Abstract
Background The Virtual Data Warehouse (VDW) was created as a mechanism for producing comparable data across sites for purposes of proposing and conducting research. It is “virtual” in the sense that the data remain at the local sites; there is no shared multi-site physical database at a centralized data coordinating center. At the core of the VDW are a series of standardized file definitions. Content areas and data elements that are commonly required for research studies are identified, and data dictionaries are created for each of the content areas, specifying a common format for each of the elements—variable name, label, description, code values, and value labels. Local site programmers have mapped the data elements from their HMO’s data systems into this standardized set of variable definitions, names, and codes, as well as into standardized SAS file formats. This common structure of the VDW files enables a SAS analyst at one site to write one program to extract and/or analyze data at all participating sites.
Methods This poster demonstrates the wide range of data sources used at Kaiser Permanente Northern California (KPNC) to feed information into our local implementation of the VDW datasets.
Results The KPNC local implementation of the VDW contains detailed medical information on KPNC members. These files contain details on 350 million pharmacy dispensings (1997–2011), 310 million unique medical encounters (1996–2011), including 1.7 million hospitalizations, 107 million ambulatory visits, 650 million diagnoses, and 224 million procedures. We have 173 million Vital Signs observations, and 530 million lab results. The VDW Enrollment and Demographic files are derived from several historical and current membership files; the VDW Pharmacy and utilization files are derived from internal KPNC system; the VDW tumor data is derived from the KPNC SEER Registry.
Conclusions The VDW at KPNC provides an easily employed unified central repository of data from all available source files. This resource enables the sharing of compatible data in multi-site studies, and also improves programming efficiency, accuracy, and completeness for local single site studies by expending resources to link these legacy systems only once.
