验证大型医院系统的电子健康记录与州和国家死亡数据库的患者匹配。
Validating the Matching of Patients in the Linkage of a Large Hospital System's EHR with State and National Death Databases.
机构信息
Department of Community Health, University of Texas Health Science Center at Tyler, Tyler, Texas, United States.
Department of Biomedical Informatics, West Virginia Clinical and Translational Science Institute, Morgantown, West Virginia, United States.
出版信息
Appl Clin Inform. 2021 Jan;12(1):82-89. doi: 10.1055/s-0040-1722220. Epub 2021 Feb 10.
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 ( = 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.
背景
尽管电子健康记录（EHR）数据已与国家和州死亡登记处相关联，但这种关联很少针对整个医院系统的 EHR 进行验证。
目的
本研究旨在验证西弗吉尼亚大学医学部（WVU Medicine）的 EHR 与三个外部死亡登记处的关联：社会保障死亡主文件（SSDMF）、国家死亡索引（NDI）、西弗吉尼亚州卫生与人类资源部（DHHR）。
方法
概率匹配用于将患者与 NDI 相关联，确定性匹配用于 SSDMF 和 DHHR 生命统计记录（WVDMF）。在子分析中，我们使用 Epic 中记录的死亡事件（ = 30,217）进一步验证了 SSDMF、NDI 和 WVDMF 捕获的一部分死亡事件。
结果
在 SSDMF 捕获的死亡事件中，分别有 59.8%和 68.5%被 NDI 和 WVDMF 捕获；在 NDI 捕获的死亡事件中，分别有 80%和 78%被 SSDMF 和 WVDMF 捕获。NDI 和 WVDMF（61.2%）和 NDI 和 SSDMF（60.6%）的 Kappa 统计值最强，而 SSDMF 和 WVDMF（27.9%）的 Kappa 统计值最弱。在 Epic 中记录的死亡事件中，分别有 84.3%、85.5%和 84.4%被 SSDMF、NDI 和 WVDMF 捕获。少于 2%的 Epic 中记录的患者死亡事件在任何死亡登记处都未找到。最后，在任何死亡登记处的“死者”中，约有 0.2%的人在死亡日期至少 6 个月后再次出现在 Epic 中，这是一个非常小的比例，从而进一步验证了这些关联。
结论
NDI 在捕获我们的 EHR 中的死亡事件方面具有最大的有效性。虽然 SSDMF 和州生命统计记录在识别死亡事件方面的捕获率和一致性略低，但这些登记处可能是大型医院系统的整个 EHR 进行研究和质量保证研究的合理替代方案。研究人员还应注意到，在 EHR 中会有一小部分“已死亡”的患者重新出现。
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