Emergency Room Visit Data Editing and Imputation Sample Clauses

Emergency Room Visit Data Editing and Imputation. Facility expenditures for emergency room services were developed in a sequence of logical edits and imputations. “Household” edits were applied to sources and amounts of payment for all events reported by HC respondents. “MPC” edits were applied to provider-reported sources and amounts of payment for records matched to household-reported events. Both sets of edits were used to correct obvious errors in the reporting of expenditures. After the data from each source were edited, a decision was made as to whether household- or MPC-reported information would be used in the final editing and predictive mean matching imputations for missing expenditures. The general rule was that MPC data would be used where a household-reported event corresponded to an MPC-reported event (i.e., a matched event), since providers usually have more complete and accurate data on sources and amounts of payment than households. One of the more important edits separated flat fee events from simple events. This edit was necessary because groups of events covered by a flat fee (i.e., a flat fee bundle) were edited and imputed separately from individual events covered by a single charge (i.e., simple events). Most emergency room events were imputed as simple events because hospital facility charges are rarely bundled with other events. (See Section 2.5.5 for more details on flat fee groups). However, some emergency room visits were treated as free events because the person was admitted to a hospital through its emergency room. In these cases, emergency room charges are included in the charge for an inpatient hospital stay. Logical edits also were used to sort each event into a specific category for the imputations. Events with complete expenditures were flagged as potential donors for the predictive mean matching imputations, while events with missing expenditure data were assigned to various recipient categories. Each event with missing expenditure data was assigned to a recipient category based on the extent of its missing charge and expenditure data. For example, an event with a known total charge but no expenditure information was assigned to one category, while an event with a known total charge and partial expenditure information was assigned to a different category. Similarly, events without a known total charge and no or partial expenditure information were assigned to various recipient categories. The logical edits produced eight recipient categories in which all events had a co...