Validating the Matching of Patients in the Linkage of a Large Hospital System's EHR with State and National Death Databases.

BACKGROUND: Though electronic health record (EHR) data have been linked to national and state death registries, such linkages have rarely been validated for an entire hospital system's EHR. OBJECTIVES: The aim of the study is to validate West Virginia University Medicine's (WVU Medicine) linkage of its EHR to three external death registries: the Social Security Death Masterfile (SSDMF), the national death index (NDI), the West Virginia Department of Health and Human Resources (DHHR). METHODS: Probabilistic matching was used to link patients to NDI and deterministic matching for the SSDMF and DHHR vital statistics records (WVDMF). In subanalysis, we used deaths recorded in Epic (n = 30,217) to further validate a subset of deaths captured by the SSDMF, NDI, and WVDMF. RESULTS: Of the deaths captured by the SSDMF, 59.8 and 68.5% were captured by NDI and WVDMF, respectively; for deaths captured by NDI this co-capture rate was 80 and 78%, respectively, for the SSDMF and WVDMF. Kappa statistics were strongest for NDI and WVDMF (61.2%) and NDI and SSDMF (60.6%) and weakest for SSDMF and WVDMF (27.9%). Of deaths recorded in Epic, 84.3, 85.5, and 84.4% were captured by SSDMF, NDI, and WVDMF, respectively. Less than 2% of patients' deaths recorded in Epic were not found in any of the death registries. Finally, approximately 0.2% of "decedents" in any death registry re-emerged in Epic at least 6 months after their death date, a very small percentage and thus further validating the linkages. CONCLUSION: NDI had greatest validity in capturing deaths in our EHR. As a similar, though slightly less capture and agreement rate in identifying deaths is observed for SSDMF and state vital statistics records, these registries may be reasonable alternatives to NDI for research and quality assurance studies utilizing entire EHRs from large hospital systems. Investigators should also be aware that there will be a very tiny fraction of "dead" patients re-emerging in the EHR.

Validating the extract, transform, load process used to populate a large clinical research database.

BACKGROUND: Informaticians at any institution that are developing clinical research support infrastructure are tasked with populating research databases with data extracted and transformed from their institution's operational databases, such as electronic health records (EHRs). These data must be properly extracted from these source systems, transformed into a standard data structure, and then loaded into the data warehouse while maintaining the integrity of these data. We validated the correctness of the extract, load, and transform (ETL) process of the extracted data of West Virginia Clinical and Translational Science Institute's Integrated Data Repository, a clinical data warehouse that includes data extracted from two EHR systems. METHODS: Four hundred ninety-eight observations were randomly selected from the integrated data repository and compared with the two source EHR systems. RESULTS: Of the 498 observations, there were 479 concordant and 19 discordant observations. The discordant observations fell into three general categories: a) design decision differences between the IDR and source EHRs, b) timing differences, and c) user interface settings. After resolving apparent discordances, our integrated data repository was found to be 100% accurate relative to its source EHR systems. CONCLUSION: Any institution that uses a clinical data warehouse that is developed based on extraction processes from operational databases, such as EHRs, employs some form of an ETL process. As secondary use of EHR data begins to transform the research landscape, the importance of the basic validation of the extracted EHR data cannot be underestimated and should start with the validation of the extraction process itself.