) Timing: M1-M36 Activity no. 1.2 - Name: Administrative and financial management The following issues will be covered: - contacts with the REgione Toscana on administrative and financial issues; - yearly periodic management reports, every 12 months, since month 12; - overall legal administrative activities; - contractual, financial, administrative and ethical management of the consortium; - preparation and maintenance of the ATS and contracts; - financial statements; - issuing audit certificates; - solving knowledge management conflicts (Intellectual Property Rights, dissemination, patenting, etc.). Tools/equipment: N/A Human resources: -Staffes personnel (full time person month) 1.2 PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 4.8 PMs -Total Personnel (full time person month) 6 PMs Subcontracts: N/A |
Expected results: Deliverables and/or Milestones N/A Timing: M1 – M36 Activity no. 1.3 - Name: Dissemination The dissemination activities will be organized in two different levels: internal and external dissemination. The internal dissemination is essential to build a community within the consortium and to spread and share results and solutions obtained by each research group to other members of the OLIMPIA Consortium. This type of dissemination will occur through meetings, tele/audio-calls and workshops involving all OLIMPIA members and executed during all stages of the project. In particular six project meetings are already planned (M5, M10, M16, M22, M30, M34) to discuss the work carried out till that moment and to allow the consortium to meet those stakeholders that will be involved in the project to obtain from them some direct feedbacks on the OLIMPIA services and work. Furthermore other meetings will be carried out close to the experimentations to plan specific actions related to the tests and the integration of the OLIMPIA systems and according to the progress of the researches. The communication flow will be guaranteed by means of the set-up of a repository website for internal use and by means of mailing lists. External dissemination aims at providing both scientific and public information to the external communities and lead users (academics, industries, citizens, public administrations, insurances, etc.). The scientific dissemination will consist of publishing research results, and theoretical insights gained in international scientific journals and scientific and technical books, and through participating in international conferences related to these research fields. Public dissemination will be carried out through public meetings organized by OLIMPIA consortium and, in case, on invitation of bodies and institutions external to this project. During these events informative material, such as brochures, will be distributed. Furthermore, through the websites of OLIMPIA project and of the partners it will be possible to consult and get access to the activities, progresses, and results. These websites will explain the OLIMPIA aims, inform and provide updates on the current state of the research, provide visual material and videos and will have a section to collect opinions and remarks of web-users. In addition, information and discussions will be shared and disseminated by means of the most important on-line social networks (LinkedIn, Twitter, Facebook, Youtube, etc.). Moreover during the project lifetime, mass media (TV and radio programs, magazines, newspapers) will be involved to view and show to a wide public the value of the services and the results of the OLIMPIA project. Tools/equipment: N/A Human resources: -Staffes personnel (full time person month) 1.1PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 4.4PMs -Total Personnel (full time person month) 5.5PMs Subcontracts: |
N/A Expected results: Deliverables and/or Milestones D1.2 – Dissemination material and plan (M6, IFC-CNR) Timing: M1-M36 Operational Objective no. (2) Name: Guidelines, Requirements, and Design for Services Description of the operational objective: This OO aims to define clinical and technological guidelines to address the end-users' needs, designing, developing and validating the OLIMPIA services by means of acceptability and usability criteria. The principles of Service Design Thinking will be followed identifying significant metrics to quantify and measure the social, clinical and economic impact of the technologies and services in the QoL of PD patients. A business model will be designed to xxxxxx market success for the new services and procedures for product qualification will be discussed and planned. The main objective of this WP will be to: - identify the end-user needs to create services that will address the clinical requirements of PD patients and that can be integrated in the field of healthcare and social care in Tuscany. - Define, develop and prototype services to be validated in an iterative process of co-creation with stakeholders. - Define technological specification and certification procedures. - Define metrics for acceptance and usability evaluation. - Define the guidelines in terms of ethical, legal and social issues. - Analyse the conditions for the implementation of OLIMPIA services by means of business models investigation. This WP aims to xxxxxx a high degree of acceptance in stakeholders and user groups as important basis for the market success and a positive health and QoL effects of the OLIMPIA system. Different stakeholders (e.g. end users, family caregivers, clinical staff, technology developers, clinicians, healthcare managers, insurances, etc.) will be involved in interviews and focus groups to find ideas, select early concepts, analyse and assess the utility of OLIMPIA services. Expected Results: deliverables e milestones Explain the expected results during the operational objective, including whether specific deliverables and milestones are foreseen for the implementation of the project. X specific measurable and verifiable results will be produced during the course of the objective (deliverables) If yes, please indicate in which activity(ies): Activity no.2.4 – There is one final deliverable D2.1 for OO2 produced in A2.4, but that takes into accounts the achievements of all activities. X the objective includes check points (milestones) If yes, please indicate in which activity(ies): Activity no.2.4 – There is the first milestone that M1 |
Timing: M1 – M12 Total cost of the objective: € 72.808,80 List of activities envisaged under the Operational Objective: Activity no.2.1 - Name: Stakeholders’ requirements analysis and guidelines - Cost: € 17.410,80 Activity no.2.2 - Name: Clinical protocol consolidation - Cost: € 22.159,20 Activity no.2.3 - Name: Service model investigation and definition - Cost: € 20.576,40 Activity no.2.4 - Name: Experimental methodology consolidation - Cost: € 12.662,40 For each activity, provide the required information: Activity no. 2.1 - Name: Stakeholders’ requirements analysis and guidelines This activity aims at studying and defining the clinical requirements that the services should satisfy in order to both deploy the OLIMPIA PD care program and address the clinical objectives. The clinical hypothesis and expectations will be described in detail identifying appropriate and significant metrics for care assessment. This work will start from the analysis of current clinical evaluation tools and the most appropriate clinical metrics will be defined by the OLIMPIA clinical team. These evaluation approaches should be consistent and responding to requirements established by the Parkinson scientific communities. Additionally, this activity aims to explore, find and define the requirements from the users' point of view, gaining insights in stakeholders' behaviours and needs and translating them into values for patients as well as formal (experts) and informal (family members) care providers. Experts, family members, and the members of the target group will be interviewed for further prototyping identifying realistic scenarios, particularly for home services. Tools/equipment: N/A Human resources: -Staffes personnel (full time person month): 1.1 PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month): 4.4 PMs -Total Personnel (full time person month): 5.5 PMs Subcontracts: N/A Expected results: Deliverables and/or Milestones N/A Timing: M1-M6 |
Activity no. 2.2 - Name: Clinical protocol consolidation In this Activity the clinical standard and protocols to be fulfilled during the experimentation phases will be defined to promote the validation of the methodology of early diagnosis and management of PD respecting a “Good Clinical Practice” approach and tracking the guidelines of quality assessment regarding the compliance to the standard defined. This task aims at analysing all the current protocols and standards as necessary step to verify and certify that the clinical methodology is secure and suited for citizen and community. Tools/equipment: N/A Human resources: -Staffes personnel (full time person month) 1.4PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 5.6PMs -Total Personnel (full time person month) 7PMs Subcontracts: N/A Expected results: Deliverables and/or Milestones N/A Timing: M4-M9 Activity no. 2.3 - Name: Service model investigation and definition This activity will explore opportunities for new services, based on the outcomes of the previous ones. Together with stakeholders concepts of services will be defined. Validation of concepts with target groups will lead to refining of them, and in an iterative process it will lead to the development of service prototypes. This process will take in co-creation with stakeholders and concerns an iterative process until all stakeholders find confidence in the solutions. This task will define the experimental prototypes for all services that will be implemented in OO5. Particularly the clinical team will decide in detail for each service: • which need will be fulfilled with the new service; • which elements are involved; • what are the role and tasks of the patients; • how the tasks have to be carried out; • how many repetitions or duration for each task. Furthermore, to define the experimental prototypes, also issues related to QoL, acceptability and usability will be considered. Tools/equipment: N/A |
Human resources: -Staffes personnel (full time person month) 1.3PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 5.2PMs -Total Personnel (full time person month) 6.5PMs Subcontracts: N/A Expected results: Deliverables and/or Milestones N/A Timing: M4-M12 Activity no. 2.4 - Name: Experimental methodology consolidation This task will analyse the conditions for implementation of new OLIMPIA services. The conditions will be worked out with stakeholders into a Service Blueprint. The Service Blueprint will identify all aspects of a service and will clarify all activities and conditions before a new service can be implemented. The conditions will be based on the 5 themes in risk management of telemedicine: 1) People (Communication, Competence), 2) Processes and Procedures, 3) Information, 4) Technology, 5) Finance. Additionally, technical opportunities, barriers and framework conditions will be taken into account. The combination of these two views will be fundamental for guiding the design of OLIMPIA system and services and the field study. The activities will be carried out on the consortium side through meeting and video/audio calls involving all consortium members and on the user / stakeholder side through 2 workshops. During these events, all partners will discuss the services’ frame and find a common agreement on them. The Service Blueprint and the technical constraints are the construct for an implementation plan in which all elements, tasks, schedule and responsibilities will be described. A communication plan will describe how to organize the necessary communication around the process of the implementation. Tools/equipment: N/A Human resources: -Staffes personnel (full time person month) 0.8PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 3.2PMs -Total Personnel (full time person month) 4PMs Subcontracts: N/A |
Expected results: Deliverables and/or Milestones D2.1 – Final experimental methodology guidelines (M12, OA-AUSLTNO) Timing: M7-M12 Operational Objective no. (3) Name: Healthcare Infrastructure Development and System Integration Description of the operational objective: The objectives of this WP are to design, develop and produce a complete technological ensemble of wearable devices and Cloud platform. The development of a pervasive system based on these ICT solutions will allow to establish a complete program of care and monitoring able to measure and collect timely information about the status of the patient; the OLIMPIA system will xxxxxx full awareness of the disease and a new self- management skill in PD while ensuring the clinical staff to follow the patients in fully automatic mode, especially during critical situations. Expected Results: deliverables e milestones Explain the expected results during the operational objective, including whether specific deliverables and milestones are foreseen for the implementation of the project. X specific measurable and verifiable results will be produced during the course of the objective (deliverables) If yes, please indicate in which activity(ies): Activity 3.1 Activity 3.6 X the objective includes check points (milestones) If yes, please indicate in which activity(ies): Activity 3.6 Timing: M3-M27 Total cost of the objective: € 179.152,80 List of activities envisaged under the Operational Objective: Activity no.3.1 - Name: Definition of system architecture - Cost: € 9.496,80 Activity no.3.2 - Name: Wearable sensors refinement - Cost: € 118.331,20 Activity no.3.3 - Name: Design and implementation of the Cloud platform_ - Cost: € 14.331,20 Activity no.3.4 - Name: Web app interfaces in Cloud for patients and clinicians - Cost: € 24.331,20 Activity no.3.5 - Name: Algorithms transferring into libraries and Cloud resources - Cost: € 6.331,20 Activity no.3.6 - Name: System integration and Lab tests - Cost: € 6.331,20 For each activity, provide the required information: |
Activity no. 3.1 - Name: Definition of system architecture The first part of the work that is needed for the integration of the different parts into a whole is to clearly define the data that sensors will send to the platform, and the data that is worth storing in order to allow further processing (reasoning) and/or presentation. Data model must be completed with detailed format definition for the interfaces, the mobile and web applications and for their parameters. The main aspects that will be faced are here listed, according to the stakeholders needs identified in the OO2: • Define the optimal M2M and M2C communication architecture. • Define the API to save/retrieve data to the cloud platform • Design and implementation of database in cloud • Design and implementation of accessibility rules to preserve privacy and security of data • Definition and Implementation of different levels of reasoning algorithms Tools/equipment: N/A Human resources: -Staffes personnel (full time person month) 0.6PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 2.4PMs -Total Personnel (full time person month) 3PMs Subcontracts: N/A Expected results: Deliverables and/or Milestones D3.1 – Definition of the overall architecture of the system for service in hospital and at home. Timing: M4-M12 Activity no. 3.2 - Name: Wearable sensors refinement The aim of this activity is to physically implement, calibrate and test the wearable sensors already developed in the DAPHNAE project. Particularly possible minimal refinements could be implemented for what concerns the battery capability, the packaging for improving wearability and firmware optimization for energy consumption and computation capability. Tools/equipment: N/A Human resources: -Staffes personnel (full time person month) 0.4PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 0.6PMs |
-Total Personnel (full time person month) 2PMs Subcontracts: N/A Expected results: Deliverables and/or Milestones N/A Timing: M4-M12 Activity no. 3.3 - Name: Design and implementation of the Cloud platform Data model must be completed with detailed format definition for the interfaces and for their parameters. Referring to interfaces, there are two different kinds of APIs. The first to send data to the platform, the second to retrieve it, possibly after a reasoning phase that encompasses filtering, extraction of relevant information, aggregation of data, inference of metadata from the analysis of available data. Whatever form these interfaces will take (SOAP, REST or proprietary), this activity must provide all details needed to create a first platform prototype, in order to let sensor gateway to send data in the correct format and to use the interfaces in order to guarantee the best user experience. At this level, there will be also the implementation of the Cloud Infrastructure, designing and setting up the Cloud system that will host the web-platform. Regarding security issues, the infrastructure behind the platform will benefit from the security, privacy and reliability features of the Tix infrastructure. After data model, API definition and platform design done in the previously, this task will involve the implementation of the Data Layer and some Core-Layer functionalities. In particular, a Database that suits the needs of the proposed data model will be chosen. From the Core-Layer perspective, this task aims also to implements the Action Modules functionalities. Together with the Action Modules, will start the development of the part of the platform that deals with the generation of the web interface. This interface will be “responsive”, that is adaptable to different display devices, depending on their size. Core-Layer development will take into account security aspects in data transfer with client devices. More generally, the issue of security in communications with the platform will be mainly managed at the application level, by adopting standards for secure communications (e.g. HTTPS), both in the development of the API that in the interaction with the Presentation Layer. With these technologies, the task will produce a first prototype of the service. Tools/equipment: N/A Human resources: -Staffes personnel (full time person month) 0.4PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 1.6PMs -Total Personnel (full time person month) 2PMs Subcontracts: |
N/A Expected results: Deliverables and/or Milestones N/A Timing: M4-M18 Activity no. 3.4 - Name: Web app interfaces in Cloud for patients and clinicians On the base of the end-user requests and acceptance criteria (OO2), the aim of this task is to develop mobile apps for tables or smartphones that enable all stakeholders to access to services and perform the following actions: - medical staff can access to patient data to evaluate the status of the disease or the planned therapy; in addition can interact with the patients asking to compile neuropsychological questionnaires or suggesting modification of therapies or calling for specialistic visits in hospital in case of critical situations; - patients can see their service profile, the ongoing of the disease, the therapy, etc. as a diary and can answer to medical staff inputs; Although the sensors of the OLIMPIA systems mostly will work seamlessly, a few user interactions for configuration and management of the used sensors have to be implemented. On the user side in this task a simple interface for look-up, pairing, calibration and validation of sensor data will be implemented. Also for the clinical side a user interface for initialization and remote configuration of the sensors has to be implemented. Tools/equipment: N/A Human resources: -Staffes personnel (full time person month) 0.4PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 1.6PMs -Total Personnel (full time person month) 2PMs Subcontracts: N/A Expected results: Deliverables and/or Milestones N/A Timing: M7-M18 Activity no. 3.5 - Name: Algorithm transferring into libraries and Cloud resources This task involves the design and the implementation of Reasoning Algorithms, capable of computing raw data in order to extract information meaningful for different user profiles. Raw data |
may come from different types of devices that, through the APIs, will send information to the web- platform. As output of this task, there will be a prototype module and an interface description, which will be used also to drive DB design. Tools/equipment: N/A Human resources: -Staffes personnel (full time person month) 0.4PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 1.6PMs -Total Personnel (full time person month) 2PMs Subcontracts: N/A Expected results: Deliverables and/or Milestones N/A Timing: M7-M27 Activity no. 3.6 - Name: System integration and Lab tests This task involves the integration of Core-Layer Reasoning Algorithms and their integration in the platform of Core-Layer Reasoning together with APIs, the part of Presentation Layer that deals with Reasoning Algorithms and subsystems used to alert caregivers. Every subsystem will be tested with the platform, in order to assess code correctness, performance and scalability. Bug fixes and small scope modification will bring a first cloud platform capable of realizing the basic services foreseen for this phase. Tools/equipment: N/A Human resources: -Staffes personnel (full time person month) 0.4PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 1.6PMs -Total Personnel (full time person month) 2PMs Subcontracts: N/A Expected results: Deliverables and/or Milestones D3.2 – Final system development and delivery (M18, IBR-SSSA) M2 – The overall OLIMPA system is ready for the experimentation. |
Timing: M10-M18 Operational Objective no. (4) Name: Artificial Intelligence for Parkinson’s disease diagnosis and assessment Description of the operational objective: The aim of this OO is to define and implement the algorithms required for reasoning and awareness among and within the experimental sessions. Expected Results: deliverables e milestones Explain the expected results during the operational objective, including whether specific deliverables and milestones are foreseen for the implementation of the project. X specific measurable and verifiable results will be produced during the course of the objective (deliverables) If yes, please indicate in which activity(ies): Activity 4.1 □ the objective includes check points (milestones) If yes, please indicate in which activity(ies): Timing: M10-M33 Total cost of the objective: € 85.471,20 List of activities envisaged under the Operational Objective: Activity no.4.1 - Name: Machine Learning and Deep Learning for motor assessment - Cost: € 28.490,40 Activity no.4.2- Name: Automated algorithms for clinical parameters extraction - Cost: € 28.490,40 Activity no.4.3 - Name: Machine Learning development for PD early diagnosis and stage classification - Cost: € 28.490,40 For each activity, provide the required information: Activity no. 4.1 - Name: Machine Learning and Deep Learning for motor assessment Motion sensors largely used IMU based wearable sensors for the assessment of motor fluctuation in the Parkinson diagnosis. Machine learning techniques (supervised and unsupervised) extensively used. In general, machine learning techniques (supervised, unsupervised) needed large dataset results in better performance from the algorithms, which typically required to tune high number of hyperparameters, ultimately prone to overfitting. When the properties of data change over time, which create serious challenge in traditional machine learning algorithm’s, and |
causes overfitting, due to tune the high number of hyperparameters. Moreover, traditional supervised machine learning algorithms do not support the incremental update of the data. whenever there is fresh data there is need to update the training model. In the recent years deep learning such as convolutional neural networks (CNNs) have shown excellent performance on the different medical image processing. Since, CNN are more inclined for image processing, therefore sensor data intended to convert in to image description to support the extraction of discriminative features. CNN with wearable sensor data for motor assessment in PD has not been systematically investigated yet. Which could overcome the limitations in traditional machine learning algorithm’s. There is need to develop new deep neural network architecture according to the sensor data which could the first objective of this activity. In the next step the CNN architecture would be compare with the traditional machine learning algorithm’s. These are the following activities would perform in this section: ⮚ Implementation and design of CNN architecture on wearable sensor data ⮚ Implementation of machine learning traditional algorithms ⮚ Comparison between CNN and traditional machine learning algorithms Tools/equipment: N/A Human resources: -Staffes personnel (full time person month) 1.8PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 7.2PMs -Total Personnel (full time person month) 9PMs Subcontracts: N/A Expected results: Deliverables and/or Milestones N/A Timing: M10-M24 Activity no. 4.2 - Name: Automated algorithms for clinical parameters extraction This task is devoted to the design and development of reasoning algorithms that will operate on sensor data producing features with clinical significance. The Core Layer will provide services to operate on these algorithms and to manage required configuration in which the algorithms can operate. The focus of this activity is to automatize the extrapolation of features. Tools/equipment: Define the tools and equipment that will be used to carry out the activities Human resources: -Staffes personnel (full time person month) 1.8PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 7.2PMs |
-Total Personnel (full time person month) 9PMs Subcontracts: N/A Expected results: Deliverables and/or Milestones N/A Timing: M10-M33 Activity no. 4.3 - Name: Machine Learning development for PD early diagnosis and stage classification Early diagnosis and progression of the Parkinson disease is not yet explored extensively. The reason behind is lake of large data available. To investigate the early stage of the disease, there is need to include the subjects with minimal motor abnormalities must be included. Even the recruitment of such patients is difficult because they often do not go the doctor until symptoms are already widely appeared. For early diagnosis of disease and for the assessment of disease progression there is need to investigate unsupervised learning techniques. Supervised machine learning techniques have been extensively used in predicting PD through a set of datasets. However, the most methods developed by supervised methods do not support the incremental updates of data. In addition, the standard supervised techniques cannot be used in an incremental situation for disease prediction and therefore they require to recompute all the training data to build the prediction models. There is need to explore soft clustering techniques such as Gaussian Mixture Model. Soft clustering is an alternative clustering method that allows some data points to belong to multiple clusters. Similarly, semi-supervised learning technique’s such as Adaptive Fuzzy inference model with the knowledge formulation from subconscious path rules and membership functions are developed with automated techniques, such as gradient method, learning techniques, clustering methods clustering algorithms. Deep learning also not yet extensively explored to assess the disease progression and for early diagnosis. Additionally, there is also growing evidence that Mild cognitive impairment (MCI) is a common symptom at the base line of the early diagnosis of PD, but the neural mechanism is unclear. There is also needed to investigate the resting-state morphological and functional magnetic resonance imaging (fRMI) (or also DATSCAN) data same time with the wearable sensor which would be helpful to improve the overall prediction accuracy of the unsupervised learning techniques. Following are the activities would be done in this part: ⮚ Implementation of the soft clustering techniques such as Gaussian Mixture Model ⮚ Implementation of Semi supervised leaning techniques such as Adaptive Neuro Fuzzy inference system ⮚ Implantation of deep leaning architecture ⮚ Comparison between different unsupervised learning techniques in order to improve the overall accuracy ⮚ Analysis with imaging and wearable sensor data to improve the overall prediction accuracy in machine learning algorithms. |
Tools/equipment: N/A Human resources: -Staffes personnel (full time person month) 1.8PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 7.2PMs -Total Personnel (full time person month) 9PMs Subcontracts: N/A Expected results: Deliverables and/or Milestones D4.1 – Report on the use of machine learning for PD: lessons learnt and efficiency. Timing: M10-M33 Operational Objective no. (5) Name: Clinical Experimentation and Evaluation Description of the operational objective: In this OO the experimentations of OLIMPIA services with patient, caregivers and clinical staff in real environments will be carried out to validate the services and devices and demonstrate their feasibility for favouring the citizen empowerment in health and disease management and improving the quality of life and independence in the community. Expected Results: deliverables e milestones Explain the expected results during the operational objective, including whether specific deliverables and milestones are foreseen for the implementation of the project. X specific measurable and verifiable results will be produced during the course of the objective (deliverables) If yes, please indicate in which activity(ies): Activity 5.3 □ the objective includes check points (milestones) If yes, please indicate in which activity(ies): Timing: M4-M35 Total cost of the objective: € 386.572,80 List of activities envisaged under the Operational Objective: |
Activity no.5.1 - Name: Pilot sites management, recruitment, and training - Cost: € 31.656,00 Activity no.5.2 - Name: Clinical experimentation - Cost: € 247.286,40 Activity no.5.3 - Name: Final evaluation - Cost: € 107.630,40 For each activity, provide the required information: Activity no. 5.1 - Name: Pilot sites management, recruitment, and training The main action of this task is to manage and maintain the pilot sites to carry out the experimentations of OLIMPIA services. Different typologies of patients will be recruited for OLIMPIA services' experimentation: healthy subjects for hyposmya screening; hyposmya subjects; healthy controls; PD patients; PD patients De-Novo (not pharmacologically treated). Before the beginning of the EMPHASIS experimentation, the socio-medical and PD subjects staff will be trained by the technical partners to autonomously use the developed technological ICTs solutions. After these training sessions: • PD subjects should be able to: - install and use devices without the assistance of technical support; - save acquired data; - receive and consult the information from clinical staff through the end-user interfaces. • the socio-medical partners should be able to: - install and use devices without the assistance of technical support; - save acquired data; - access to the cloud to manage and interpret stored data; - remotely support PD patients and their informal caregivers for services @home. Tools/equipment: N/A Human resources: Specify for each partner the skills and relative timing (in full time person month) needed to carry out the activities. -Staffes personnel (full time person month) 2PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 8PMs -Total Personnel (full time person month) 10PMs Subcontracts: N/A Expected results: Deliverables and/or Milestones N/A Timing: |
M4-M30 Activity no. 5.2 - Name: Clinical experimentation This activity concerns the execution of the global experimentation as described in the first part of this proposal. Basically, three experimental actions will be performed. In order of time, the experimentation will start with the hyposmya screening on the territory and then, according to service models defined in OO2, the experimentation will go toward the ambulatory service in hospital and to the domiciliary telemedicine services. This experimentation will be useful, not only to assess the effectiveness of the proposed service models, but also to acquire data for enhancing the scientific clinical challenges. Tools/equipment: N/A Human resources: Specify for each partner the skills and relative timing (in full time person month) needed to carry out the activities. -Staffes personnel (full time person month) 8.8PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 35.2PMs -Total Personnel (full time person month) 44PMs Subcontracts: N/A Expected results: Deliverables and/or Milestones PMs Timing: M7-M32 |
Figure 5. The experimental plan proposed in OLIMPIA for recruitment, identification, and assessment of healthy subjects, IH people, and PD patients. Activity no. 5.3 - Name: Final evaluation A global evaluation of the experimental phase will be performed on two different levels: • Medical / treatment assessment The methodology and metrics for medical assessment of the level of PD on patients will be defined and will be evaluated during the entire experimental sessions. Data acquired during the biomechanical analysis will be analyzed and evaluated by means of specific algorithms for data processing that will be developed to objectively measure parameters to quantify subjects’ motor performances, such as frequencies, amplitudes, smoothness, etc. Particularly these algorithms will be based on feature extraction in the domain of time and frequency and statistical classification tools, such as Principal Components Analysis, State Vector Machine, Neural Networks, Machine Learning, etc. The acquisition of data during experimental sessions will permit to evaluate changes in subjects’ skills over time. Specifically the assessment of healthy controls performances will determine normative values of reference for biomechanical features extracted by the exercises.The final aim is the development of a "PD curve" in which all the subjects will be collocated according to their performances. This "curve of disease" will be able to identify for each patient his level of pathology with great support to diagnosis. • Quality of life, Quality of services and Usability / Acceptability assessment of OLIMPIA technologies The methodology and metrics for QoL, QoS and Acceptability will be evaluated during the entire experimental sessions, considering how these metrics change from the beginning of the experimentation (services without OLIMPIA system) and during and after the experimentation (services with the OLIMPIA system) with particular attention on the empowerment of the patients in the self-care program. |
Tools/equipment: N/A Human resources: Specify for each partner the skills and relative timing (in full time person month) needed to carry out the activities. -Staffes personnel (full time person month) 6.8PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 27.2PMs -Total Personnel (full time person month) 34PMs Subcontracts: N/A Expected results: Deliverables and/or Milestones D5.1 – Report on the overall experimentation in hospital and at home. Timing: M13-M35 Operational Objective no. (6) Name: Exploitation and Impact Assessment Description of the operational objective: This OO aims to analyze the socio-economic impact of XXXXXXX proposal and the simulation of the long run benefits for the patients and the healthcare system generated by the solutions developed in the project. Then, it will plan exploitation activities for the identification of opportunities for services and technology transfer in industry and in society. Expected Results: deliverables e milestones Explain the expected results during the operational objective, including whether specific deliverables and milestones are foreseen for the implementation of the project. X specific measurable and verifiable results will be produced during the course of the objective (deliverables) If yes, please indicate in which activity(ies): Activity 6.1 Activity 6.3 Activity 6.4 □ the objective includes check points (milestones) If yes, please indicate in which activity(ies): Timing: M1-M36 Total cost of the objective: |
€ 99.716,40 List of activities envisaged under the Operational Objective: Activity no.6.1 - Name: Data management plan - Cost: € 17.410,80 Activity no.6.2 - Name: Innovation management - Cost: € 14.245,20 Activity no.6.3 - Name: Socio-economic impact assessment - Cost: € 33.238,80 Activity no.6.4 - Name: Exploitation plan and IPR management - Cost: € 34.821,60 For each activity, provide the required information: Activity no. 6.1 - Name: Data management plan Confidentiality and data protection is very important when patient’s records are used. A trustworthy structure by the use of message security model in terms of security tokens, in combination with digital signatures to protect and authenticate SOAP messages, will be designed. X.509 certificates will be also used in the application. Different degrees of authorization will be created for viewing, adding or modifying this information. Additionally, the procedures for ethical committee approval will be followed in order to activate the experimentation. Tools/equipment: N/A Human resources: -Staffes personnel (full time person month) 1.1PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 4.4PMs -Total Personnel (full time person month) 5.5PMs Subcontracts: N/A Expected results: Deliverables and/or Milestones Describe the project results check points (milestones) and describe the main measurable and verifiable results (deliverables) indicated in the "operational objective" section, specifying the units of measurement and expected values at the end of the project. D6.1 - Timing: M1-M18 Activity no. 6.2 - Name: Innovation management This task focuses on the definition and use of appropriate tools for product and process innovation during the project, favouring the common understanding of processes and objectives within the consortium, the exploitation of external or internal opportunities and the use of creativity to introduce new ideas, processes or products. According to the requirements of the Project, specific |
tools will be used, such as brainstorming, product lifecycle management, idea management, etc., in order to always promote the integration of organization, technology and market. Furthermore, in order to manage better the innovation, various aspects of the process of innovation and its outcomes will be measured and assessed, focusing on inputs and outputs, expected impact, measures to assess the activities and availability of factors that facilitate such a process. Tools/equipment: N/A Human resources: -Staffes personnel (full time person month) 0.9PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 3.6PMs -Total Personnel (full time person month) 4.5PMs Subcontracts: N/A Expected results: Deliverables and/or Milestones N/A Timing: M13-M30 Activity no. 6.3 - Name: Socio-economic impact assessment The activities of the tasks will follows the below general perspectives on data collection and analysis: • The clinical and socio-economic impact of OLIMPIA will be assessed analysing data coming directly from the patients in the hospital and at home’. This data will be available for researcher and people involved in the impact analysis by a dedicated platform. • Economic data will be certified by the responsibility of accounting department of clinical partners for the information coming from the clinical centres. • The economic impact of EMPHASIS will be assessed considering direct and indirect health and no health costs, quality of life and patients’ empowerment differences in adopting or not the EMPHASIS’ solutions. • The long run clinical and socio-economic impact of EMPHASIS will be assessed through simulation techniques that, based on the available data collected in the project, will consider the evolution of clinical and health resource consumption in the two opposite scenarios of the status quo and the EMPHASIS solution of Parkinson management. • Simulation will be performed modeling the pathways of the early stages and the development of PD. • The quality of life assessment will be achieved through standard questionnaires (PDQ-39: the Parkinson's disease quality of life questionnaire) that will be administrated using the apps in the hands of patients and/or their caregivers. • The impact of EMPHASIS in increasing patients’ empowerment will be assessed through data |
on the frequency of using the telemedicine solutions directly available from the patients through the telemedicine technology and apps. • The overall assessment will be based on the MAFEIP instrument (xxxxx://xxx.xxxxxx.xx/), i.e. the "Monitoring and Assessment Framework for the European Innovation Partnership on Active and Healthy Ageing" (MAFEIP), developed to support evidence-based decision-making processes for all institutions and users in the health and care sector. Tools/equipment: N/A Human resources: -Staffes personnel (full time person month) 2.1PMs -R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 8.4PMs -Total Personnel (full time person month) 10.5PMs Subcontracts: N/A Expected results: Deliverables and/or Milestones D6.2 – Report on the socio economic impact (M30, IBR-SSSA) Timing: M19-M30 Activity no. 6.4 - Name: Exploitation plan and IPR management The OLIMPIA exploitation activities will include the identification of opportunities for services and technology transfer in industry and society. All partners taking part in the project will be involved and will be allowed to co-operate with partners, companies and organisations external to the project. OLIMPIA researchers will study how the results of previous OOs on the service and business models of OLIMPIA tools can be imported in the real contexts, the obtainable advantages, the needed changes both in term of costs, social services and employees, and to compare these information with usual services. The activities of this task will include: • Identification and description of customer groups: identification of stakeholders and end user groups, their main interest and main benefits of OLIMPIA; identification of communication channels useful to get in touch with these groups; identification of information requirements of these groups (who needs which information to raise interest in OLIMPIA results?). • Research and draft development of business models: identification and comparison of successful business models (national, European and international) in the field of disease prevention, management or treatment; transfer of suitable elements of these business models onto OLIMPIA; enhancement of business models to include and satisfy sufficient stakeholders, ensuring an high interest of participating in the distribution of the services, devices and systems developed. |
• IPR Management plan: since OLIMPIA could produce some patents in the management of Parkinson Disease, then an appropriate IPR plan will be developed. Thus patents will make the new technology developed in OLIMPIA publicly available, while still protecting the IPR of the consortium, and will support the impact of the project not only on academic and industrial research but also on industrial development.
• Exploitation and valorisation strategy: realization of the deliverable Exploitation plan that will provide a formal planning document for use and exploit the knowledge gained during the entire life cycle of the project OLIMPIA
Tools/equipment:
N/A
Human resources:
-Staffes personnel (full time person month) 2.2PMs
-R&D personnel with fixed-term employment relationships specifically hired for the project (full time person month) 8.8PMs
-Total Personnel (full time person month) 11PMs
Subcontracts:
N/A
Expected results: Deliverables and/or Milestones
D6.3 – Report on market and service model analysis, business and exploitation plan (M36, IBR- SSSA)
Timing:
M22-M36
Description of the activity carried out by the RO participating in the project pursuant to art. 4 of the call for proposals, specifying whether the participating ROs carry out an additional activity or whether they contribute to the activities listed above. IWATA University: Prof. Xxxxxx Xxxxxx is expert of Artificial intelligence and deep learning. He was one of the first to use deep learning with wearable sensor and is also editor in chief of the knowledge based journal. He is a perfect mentor to support the OLIMPIA project in developing the use of deep learning or anfis techniques in PD applications. TUKE University: Prof. Xxxxx Xxxxxx is expert of Cloud infrastructures and artificial intelligence. He is working on the cognitive robotics field and its implementation on the Cloud (Cloud Robotics). He is a perfect mentor to support the OLIMPIA project in developing Machine Learning based algorithms pervasively between the Cloud platform and xxx xxxx (or in local). |
UNIPA University: Prof. Xxxxxxx Xxxxxxxxx is a neurologist expert in treatments and therapies of PD. He is a perfect mentor both from a clinical and academic point of view to support the clinical partnership and will be mentoring the experimental methodology and the evaluation of results. |
The project includes clinical trial phases: X NO If YES specify: type of study trial phase (if applicable) If the project provides for the start-up of activities, clinical trial phases must be submitted to the relevant ethics committee for a positive opinion when the agreement is signed |
The project includes animal testing phases: X NO If the project involves animal testing phases, it is necessary to submit, at the conclusion of the agreement, the authorization of the Italian Ministry of Health according to art. 31 of Decreto Legislativo 26 del 4/3/2014. |
SECTION 4 – PROJECT SPECIFICATIONS IN RELATION TO THE SELECTION CRITERIA
A) Scientific and technical quality of the proposal • Scientific novelty, scientific merit and quality of approach; The scientific novelty of the OLIMPIA proposal has to be researched in the clinical and scientific/technological challenges that it aims to address (see par. 4). In particular the innovation is related to the opportunity to improve and standardize the current practice for PD diagnosis and monitoring, providing the clinicians with a reliable system composed by accurate wearable sensors and ease-to-use interfaces for data acquisition and feedback/results visualization. All the system is embedded into a structured cloud platform that can be integrated within the Regional Healthcare System. In detail the main scientific innovations are related to: ▪ Objective Diagnosis: OLIMPIA proposes an accurate reliable wearable system able to provide an objective assessment of the motor impairments caused by PD. The possibility to quantitatively measure the motion of the subjects will ensure an objective reduction of the variability that currently affects PD diagnosis. Semi-quantitative evaluation scales (i.e., MDS-UPDRS, HY) and the visual examination of motor tasks performed by the clinician during neurological exams (see par. 2.1-2.2) will be overcome by the accurate measurements of a wide set of parameters characterizing the movements. Moreover, the opportunity in OLIMPIA to store and update the acquired data into the cloud infrastructure enables the reduction of both intra- and inter-rater variability because the use of an unique database in which are saved all data from involved patients (e.g., clinical data, motor data, olfactory evaluation) can allow the availability of objective measures for all the authorized clinicians that can access to them. This can reduce misdiagnosis and improve the quality of care for patients, timely treated with the most adequate therapeutic path, slowing down the PD symptoms (according to HP3). ▪ Early Diagnosis: Currently there is no a standard procedure for early detecting PD. Nowadays, neuroimaging techniques, able to investigate cerebral pathologies, are currently used to confirm the PD diagnosis, but they require specially trained dedicated staff and are invasive, such as the single proton emission computed tomography (SPECT) DaTSCAN, expensive, such as the nuclear magnetic resonance with diffusion tensor imaging (NMR-DTI), or not specific enough, such as the Transcranial Sonography (see par. 2.1). The OLIMPIA system would allow the disease identification during the prodromal stage of the pathology through the accurate measurement of the motor pattern in subjects at risk for developing PD in the next 5 years. Hypothesizing that IH can be assumed as a preclinical biomarker for PD (see HP2, par. 2.2), identifiable with a low-cost validated test (i.e., the Italian Olfactory Identification Test – IOIT) [30] which is easy to administer, a two- step approach can represent a valuable solution to identify the early onset of PD by combining olfactory screening and non-invasive measurements of motion capabilities in IH subjects. This screening will be carried out by the GPs that will identify subjects with IH among their patients, and address them towards the Day Center and the neurologist for |
the assessment of motor performance. Following this process, PD could be reasonably diagnosed 5-7 years before than today (according to HP1), when the neuropathological process is already started but there is not clear evidence of the cardinal motor signs of the disease. ▪ Artificial Intelligence for PD: XXXXXXX proposes the study and implementation of innovative algorithms of machine learning, investigating both conventional (i.e., supervised classifiers and unsupervised methods) and unconventional, such as deep learning. Currently deep learning has been mainly used for image analysis, while regarding the PD some studies apply it to a voxel dataset. The use of deep learning on inertial sensors data (i.e., accelerometer, gyroscope) is novel and is arising in particular for applications of gesture and activity recognition. The use of this approach for motor analysis in PD is a novel challenge that will allow to measure new parameters providing a more accurate assessment and diagnosis, especially for specific complex impairments such as freezing of gait and hesitations. On the other hand, the wide amount of collected data in OLIMPIA will require Machine Learning techniques, particularly useful for Big Data management, thus to treat, analyse and make accessible and available the results of the analyses carried out within the project. ▪ Novel patient-centred healthcare model: OLIMPIA proposes the implementation and validation of an innovative healthcare model that, involving innovative technologies, places the patient at the centre and involves all the stakeholders, from GPs to specialist to local services and formal and informal caregivers, to guarantee a continuity of care and improve the quality of service and QoL for the patients. The OLIMPIA model is based on the Day Service, which is focused on the management of those diseases that request a specific diagnostic and therapeutic path. Thanks to the OLIMPIA services all the stakeholders involved in PD diagnosis and treatment will be integrated improving the quality of the care process and, at the same time, the patients will become more aware. Moreover, the model implemented in OLIMPIA, thanks to the use of the cloud infrastructure, the low cost sensors, and the daily service can be easily deployed. • Scientific evidence and credibility of the proposal The scientific innovation proposed in this project has a clear evidence, which derives from the accurate analysis of the State of the Art, about the clinical practice for PD diagnosis, the wearable devices developed for PD assessment and the telemedicine services proposed for PD home monitoring, highlighting the current limitations. The State of Art analysis allows to formulate the hypotheses described in par. 3 and, consequently define the challenges (par. 4) that XXXXXXX proposal would address. However, preliminary studies carried out during the past years allowed to lie down the basis for this proposal, with promising results for both early objective diagnosis and application of Machine Learning techniques for data classification. In particular, for the early PD detection, in the published paper “C. Xxxxxxxxx, et al. (2018). “Combining olfactory test and motion analysis sensors in Parkinson’s disease preclinical diagnosis: A pilot study”. Acta Neurologica Scandinavica. 137:204–211. xxxxx://xxx.xxx/00.0000/xxx.00000.” (see par. 7.4), it is presented a case-study of a subject |
identified as affected by IH during a territorial screening and analysed over the time with the sensors. The abnormal worsening of the performance after 1 year follow up in this subject, differently from other 20 IH subjects who maintained constant performance, induced the neurologist to administer him a sub-acute dopaminergic test with L-dopa over a month. The improvements of motion when the pharmacological therapy was at maximum dose, and the consequent worsening when it was taken off, allowed to identify this subject as a probable PD. Then a SPECT DaTSCAN confirmed the diagnosis, that was anticipated with respect of the current clinical practice of about 5 years. Differently, for the application of Machine Learning techniques, in the published paper “X. Xxxxxx, et al. (2018). “Comparative motor pre-clinical assessment in Parkinson's disease using supervised machine learning approaches”. Annals of Biomedical Engineering xxxxx://xxx.xxx/00.0000/x00000- 000-0000-0” (see par. 7.4), supervised classifiers where compared to identify statistically significant differences in motor performance between an healthy group and a PD patients group with excellent results (specificity and recall equal to 0.967). Moreover the same system was also able to distinguish the performance of an IH subjects group, within a multi-class approach classification, with 0.78 accuracy. This paper is the first work in literature that take into account the motor analysis of IH subjects and it is promising for supporting the neurologist for a quantitative assessment of patients’ motor performances, identifying slight worsening in motion capabilities for objective diagnosis of PD. These preliminary results suggest that, nevertheless the raised limitations, these approaches can be furtherly investigated for application in PD to improve diagnosis and assessment, with consequent enhancement of quality of care and patients QoL. Furthermore, the partners of this project have already acquired a robust know-how on these topics and can refine and improve them to create an accessible integrate system for the diagnosis and management of PD. • Clarity and appropriateness of the project development strategy The project development strategy is detailed in the GANTT chart (see attachment B1). The OLIMPIA work plan is organized in two main phases for a total duration of 36 months (3 years). The first phase is oriented to the development of the OLIMPIA system and services, while the second one to the experimentation and assessment. The first months of the project will be needed to define the clinical and technical requirements, to consolidate the clinical protocol and the experimental methodology, and to design the service model (OO2). The wearable system, the user’s interfaces and the cloud platform will be integrated into an Healthcare Infrastructure that enable the use of the system into the Regional Healthcare Service (OO3). Particular attention will be dedicated to the development and automation of innovative AI algorithms based on Machine Learning approaches for the extraction of new parameters of clinical interest for PD assessment, and for the management of the data acquired during the project (OO4). During the second phase, OLIMPIA services and system will be tested and evaluated in real settings, both in hospital and at home (OO5), under the supervision of the clinical partners by means of appropriate metrics (OO2). The experimental phase with the OLIMPIA wearable sensors will start during the second year (Figure 5) after the design, development and prototype of the system. During the entire duration of the project innovations, impacts and outcomes of the work will be documented, disseminated and exploited among the community and industry (OO1, OO6). The project management, as coordination of activities and administrative/financial management will last for all the project (OO1). |
ALLEGATO B BANDO RICERCA SALUTE 2018 |
• Applicability and transferability of results OLIMPIA aims to implement a system for the diagnosis and management of the PD that could be integrated into the Regional Healthcare Service. The OLIMPIA model, as healthcare service, is based on the Day Service for taking care of PD patients and people at risk for the disease, identifying for them the most appropriate PDTA. The Day Service is already presents in many AUSL of the NHS, thus the transferability of the results into the NHS is highly feasible, adapting the current service (generally dedicated to cardiovascular and gastroenterological diseases) to that proposed by OLIMPIA for neurological disorders and in particular for the Parkinson’s Disease management. The applicability and the transferability of the results will be assessed during the project by means of the Monitoring and Assessment Framework for European Innovation Partnership on active and healthy ageing (MAFIEP) tool, a monitoring framework comprising a web-based tool which rests on the principles of Decision Analytic Modelling (DAM). The MAFEIP tool has been already used by different institutions (governments, large and small companies, university, etc.) for assessing the potential impact of new propositions for health and social care interventions, becoming a standard for the European projects. The appropriate inputs required by the MAFEIP tool to perform an incremental analysis of the impact of the proposed innovation will be detailed and provided during the project (activity 6.4) to investigate and prove the cost-effectiveness and sustainability of the proposed solution. The main objective of the exploitation plan is to xxxxxx technological transfer that can lead to the creation of new business opportunities both in the investors’ side and occupational side, with the intention of ensuring a strong leadership position in these new markets. The mission of the consortium regarding the exploitation activities will be to launch the OLIMPIA model on the market, improving the quality of life of persons suffering from PD and, in the meantime, guaranteeing to the Healthcare System to optimize the costs (e.g., homecare) and the efficiency of the patients’ treatment assessment and of the PD early prevention (e.g., neuroprotective pharmacological treatment). |
B) Level of innovation: The OLIMPIA Proposal aims at achieving an innovation of both product and service through the engineering and commercialization of wearable devices, mobile/web applications as well as the end-users smart interfaces with a medical valence. Additionally, innovation is also pursued in terms of process by the integration of such technologies in specific healthcare processes through the connectivity with Cloud platforms and through a full integration with the current Regional services and healthcare paths in Tuscany. The innovation of the product is an immediate follow-up from the previous DAPHNE project, as already introduced before in this proposal, in which a wearable system was designed, tested and also CE certified, as well as taken in consideration by the spin-off company Co-Robotics srl for commercialization (xxx.xxxxxxxxxx.xx/xxxxxxx). Advancements were achieved in the term of physical proprieties (wearability, miniaturization, electronic integration) and functional features (complete analysis of the motor pattern) with regard to the measures of the hand motor performance and of the gait parameters. |
ALLEGATO B BANDO RICERCA SALUTE 2018 |
DAPHNE Project (04/2016 – 10/2018) OLIMPIA Project (2019 - 2021) Figure 6. The Technology readiness levels (TRL) of the OLIMPIA system for PD care. At the end of the DAPHNE Project, the wearable device was finalized and CE qualified for general purpose in movement analysis applications (TRL8); however, the system was not clinical validated, because a higher number of subjects was required for statistical robustness, and therefore not qualified as medical device: in this sense the system achieved TRL7 as a medical device. In the OLIMPIA proposal, the wearable system will be furthermore tested with a reasonable number of subjects in order to achieve the conditions for a medical validation and qualification. A TRL9 will be demonstrated and achieved thanks to the network of departments of neurology included in the consortium, where the system will be used during the normal clinical operations. Additionally, in the OLIMPIA proposal, the product innovation will be further pursued by the integration of the devices with a Cloud Platform with the objective to add storage and computational capabilities and also solving barrier related to the security of data (Cybersecurity). The integration of the system with cloud technologies will furthermore xxxxxx the possibility to use the system both in hospital (ambulatory) and at home in telemedicine services, thus laying the foundations for innovation in processes. The innovation of product can be evident also considering: • the absence, in the European market, of commercial technologies mainly based on inertial sensors used to measure the human hand motor performance, particularly considering the field of the early diagnosis, treatment and monitoring of Parkinson's disease; • the technological progress beyond the commercial solutions available in the global market used to measure the human motor performance and based on no-inertial technologies in terms of acceptability, usability and reliability; • the absence in the global market of smart tools of rehabilitation, end-users interfaces and dedicated web/mobile applications usable both at home and hospital. The innovation of service can be identified also taking into account the absence of: • a validated and overt methodology regarding the early diagnosis of Parkinson's disease; • an instrumentation to support the clinical staff in the diagnosis, treatment and monitoring of PD; • an instrumentation to support the patients during the rehabilitation procedures; |
ALLEGATO B BANDO RICERCA SALUTE 2018 |
• a complete service of care, treatment and rehabilitation personalized and accessible remotely; • social tools of communication between patients and clinical staff. The innovation of process consists also in the provision of both equipments and software able to: • include family/informal caregivers into the technology-assisted care process; • optimize the health service in terms of timeliness, flexibility, human resources as well as user base served; • optimize the health service in terms of cost saving; • reduce the CO2 emissions harmful to the environment minimizing the frequency of visits and transports. The OLIMPIA Proposal aims also at the validation of a new clinical protocol based on an innovative methodology of early diagnosis and pharmacological treatment and on monitoring. Particularly, it will aim to identify and validate: • novel protocols for early diagnosis based on olfactory and motor assessment • identification of the most suitable PDTA for each PD patient, based on the level of the pathology; • the possibility to integrate day services in PD; • the possibility to standardize the healthcare path in PD at regional level first and national and international level. |
C) Reliability of applicants: The OLIMPIA proposal involves, as partners, five main Research Organizations (ORs) located in the Tuscany Region. In particular the departments actually involved into the proposal are: the Institute of BioRobotica of Scuola Superiore Sant’Xxxx (IBR-SSSA), the Operative Units of Neurology of Ospedale delle Apuane (AUSL Toscana Nord-Ovest), Neurological Clinic 1 of Ospedale di Careggi (AOU Careggi), Ospedale Santa Xxxxx Xxxxxxxxxx di Firenze (AUSL Toscana Centro), and the Institute of Clinical Physiology of Consiglio Nazionale delle Ricerche of Pisa (IFC-CNR). • Scuola Superiore Sant’Xxxx (SSSA) The Scuola Superiore Sant'Xxxx (xxx.xxxxx.xx) is a public University with a special status admitting excellent students at graduate, doctoral and post-doctoral level in the sectors of engineering, medicine, agriculture, economics, law and political science. The mission of SSSA is to perform excellent research activity managed mostly by six Institutes, which are: BioRobotics, Law, Politics and Development, Economics, Management, Life Sciences and TeCIP - Institute of Communication, Information and Perception Technologies. These Institutes manage the research activity through a number of highly qualified research laboratories, most of them are located at the “Polo Sant'Xxxx Xxxxxxx” (PSV), in Pontedera, where also the BioRobotics Institute is located. The PSV is a research park specifically created to better the research activities of SSSA and to favour technology transfer, with a surface of 6,300 square meters and important facilities for design and (micro)fabrication, as well as for educational activities. |
The BioRobotics Institute (IBR) The Biorobotics Institute of Scuola Superiore Sant’Xxxx was founded in January 2011 by Xxxx. Xxxxx Xxxxx with the mission of performing research in the field of advanced robotics and it is located in Pontedera, Pisa. At present the Biorobotics Institute includes about 200 people, including 7 Full Professors, 4 Associate Professors, many Post-Doc researchers, and about 90 PhD students. The Biorobotics Institute conducts theoretical and experimental researches in biorobotics, a discipline characterized by a high degree of interdisciplinarity. For this reason it has a strong tendency toward integrating heterogeneous bodies of knowledge, of both scientific and humanistic nature, in order to study the theoretical, practical and social problems associated with the development of advanced robotic systems. Before 2011, the Biorobotics Institute was the ARTS Lab, that over the course of its 20-year history has built and consolidated a vast wealth of knowledge and expertise in the fields of Robotics Service Robotics, Humanoid Robotics, Neurorobotics, Bionics, Neural Interfaces, Assistive Robotics, Robotics for Neurorehabilitation, Gerontechnologies, Biomimetic Robotics. Several robotic platforms have been developed in the framework of national and international projects, as for example humanoid robots, platforms for experiments on learning and sensory-motor coordination, cybernetic and prosthetic hands, wearable devices for biomechanical motion analysis, robotic systems for functional support and rehabilitation of human limbs, humanoid robotic hands and systems for personal assistance of disable and elderly people. The BioRobotics Institute’s expertise actually covers the following main areas: Artificial Hands, Neuro-Robotics, Soft Robotics, Surgical Robotics and Allied Technologies, Robot Companions for Citizens (i.e. Assistive Robotics, Neurodevelopmental engineering), Biomedical Signal Processing, Translational Neural Engineering and Creative Design. Beyond the laboratories set in Pontedera, the BioRobotics Institute actually has 7 additional laboratories and research centres located in Tuscany area: Assistive Robotics – Ambient Assisted Living (AAL) Laboratory, Peccioli, Pisa; Laboratory of Rehabilitation Bioengineering at Auxilium Vitae Rehabilitation Centre, Volterra; Locomotion Disorders Laboratory, Pisa; Neuro-Developmental Engineering Laboratory, Pisa; Research Centre on Sea Technologies and Marine Robotics, Livorno, Center for Micro- BioRobotics IIT@SSA, Pontedera. Joint Open Lab with Telecom Italia on Disruptive Innovation in e-Health Systems, Pontedera. o Assistive Robotics – Ambient Assisted Living (AAL) Laboratory The BioRobotics Institute, first through the ARTS Lab, has been collaborating on the topics of services to the elderly citizens with the Municipality of Peccioli since 1995. This collaboration brought to the set-up of the Assistive Robotics and Ambient Assisted Living (AAL) Laboratory. The main goal of this lab is to develop and test different ICT and Robotic technologies to support people, especially elderly, in their daily life and in the execution of work task in order to improve their quality of life. The main research themes of the lab are Assistive Robotics and Social Robotics, Smart Environments monitoring systems (WSN and Aml), Wearable devices and Body Area Network, Roadmap activities and Coordination Actions, Acceptability and Dependability issues and ICT Industrial and Design consulting. Part of the lab is made of a domotic apartment that is used for experimental research, the test and the validation of assistive technologies also with real users. During these years Xxxxxxxx'x senior citizens have had the opportunity to test and be trained to different robotics’ technologies, have |
had direct experience with new ICT and robotics services and have proposed ideas and solutions to scientific community. The main investigator, and scientific coordinator of OLIMPIA, Dr. Xxxxxxx Xxxxxxx (MScEE, Phd in Bioengineering) is Assistant Professor at BioRobotics Institute, Scuola Superiore Sant’Xxxx, Pisa, Italy, focusing on cloud and social robotics, ambient assisted living, wireless and wearable sensor systems, biomedical processing, acceptability and AAL roadmapping. He participated in various National and European projects, being project manager of Robot-Era, AALIANCE2 and Parkinson Project, to name but a few. He was visiting researcher at the EndoCAS Center of Excellence, Pisa; at the Takanishi Lab, Waseda University, Tokyo; at Tecnalia Research Center, Spain. He was granted from the International Symposium of Robotics Research Committee as Fellowship Winner for best PhD thesis in Robotics; from the Regional POR FSE 2007-2013 for a 3-years Research position at The BioRobotics Institute; from the ACCESS-IT 2009 for the Good Practice Label in Alzheimer Project; from the Well-Tech Award for Quality of Life with the Robot-Era Project. He is author of various papers on conferences and ICI journals. • Azienda USL Toscana Nord-Ovest (AUSLTNO) The Azienda USL Toscana Nord Ovest, established with regional law n. 84 of 28th Dec 2015, includes the ex ASL of Massa Carrara, Lucca, Pisa, Livorno e Viareggio. The company has over 13,000 employees, 2 billion euros in budget, 13 hospital establishments, 12 district areas and a resident population of over 1 million and 200 thousand inhabitants. The AUSLTNO is part of the National Healthcare Service (NHS), as part of the SSR. The SSR, according to the principles and values of the Constitution and the Regional Statute, inspires its action to: o Citizen’s centrality and engagement, as holder of the right to health, and active subject in the care path; o Universality and equal access to health services for all patients; o Guarantee for all the assistants of the uniform and essential levels of assistance provided for in the planning documents; o Uniqueness of the health system and public funding of essential and uniform levels of assistance; o Institutional subsidiarity and full involvement of local authorities in health promotion policies; o Horizontal subsidiarity and enhancement of social training, in particular those operating in the third sector; o Competition of institutional subjects and participation of social partners in regional health planning documents; o Freedom of choice of the place of care and of the health worker in the context of the planned offer and assistance path; o Professional enhancement of the staff of the regional health service and promotion of its participation in the processes of planning and evaluation of the quality of services. Ospedale delle Apuane (OA) The OA is the provincial reference point for acute and complex cases where every citizen is guaranteed an effective diagnostic, therapeutic and timely path. Care intensity assistance includes: High Intensity level: intensive and sub-intensive stays (e.g., Intensive care, stroke unit); Average Intensity level: the acute hospitalizations subdivided by functional areas (e.g., Medical, |
Surgical, Maternity xxxx); a Low Intensity level dedicated to post-acute patients. The main investigator for OA-AUSLTNO is Dr. Xxxxx Xxxxxxxxx which is associate medical director at the hospital admission/stay unit of the Neurology department of the ‘Ospedale delle Apuane di Massa’ – Azienda USL Toscana Nordovest. Responsible for the Parkinson’s disease and movement disorders Clinic. Moreover activity at the clinical Neurophysiology clinic, at the Myasthenia Day hospital Clinic and the Clinic for disorders of the peripheral nervous system. Member of brain death ascertainment The OA-AUSLTNO has an active Day Service, which offers an alternative to Daily Hospital, especially for diagnostic activities. Day Service aimed at managing clinical cases whose solution requires the provision of clinical and instrumental investigations multiple and multidisciplinary, even complex, provided by a specific therapeutic diagnostic pathway on the clinical problem of the patient and not on the individual performance. Dr. Xxxxxxxx Xxxxxxxx, which is the Nursing Manager for integrated PDTA Hospital/Territory gained a lot of experience in management of chronic diseases, in particular about heart failure (see RACE project). • Azienda Ospedaliero Universitaria - Careggi (AOUC) The AOUC: o is a public juridical entity and is endowed with entrepreneurial, organizational, administrative, patrimonial, accounting, managerial and technical autonomy; o it is integrated with the University of Florence and it is characterized by hospitalization, specialist outpatient services and emergency-urgency activities; o it aims the development of highly specialized activities as reference for Area Vasta, at regional and national level; o it carries out unitary and inseparable functions of assistance, teaching and research, constituting a structural element of the NHS, in particular of the SSR, and of the University System; o it pursues the development of highly specialized activities and fosters innovation in the organizational and clinical-assistance fields, also through the introduction of cutting-edge technologies for diagnosis, treatment and translational research. In 2017, the AOUC had 5’165 hospital staff, 276 university staff, and 88 collaborators. The fundamental purpose of the company and the justification of its existence consist in achieving the highest level of response to the demand for health, defined as recovery and maintenance of physical, mental and social health, in a process that inseparably includes the teaching, as a tool for building and improving the skills of operators and subjects in training and research, aimed at the continuous progress of clinical and biomedical knowledge. To achieve this goal all the scientific and clinical competences of the university departments of the biomedical area and those of the Health Service contribute, as well as the didactic activities coordinated by the School of Human Health Sciences. The AOUC contributes to the development and achievement of the mission declared by SSR. It identify as constitutive elements of the mission the following principles: • to pursue innovation in health, as a process of creating new organizational, technological and productive tools, or modifying existing ones, able to guarantee an appropriate |
response to the growing demand for health and wellbeing of citizens; • to promote innovative organizational solutions also through Project Management tools; • to affirm research, both basic and applied, as an indispensable tool for the development of scientific knowledge and the training of new professionals; • to support organizational and management innovation as a value element of the company system as a whole; • to guarantee the development of activities aimed at the study of biodiversity and research in the pharmaceutical field; • to implement organizational models capable of affirming the principles of personalized medicine, as an innovative therapeutic modality that makes possible, even through pharmacogenomic analysis, the personalization of the therapeutic strategy; • to enhance gender medicine as an area of care practice that applies the concept of "gender diversity" to guarantee the best treatment for all, men or women, according to gender specificity; • to give appropriate answers, in the care path, to the needs of knowledge and skills related to the different educational objectives of the School of Human Health Sciences and related university departments and to develop appropriate training courses built on the centrality of the trained subject; • to develop and enhance the educational offer with particular regard to the Continuing Medical Education program (E.C.M.); • to seek the clinical and organizational appropriateness of the services, in compliance with the universalistic principles of care and in compliance with the principle of equity in accessibility to healthcare services; • to operate within a path, through participation in the networks of the Metropolitan Area, the Area Vasta and the Regionals, acting as a reference point for the various pathology networks that emerge from the reference context; • to pursue, in the most effective way, the absence of pain in the care phases to protect the quality of life and the dignity of the person; • to xxxxxx a relationship with citizens based on transparency and respect for the protection of the confidentiality of information on personal data; • to highlight and strengthen communication processes for citizens, trainees and operators, in order to increase the sharing of ethical values and business objectives, also activating experimental paths with the media world; • to disseminate information among its users, even of a bioethical nature, in order to favor decision-making autonomy; • to enhance the role of all professionals in the clinical governance of the company; • to promote the comparison with local authorities, the trade unions of the SSR and the University, associations for the protection of citizens and users, voluntary associations; • to guarantee the achievement and maintenance of organizational and professional standards promoted by scientific societies, standards and laws on quality and safety of care; • to guide towards the continuous improvement of the services offered to users, encouraging their full satisfaction in the field of diagnostic, therapeutic and care pathways. Clinica Neurologica I The Clinica Neurologica 1 is directed by xxxx. Xxxxxx Xxxxx and includes a team of clinicians that |
treat the chronic, inflammatory, and degenerative disease, and with complex diagnosis of the nervous system with high care intensity and experimental treatments, in particular: dementia, multiple sclerosis, neuromuscular diseases, rare neurological diseases, movement disorders, and Parkinson's disease. The activity are performed as ordinary hospitalization, Day Hospital, outpatient and counseling. The laboratory activities include: • Neurosonology: ecocolor doppler and transcranial, TCD diagnosis of right/left shunt, TCD assessment in barbiturate coma; • Neuroimmunology: development of diagnostic methods; • Neurogenetics: genetic analysis of neurological diseases, University of Excellence member of the DENOTHE University; • Neuropsychology: extended psychometric evaluations. The main investigator involved in the project, Dr. Xxxxxx Xxxxx works as Neurologist at AOU Careggi in Florence. Responsible of Outpatient Service for Parkinson’s Disease at Dipartimento Neuromuscoloscheletrico e degli Organi di Senso of AOU Careggi. Her main interest is related to Parkinson’s Disease and Parkinsonisms, in particular study of biomarkers, diagnostic methods, neuropsychological and psychiatric aspects and neurosurgical treatment. • Azienda USL Toscana Centro (AUSLTC) The AUSLTC includes the ex AUSL of Firenze, Empoli, Prato, and Pistoia. The company with an area of 5000 km2 and 1’500’000 of assisted workers has over 14’000 employees, 13 hospitals, 220 territorial structures, 9 district areas, 8 Società della Salute. The AUSLTC inherits and develops the positive experience of Empoli, Florence, Pistoia and Prato ASL, at the service of all people and to protect their health, engaging with passion and responsibility to ensure and improve the quality of life and individual well-being of his clients, through a global, personalized, safe and evidence-based offered care. The principal elements of the AUSLTC are the human, moral and technical qualities of its professionals, with a constant tension towards the enhancement of the excellences already developed within the individual pre-existing ASL and brought back into the new organizational dimension. The AUSLTC aims at creating and managing an integrated network of health services for prevention, treatment and rehabilitation and a network of social and health services in hospitals, outpatient and home. The challenge is the territorial dimension of the Area Vasta Centro with the aim of making the offered service uniform and equally accessible, through a unique and consistent reading of the needs of health, ensuring appropriate answers on multiple levels of complexity, always attentive to the peculiarities and local problems. Ospedale Santa Xxxxx Xxxxxxxxxx (OSMA) The OSMA is a public hospital located in Bagno a Ripoli. It was built in the late 60s, becoming full operative in the early 80s. Alongside the Hospital, in 1996 another facility was built, mainly used for administrative and territorial activities. The central and the base of the helicopter are available. The OSMA is the main hospital of the Hospital Presidium Florence II, which also includes the Borgo San Xxxxxxx Hospital and the Figline Valdarno Hospital. It has a wide catchment area that |
goes from the District 2 of Florence to the whole area of Chianti, Val di Pesa and Valdarno, in addition to the Serristori Hospital of Figline Valdarno. To date, the OSMA is increasingly establishing itself as a hospital strongly focused on oncology, with the increasingly structured development of the Senology and the creation of the corporate Breast Unit according to regional directives. It is the Regional Reference Center for Melanoma; the recent regional resolution also identifies the OSMA as the site of the Melanoma and SKIN Cancer Unit of the Area Vasta. Moreover, OSMA has an important activity of Interventional Cardiology, in fact it is a center for the pathways of primary angioplasty in the overlapped ST myocardial infarction. For some years it has been recognized by the WHO as "Child Friendly Hospital" and since 2013 has also obtained the recognition of two "pink stamps" from the National Women's Health Observatory for the diagnostic-therapeutic pathways and services dedicated to female pathologies. The main investigator for OSMA-AUSLTC is Dr. Xxxxx Xxxxx. She has been involved in the diagnosis and treatment of Parkinson's disease for over 30 years with focus on the screening and on the advanced stages of the disease. She is co-founder and since than reference physician of the Florence Parkinsonian Association and, at present, she is group leader of the Parkinson and Movement Disorders Team in the Neurology department SOS of the Santa Xxxxx Xxxxxxxxxx Hospital in Florence. XxxX participated to several clinical pharmacological trials and epidemiological studies (i.e., the National CNR ILSA study) and in research projects dedicated to the study and identification of Parkinson's disease preclinical markers as for example the validation of olfactory tests [30]. Currently she is Principal Investigator at the DYsCover study (Dyskinesia COmparative Interventional Trial on DUODOPA vs oral medication) an international study involving European and non-European countries. • Consiglio Nazionale delle Ricerche (CNR) – Istituto di Fisiologia Clinica (IFC) The Institute of Clinical Physiology (IFC) is the largest biomedical research institute in the clinical field of National Research Council (CNR). The focus of research in IFC is on etiopathogenesis, diagnostics and therapy of cardio-pulmonary diseases. The Institute's activities can be well defined as the synergy of four main areas of interest: Clinical Pathophysiology, Experimental Medicine, Molecular and Cellular, Techno-sciences (e-health, nanomaterials, biotechnology) and epidemiology (clinical, environmental, social and molecular). Within the epidemiology field, the involved unit carries out research on models and statistical methods in medicine. The team responsible for the activities has epidemiological and biostatistical competences matured in the clinical field, attested by publications in peer-reviewed scientific journals. The main investigator for IFC-CNR is Xxxxxxx Xxxxxxxxx (CNR Researcher, Adjunct professor, scientific-disciplinary profile Medical Statistics (ssd MED/01) who participated as a biostatistician at research on the pathology of Parkinson's and other diseases, with consequent publications in relevant international journals. CNR: Advanced biostatistical competences allowed the partnership to international, national, regional and local research projects as experts in epidemiology and biostatistics. • Experience already gained in carrying out similar projects Each partner of OLIMPIA is already linked with research and development networks both National and International, since they had been already involved in Regional, National, and European |
proposals. In particular, all the partners of OLIMPIA were already involved in the clinical experimentation of XXXXXX project, whose clinical protocol was named “CASANOVA-PD Study: Validation of ergonomiC superlight weArable Sensors for AN ObjectiVe Assessment of upper and lower limbs movement and gait in Parkinson's Disease: a cross-sectional controlled, open-label, pilot Study”. Such study, with the scientific supervision of IBR-SSSA and the clinical coordination of OA- AUSLTNO was fundamental to lie the basis for the OLIMPIA project, which reinforces the pre- existing collaboration and cooperation. Additionally to XXXXXX, each partner was involved in other relevant project as follows: • IBR-SSSA: - Xxxxx.Xxxx - AmbienT Response to Avoid Negative Stress and enhance SAFEty (Jul 2014- Jun 2017) xxx.xxxxxxxxx.xx . The project is an EU Project funded in the AAL-Joint Program, Call 6 (AAL-6-2013-64) and aims at developing a system to support senior workers who can and wish to stay longer in a job position with high personal and public safety risks. In particular the project has is focus in the transportation sector where there is a potential for high physiological stress, such as truck drivers, train drivers and transport control room personnel. Xxxxx.Xxxx will deploy environmental monitoring, physiological monitoring and movement monitoring in order to develop countermeasures to reduce the negative stress. - Robot-Era - Implementation and integration of advanced Robotic systems and intelligent environments in real scenarios for ageing population (Xxx 2012 – Dec 2015), www.robot- xxx.xx. This project is a Large Scale Integrated Project in the Challenge 5.4 of the FP7 program. Its objective is to develop, implement and demonstrate the general feasibility, scientific/technical effectiveness and social/legal plausibility and acceptability by end-users of a plurality of complete advanced robotic services, integrated in intelligent environments, which will actively work in real conditions and cooperate with real people and between them to favour independent living, improve the quality of life and the efficiency of care for elderly people. - ACCRA - The mission of ACCRA (Dec 2016 – Dec 2019) is to enable the development of advanced ICT Robotics based solutions for extending active and healthy ageing in daily life by defining, developing and demonstrating an agile co-creation development process. To this end, a four-step methodology (need study, co-creation, experimentation, sustainability analysis) will be defined and applied in three applications (support for walking, housework, conversation rehabilitation) and assessed in France, Italy, Netherlands and Japan. ACCRA is a joint European-Japanese initiative including a multidisciplinary team of 6 European partners and 3 Japanese partners. www.accra- xxxxxxx.xxx/xx/xxxxxx-xxxx/ • IFC-CNR: - the local project IPMP-MS (Iposmia Idiopatica – Malattia di Parkinson’s nella provincia di MaSsa Carrara), a longitudinal study on relationship between idiopathic hyposmia and Parkinson’s disease (see par. 7.4); - the National project E-MIOT (Extension-Myocardial Iron Overload in Thalassemia) project, regarding the prognostic factors related to thalassemia. • OA-AUSLTNO: - the local project IPMP-MS (Iposmia Idiopatica – Malattia di Parkinson’s nella provincia di |
XxXxx Xxxxxxx), a longitudinal study on relationship between idiopathic hyposmia and Parkinson’s disease (see par. 7.4); - RACE – Study and technical validation of the effectiveness of a tele-home-monitoring system for patients affected by chronic heart failure. • OSMA-AUSLTC: - A Study Comparing Efficacy of Levodopa-Carbidopa Intestinal Gel/Carbidopa-Levodopa Enteral Suspension and Optimized Medical Treatment on Dyskinesia in Subjects With Advanced Parkinson's Disease (DYSCOVER). • Technical and scientific qualification (adequacy and complementarity of the competences involved) of the research groups with particular reference to the project proposal; The Consortium is well balanced between high-quality technical-scientific ORs (IBR-SSSA and IFC-CNR) and clinical ORs (OA-AUSLTNO, AOUC, OSMA-AUSLTC) with large experience with the Parkinson’s Disease. Also the external participants are in turn well balanced, with two high- quality technical-scientific ORs (TUKE and IWT), and a clinical OR of the Italian NHS (UNIPA). The groups are complementary for their background and know-how, and their expertise will be fundamental for the success of the project. In particular IBR-SSSA will lead the project, managing the administrative and financial issue, as well as, actively contributing in each other Operative Objective. Thanks to the research and knowledge in artificial intelligence and development of ICT system, IBR-SSSA mainly contribute in OO3 and OO4, to address the scientific/technical challenges proposed in OLIMPIA. TUKE and IWT, on the basis of their own background, could contribute respectively in OO3 and OO4, providing support and consulting about Cloud computing (TUKE) and Artificial Intelligence (IWT). The clinical ORs will be fundamental for achieving the clinical challenges because they will offer their knowledge and facilities to recruit, test and assess the subjects involved in the project. Starting from their experience the requirements, clinical protocol and methodology will be definitively consolidated to identify an homogeneous approach for diagnosis and treatment of PD. Finally, IFC-CNR will make available biostatistics knowledge to analyse data acquired during the experimentation and provide the final evaluation of the OLIMPIA project. • Facilities, equipment and resources available for the project; The clinical partners (OA-AUSLTNO, AOUC, OSMA-AUSLTC) will make available for clinical experimentation their facilities, where patients and other subjects involved in the study will be subjected to clinical examination and to the acquisition of the motor pattern through the OLIMPIA system. The OA-AUSLTNO will provide the Day Service to integrate in it also the PDTA for PD patients and IH subjects who need for further investigation for suspected PD onset. Following, also the other clinical ORs will implement an analogous service. • Connection with national and international research and development networks. The O.U. Neurology of OA-AUSLTNO, AOUC, OSMA-AUSLTC, as well as the UNIPA are part of an Italian network of Neurology that has been constituted and consolidated during previous experiences. The IBR-SSSA, with its scientific coordinator Dr. Xxxxxxx Xxxxxxx, is active member of the Italian Association of Ambient Assisted Living (AitAAL), which aims to xxxxxx inter and multidisciplinary activities in research and business, promoting the cross fertilization between research centers, |
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industry, policy makers and healthcare service providers. AitAAL is also involved in the National Cluster of Tecnologie per ambienti di Vita (TAV), promoting road-mapping and lobbing activities in conjunction with MIUR. The Italian Ambient Assisted Living Forum is one of the main event of the Association. Moreover, IBR-SSSA has already established collaboration with Prof. Xxxxx Xxxxxx, director of the Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, TU Košice, Slovakia, and with Xxxx. Xxxxx Xxxxxxx, head of Social Robotics Lab of Kyushu Institute of Technology, Japan. | ||||||
D) Technical validity and economic viability of the project: The OLIMPIA Proposal is a unique opportunity to address different innovation challenges by scientific, technological, clinical and social points of view. In particular OLIMPIA aims to radically change the clinical and scientific approach to PD through an innovative methodology of early diagnosis made possible by the development of a complete portfolio of new technological devices. The clinical/scientific innovation of OLIMPIA (Table 4) certainly resides in the medical advantages deriving from the possibility to objectively and quantitative measure the motor performance of the upper and lower limbs in PD patients and in those at risk for developing the disease. The proprieties of measure of the devices based on inertial sensors allow to detect in a very detailed manner the values of, among others, amplitude, speed, frequency and variability, providing a reliable tool in support to neurologists for the evaluation of the biomechanical tests to both early diagnose PD or determine its stage. The designed instrumentation allows to identify fine changes in motor performance (not visible to the eye) projecting the diagnosis of the disease towards a pre-motor phase (about 5-7 years before) unidentifiable with current diagnostic techniques. The early administration of the neuro- protective therapies would allow to delay the onset of the disease and its characteristic symptoms, widely disabling for patients, as well as to slowdown the progression of the disease, with a reduction the number of patients in the more severe HY stages associated with poor quality of life and need of constant care and assistance. Table 4. Clinical/Scientific Innovation of OLIMPIA | ||||||
Innovation | State of the art | Critical aspects | Activities to go beyond the state of the art | Measures of innovation | ||
Validation of an innovative diagnosis and treatment methodology for PD assessment. | - Diagnosis based on the neurologist visual assessment related to the MDS- UPDRS scale. | - Capability of the instrumentation in distinguish between healthy and PD patients. - Definition of metrics of assessment based on normative data. | - Design of a valid clinical trial following a GCP approach. - Recruitment of a statistically significant sample size of both healthy and PD patients. | - Repeatability of measures intra- operator and inter- operator with specificity higher than 95% in distinguish healthy and PD patients. - Identification of normative thresholds. | ||
Formulation of an innovative PD early detection methodology. | - Absence of a standard practice or procedure of PD early diagnosis. | - Recruitment of a significant sample size of IH subjects. - Identification of IH subjects who will develop PD during | - Extension of the collaboration with the IPMP-MS project to both recruit a statistically significant number | - Identification of IH subjects who show a worsening in motor performance over time predicted by the instrumentation (at | ||
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the project. | of IH subjects and identify pilot cases. | least 5 patients). | ||||
The objective measure of the motor exercises proposed in the scale MDS-UPDRS III and the opportunity to store the results of each test performed in a database, could induce a significant reduction of the variability that afflicts the observations of neurological visits (i.e. inter-operator and intra-operator). The valence of the proposal is remarkable because the OLIMPIA system aims to define a new clinical methodology of early diagnosis and management of PD including home care (Table 5). Table 5. Social innovative aspects of OLIMPIA Research in PD is often characterized by small clinical trials and subjective data, difficult to share between researchers. Analytics platforms to detect patterns in data collected and to deliver accurate predictive models could be very useful to objectively monitor the broad array of symptoms (motor and non-motor) characterizing PD. The Cloud has already proven its multiple benefits to the business world and also the medical sector can obtain several advantages from this technology as well as the PD management. The most common approach is leveraging sensing technology to automatically evaluate the | ||||||
Innovation | State of the art | Critical aspects | Activities to go beyond the state of the art | Measures of innovation |
Continuous monitoring of social impacts (acceptance, QoL) with quantitative and qualitative methods of assessment. | - Social impact assessment typically conducted after relatively short- term experimentations by applying cross- sectional methodology which provides only the snapshots of the social impacts of new technology and services. | - Designing a questionnaire targeted for the social needs of PD patients and stakeholders. - Engaging participants in repeated activities over a long period of time. | - In addition to ordinary accessibility and usability indicators (e.g. UTAUT), the impacts on subjects’ overall QoL and social activity will be measured repeatedly to create a more comprehensive picture of the social impacts of this new technology. | - The impacts of the continuous monitoring will be assessed at the end of the project by determining its direct and indirect impacts on the design of the final product. |
Analyzing the social impacts of the OLIMPIA system in a real home environment by observing the use of devices and services. | - In current projects social impacts have been predominantly studied under a controlled laboratory environment, which is cleaned from the complex social dynamics and interactions of real environments. | - Gaining access to PD patients’s homes and keeping non- participant observation unobtrusive for both patients and their family. | - Unobtrusive observations conducted in real life settings. - Study of the use of OLIMPIA system, its integration into daily social interactions and its effects on PD patients social activity and life satisfaction. | - Determining what factors promote and hinder the successful integration of the OLIMPIA technology and services as a “third party” in daily H2H communication interactions. |
performance of specific motor tasks and collect data locally or on a remote server. Usually these systems have to be used in a controlled environment, typically at home. Actually a recent review shows a growing interest in Cloud Computing for Healthcare even if currently only few successfully implementations exist and many issues on data safety and security are still to be solved.
Table 6. Technological innovation and progress of OLIMPIA beyond the state of the art
Innovation | State of the art | Critical aspects | Activities to go beyond the state of the art | Measures of innovation |
Development of an extensible Cloud Platform for clinical data management. | - Current Data Management platforms lack in supporting future extension. | - Facilitate future platform extension supporting integration of other data sources such as new sensors. | - Support in possible future extension keeping isolation from exchanged data formats and related semantics. - Integration of new sensors and/or data semantic extension (ontology models). | - Production of full prototypes (TRL6). - Acceptability and usability assessment from all stakeholders and preliminary certification assessment (TRL7). |
Creation of a comprehensive clinical database on Parkinson symptoms and therapy for research. | - Technology for data mining and Cloud computing rarely use in the clinical field. - Lack of appropriate knowledge base data for research purposes. | - Research and clinical investigation are often slowed down by a prolonged period required to get enough data to work on. | - Sharing of gathered data among research group. - Fast growing knowledge base for clinical investigation. | - Production of full prototypes (TRL6). - Acceptability and usability assessment from all stakeholders and preliminary certification assessment (TRL7). |
• Economic viability: consistency between costs and expected results and sustainability.
The expected impacts of XXXXXXX proposal can be summarized in the following items, according to the different phases in which the technological and service solutions are employed in Parkinson management.
• Clinical pilots will be conducted, providing evidence that XXXXXXX can slow down the progression of PD and detect risk for early intervention;
• Testing will provide evidence on the effectiveness of XXXXXXX proposal to provide tailored interventions including therapy and assistance;
• The project will prove that the concept of diagnosing and managing PD with an innovative and advanced ICT solution based on wearable sensors, advanced interfaces and cloud technologies is cost-effective for healthcare systems and improves the PD patients's QoL and their carers; • The involvement of stakeholders from the value chain will provide a multidisciplinary approach critical to the management of PD. • Stakeholders from the value chain (patients, carers, primary and secondary healthcare, component and hardware manufacturers) will be consulted and involved in the development of the solution at all stages, ensuring that commercial and end-user requirements are at the core of the end product. • Lack of gold standard and test for PD means that currently healthcare practitioners do not have access to an objective assessment tool for the diagnosis of PD, leading to an impressively high percentage of misdiagnosis. OLIMPIA will increase the number of people that receive a correct diagnosis meaning that a greater number of people can be treated for PD, thereby slowing down the worsening of their condition. • PD patients will be able as a result to maintain independent living, employability and continue with daily activities. • Improve the effectiveness of drug treatment by avoiding trial and error dosage. • Early risk detection will be made possible through better knowledge of the condition. • Reduce the cost of PD diagnosis and management for healthcare systems in Europe. The clinical expected impacts have a socio-economic counterpart expressed in a saving of the health resources with a net benefit both from the healthcare system’ (NHS) and PD patients's perspectives, guaranteeing a quantitative and qualitative improvement of the efficacy and efficiency of care by means of the OLIMPIA remote system. With respect to the public payer perspective it is expected the reduction of costs related to: • visits, hospitalization and of the rehabilitative procedures; • human resources employed in the management of PD thanks to personalized therapy and greater adherence; • early working retirement; • assistance in the severe stages of the disease; • SPECT-DATSCAN exams thanks to the early diagnosis methodology. With respect to the patients' and their families' perspective it is expected: • the reduction of the direct (i.e. public or private transports) and indirect (reduction of the productivity) costs; • the reduction of the costs linked to the neuropsychological assistance; • the self-empowerment of the PD patients in the management of the disease through the awareness of the same; • the reduction of the psycho-physical stress and of the cases of depression both of patients and families; • the availability of useful suggestion about public events on PD, dietary habits and healthy lifestyles, which may contribute to patient’s social activity, sense of belonging and QoL. With respect to the clinical staff perspective: |
• greater effectiveness and efficiency of the care service with a larger number of patients daily assisted; • more information about the disease with the concrete possibility of therapy adaptation; • greater availability and timeliness regarding feedbacks about patients' health status; • reduction of erroneous diagnosis of the disease in the early phases. In OLIMPIA it is expected a net costs reduction regarding the management of PD through the provision of a complete technological kit (wearable devices, smart end-users' interfaces and Cloud service) (Figure 7) both to health districts and patients within a service of remote care. The estimated costs of the kit are about 4000€. Figure 7. The OLIMPIA preliminary technological kit. The early diagnosis methodology and the home monitoring scenario are expected to lead both to a reduction of costs and to an optimization of the human resource management, taking in account the current Italian tariffs and costs of PD. A preliminary investigation on the Tuscany Region highlights that each neurologist performs an averaged number of visits per day of 6; the duration of each visit is approximately 30min and its cost is about 31.5€ per visit. A possible innovation in the procedures is the use of three kits of the system, that could be used by an equipe composed of a neurologist and 2 physicians. With the same consideration done before, this table clearly says that there is a save of 3€ in this scenario (Figure 8), that could become an important value if multiplied for the number of Parkinson patients in Tuscany. Of course, this is just a simple example; more investigations will be performed during the project is funded. Additionally, our research could also demonstrate the feasibility of early diagnosis, favouring prevention with a reduction of up to 5% of PD patients in HY4/5 and 750k€/year only in Tuscany for example. Figure 8. Expected Impact of OLIMPIA care model on the current SSR for PD care The introduction of home monitoring scenarios, i.e. telemedicine remote services at home, is here intended to not replace the visits in hospital. On the contrary the home scenario could be used to |
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better increase the number of visits and to collect a huge amount of data that, using artificial intelligence techniques, could improve the diagnosis and interventions capacities. |
E) Ability of the project to create good network relationships: Describe the project's ability to create good network relationships through: • sharing and exploitation of technological infrastructures, such as integrated organisational and research platforms (also with Clusters identified by the Tuscany Region and regional networks) The OLIMPIA service model had been designed to be integrated in the already existing SSR. As a matter of fact, the proposed model is based on: • the use of innovative wearable sensors: The sensors used in OLIMPIA are low cost and accurate and they require only a user-friendly interface and a computer to be used;, • the Day Service, a tool already adopted in different hospitals to manage the chronic disease patients; • a cloud based software, which will be developed to be fully interoperable with the TIX, currently used by the AUSL. The OLIMPIA will also join the Rete Telematica Regionale Toscana (RTRT) to allow a real deployment within the Tuscany healthcare services. Moreover, in the future, in the OLIMPIA model could be included the possibility to use the FSE to use its advantages for the outcome of the assessment made with the OLIMPIA system. The composition of the Consortium will also xxxxxx the technology transfer between the research organization and the SSR. It is composed of two research organization and three clinical partner that are part of the SSR, so, the technology transfer will begin from the first phases of the project when the OLIMPIA system will start to be used during the examinations, since the TRL of the OLIMPIA sensors is already high, thus ready to be deployed. • scientific cooperation with national and international bodies (if any) The OLIMPIA consortium can count on the competencies of the external research organization such as: ▪ Intelligent Software Systems (Fujita Laboratory), Iwata Prefectural University, which is expert in artificial intelligence topics in healthcare applications and will thus supervise the research activities about the use of novel machine learning approaches that will be included in the OLIMPIA system. ▪ lntelligent technologies Department of Cybernetics and Al Faculty of EE & lnformatics, Technical University of Kosice, that, being expert in innovative cloud technologies for distributed computing, will supervise the development of the Software as a Service part; ▪ Clinica Neurologica, Dipartimento di Biomedicina Sperimentale e Neuroscienze, Cliniche, Sezione di Neurologia, University of Palermo that will supervise the clinical experimentation and validation of the OLIMPIA service. Further cooperation will come from the Italian network of neurology created within the DAPHNE project. At national and international level, the OLIMPIA project will be in contact with: ▪ European Association of Parkinson Disease and some PD associations in Tuscany (see letter of endorsement); ▪ The Italian Association of Ambient Assisted Living (AitAAL), whose SSSA is founder and active member; ▪ The European Innovation Partnership on Active and Healthy Ageing, whose the coordinator is member. OLIMPIA experience could be presented as a best practice in the |
ALLEGATO B BANDO RICERCA SALUTE 2018 |
use of MAFEIP tools. |
F) Relevance of the project assessed in terms of: • coherence with regional sectoral policies The OLIMPIA proposal aims to develop a service model where the PD patient is placed at the center of the care process involving all the stakeholders of the healthcare system, from the GP to the specialists to the local services and formal and informal caregivers. The OLIMPIA service will integrate the use of low cost wearable sensors that can help the neurologists to objectively diagnose and monitor the progress of the PD, from the preclinical to the clinical phase, so to intervene promptly with the pharmacological therapy when there are subtle variation in the motion pattern. The service model will include also a cloud infrastructure, where all the data about the patient can be uploaded and where the parameters extraction and machine learning algorithms will evaluate information useful to the specialists to better follow the patient. The OLIMPIA proposal is therefore in line with the actual healthcare policies. According to the “Piano Sanitario e Sociale Integrato Regionale 2012-2015” and the “Piano Nazionale delle Cronicità”, it is important to develop a model of assistance that is focused on the integration of all the stakeholders of the healthcare system, from the patient to the specialists, to the GP and the local services. The patient has to be at the center of the model and has to be involved in the management of his/her own health. According to this assumption, the OLIMPIA service aims to place the PD patient at the center of the care process, trying also to intervene before the manifestation of the motor symptoms. Since the epidemiology of PD is still a matter of research [98], beyond the suggestions of maintaining a healthy lifestyle, the important issue is to diagnose the disease as soon as possible, when it is in its early stage. For this reason, one of the main point of the OLIMPIA model is the involvement of the GP as an active part of the service. An olfactory screening will be administered by the GP to find persons with idiopathic hyposmia, which is a preclinical sign of PD. In this way, the OLIMPIA project tries to xxxxxx the initiative medicine, which is an approach based on a proactive healthcare model that goes towards the citizen to maintain good health condition and delay the progress of the disease, as explained in the “Piano Sanitario Regionale 2008-2010”. According to “Piano Sanitario e Sociale Integrato Regionale 2012-2015”, the OLIMPIA service foresee different path for each level of the disease, personalizing thus the care process and enabling the patient to self-manage the disease, also thanks to the take in charge by the Day Service as mean of actualization of the different diagnostic and therapeutic path. The take in charge by the Day Service will guarantee to the patient an easy access to an integrated multidisciplinary team that will follow him/her during the disease. This team, further from specialists, will include all the stakeholders, including the GP, which will be a reference point for the patient. The OLIMPIA service will be based, beyond the aforementioned points, on the use of low cost inertial wearable sensors to have an accurate evaluation of the motor pattern and thus an objective diagnosis of PD. Through the OLIMPIA project the use of these sensors in the diagnosis and monitoring of PD patients will be further validated so that they could be used in the future as a low cost tool to support the medical assessment. These sensors are one of the fundamental part of the OLIMPIA service and will be used also at home by the patients. The wearable sensors, thanks also to an ad-hoc interface, will be an important tool for the telemedicine service included in OLIMPIA. These easy-to-use and low cost tools will be used at home by the patient to simplify the follow up, improving the continuity of care thus to allow the healthcare team to have more |
frequent measurement of the motor pattern and promptly evaluate any subtle change in the motor performance. As fostered by the “Piano Sanitario e Sociale Integrato Regionale 2012-2015”, the telemedicine should be an extension of the clinical medicine and thanks to the OLIMPIA system it will be possible to have a more frequent monitoring of the patient that will consequently allow to better personalize the pharmacological therapy and the therapeutic path. As suggested by the “Piano Sanitario e Sociale Integrato Regionale 2012-2015” and described in the previous section of this proposal, the use of the sensors at home is foreseen only for some PD patients, according to a diversification of the care path in line with the level of disease. Another key point of the OLIMPIA service is the use of a cloud infrastructure where all the data about patients can be stored and elaborated to extract significant parameters that will be used by the specialists to objectively diagnose and monitor the patient and that could be available also to the patient. Indeed, one of the objective of the OLIMPIA proposal is to build a Software as a Service (SaaS) solution fully interoperable with the Tuscany Internet Exchange (TIX) datacenter, thus enhancing the connectivity and the accessibility to the information by all the stakeholders, improving the management process, and making easier for the patients to access their own data, as promoted by the “Piano Sanitario e Sociale Integrato Regionale 2012-2015”. Moreover the information about the patients could be included in the Fascicolo Sanitario Elettronico in order to improve the quality of the assistance and of the care. • Consistency with the purpose of the Call The OLIMPIA proposal aims to implement and test a new service model that will introduce the use of innovative and low cost technologies in the Day Service and at home to improve the diagnostic and therapeutic path of PD patients. Wearable sensors will be used to measure the motor performance of PD patients, allowing an objective and accurate diagnosis and monitoring. Furthermore, the combination of the OLIMPIA sensors with an olfactory screening will be further investigated to identify PD patients in the preclinical phase, when motor symptoms are not already evident. The early diagnosis will make it possible to begin promptly the pharmacological therapy, in order to slow down the progress of the disease and improve the care process. The wearable sensors will be integrated in specific diagnostic and therapeutic path that will be carried out in the Day Service, which has already been demonstrated to be a good institution for the management of chronic diseases. The use of such sensors even at home will allow to further improve the diagnostic and therapeutic path thanks to a more continuous monitoring of the patients. The OLIMPIA model foresees also the involvement of all the stakeholders of the healthcare system, from the specialists to the GP, to the local services and formal and informal caregivers. The collaboration among all these actors permits to create a net around the patient that will help him/her to follow the diagnostic and therapeutic path and to be able to manage the disease by him/herself. In addition, the OLIMPIA model is based on a cloud infrastructure that will allow the storage and evaluation of the data acquired with the sensors to obtain significant parameters for the motion assessment of PD patients. These data will be available to all the stakeholders of the healthcare process, strengthen the collaboration among them and improving at the same time the care process around the patient. The OLIMPIA proposal presents therefore a new service model that can adapt easily to the already existing structures improving at the same time the management of the PD by the patient and the diagnostic and therapeutic process thanks to innovative technologies, a cloud infrastructure, good existing services. |
• Potential transferability and spillover to the Regional Healthcare System (SSR) As described in the previous paragraph, the OLIMPIA service model is based on the introduction of innovative technologies for the PD diagnosis and monitoring in already existing infrastructure, i.e. Day Service, or at home. In the last years, several hospitals have already organized a the Day Service infrastructure for the management of chronic diseases, so the it will be easy to transfer the OLIMPIA service model. Moreover, since the OLIMPIA wearable sensors are low cost and easy to use, like the olfactory screening, the adoption of such kit in the Regional Healthcare System (SSR) will not be onerous. Additionally, for real deployment within the Tuscany healthcare services at the end of the project, the OLIMPIA will join the Rete Telematica Regionale Toscana (RTRT) and thanks to the interoperability of the SaS developed with the TIX, the use of such model will be easily accessible and shareable. • Patient and association engagement The OLIMPIA Consortium is already in touch with the Associazione Italiana Parkinsoniani (AIP) onlus, section of Florence coordinated by Xxxxxxxx Xxxxxx (xxxxx://xxxxxxxxxx.xxxxx.xxx/). This association was funded in 1993 and its aim is, among others, to improve the QoL of patients. This association is made of around 25000 PD patient’s families around Italy. The association will be involved by keeping them updated about the progress of OLIMPIA. Moreover, the PD patients related to the section of Florence, can take part to the experimentation with the wearable sensors to have the possibility to be monitored with an objective and accurate tool and to better manage the therapeutic path. Finally, the patients and their families will be involved in the first phases of the project for the evaluation of the acceptability and the usability of the OLIMPIA system. |