Data and Integration Sample Clauses

Data and Integration. (HIMSS, 2009) When considering these IA models, we considered the current state of information and the integration that may be needed. Currently, there is not a system that collects and aggregates this data with the level of granularity needed to perform population-based analysis for this population. This type of information is typically stored within each healthcare organization and contained within their custom electronic medical record system and/or data warehouse. If analysis is needed, data is usually generated in a report and limited to the accessible information generated from that specific organization. In the future, we want to leverage the work of existing centers by a collective interaction that allows the compilation, comparison, standardization and aggregation of granular intraoperative transplant information across multiple transplant institutions. Below is a brief description of the information’s location, and expected integration of sources.
Sample 1
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Data and Integration. (HIMSS, 2009) When considering these IA models, we considered the current state of information and the integration that may be needed. Currently, there is not a system that collects and aggregates this data with the level of granularity needed to perform population-based analysis for this population. This type of information is typically stored within each healthcare organization and contained within their custom electronic medical record system and/or data warehouse. If analysis is needed, data is usually generated in a report and limited to the accessible information generated from that specific organization. In the future, we want to leverage the work of existing centers by a collective interaction that allows the compilation, comparison, standardization and aggregation of granular intraoperative transplant information across multiple transplant institutions. Below is a brief description of the information’s location, and expected integration of sources. ❖ Location of Data: The data of interest already exists in the local EMR at each facility. However, through the development of this system, it could be requested that other elements be added to the system and/or each local EMR respectively. Each module allows customization, so formats and locations of data elements will likely vary. There will be a need to provide data mappings between applications as definitions are identified. ❖ Integration: PHI data from various sources at each center will be integrated into a shared system. As mentioned above, there will be a need to identify specifications and map elements from each source location to the ALTCDSS. For purposes of the information itself, here is a sample of relevant data elements that will span the various data types of: character, varchar, integer, numeric, date, time, and timestamp. Pre-Op Intra-Op Post-Op/Outcomes Center Cardiac Arrest (1=yes, 0=no) cardiac arrest (1=yes, 0=no) Year of Transplant Total Operative Time Intra-op death Cardiac Arrest (1=y, 0=n) Operation>8hr (1=yes, 0=no) 30-day graft failure (1=y, 0=no) Donor Type (1=D, 2=L) Case Timestamps 30-day mortality (1=y, 0=n) MELD Calculated Post Reperfusion Syndrome (1=yes, 0=no) Hospital-death (1=y, 0=no) MELD>20 (1=yes, 0=No) Severe Hypotension (<60mmHg) (1=yes, 0=no) 1-year graft failure (1=y 0=n) Redo (1=yes, 0=no) FFP units 1-year-mortality (1=y, 0=n) Age RBC units Post-op-Dialysis Gender (1=M, 0=F) PLT units Post-venti Weight (kg) Cryo units Reoperation (1=yes, 0=no) Height (cm) Fibr...
Sample 1

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