Abstract
Background Healthcare data is highly complex, and considerable effort is required to create rich data resources that are reliable, user-friendly and represent valid utilization. Challenges include identifying appropriate sources, interpreting the data in a given source, matching data between sources, and transforming source data to meet desired specifications. These challenges are shared by all healthcare analytic groups, including research, finance, HEDIS reporting, care delivery and membership management. Creating partnerships for data development both within and across institutions could make the process more efficient by pooling specialized knowledge and providing opportunities to share both development strategies and data products. Objective: Within KPMAS, our goal was to facilitate development of the Virtual Data Warehouse (VDW) by tapping into existing knowledge about source data and strategies for validating and refining data. Across KP sites, our goal was to reduce the effort required to build and use the VDW by sharing code and analytic infrastructure among sites using highly similar data sources.
Methods We used a combined systems and human factors approach to identify opportunities for partnership. Within KPMAS, we invited non-research analysts with similar data needs to participate in discovery activities prior to building the VDW. As an incentive to help us find data solutions, we provided all regional analysts with access to the resulting Regional Data Warehouse. Across KP sites, we initiated and led two workgroups focused on code sharing and sharing potential infrastructure solutions to common problems.
Results We developed a decision tree that guides the developer through the process of developing relevant data partnerships. As a result of our data partnerships, we developed several shared KPMAS data products including a table that identifies voided patient encounters that should be excluded from analysis and a table that flags procedure records that do not represent valid utilization. Working across Kaiser Permanente sites, at least three sites are now using common code to produce VDW data.
Conclusions Sharing code and data products within the organization and across Kaiser Permanente sites has reduced the burden of developing the VDW, increased data quality and shared efforts to reduce costs.
