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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) l...

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Autores principales: Conway, Rebecca B. N., Armistead, Matthew G., Denney, Michael J., Smith, Gordon S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Georg Thieme Verlag KG 2021
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7875675/
https://www.ncbi.nlm.nih.gov/pubmed/33567463
http://dx.doi.org/10.1055/s-0040-1722220
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author Conway, Rebecca B. N.
Armistead, Matthew G.
Denney, Michael J.
Smith, Gordon S.
author_facet Conway, Rebecca B. N.
Armistead, Matthew G.
Denney, Michael J.
Smith, Gordon S.
author_sort Conway, Rebecca B. N.
collection PubMed
description 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.
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spelling pubmed-78756752021-08-17 Validating the Matching of Patients in the Linkage of a Large Hospital System's EHR with State and National Death Databases Conway, Rebecca B. N. Armistead, Matthew G. Denney, Michael J. Smith, Gordon S. Appl Clin Inform 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. Georg Thieme Verlag KG 2021-01 2021-02-10 /pmc/articles/PMC7875675/ /pubmed/33567463 http://dx.doi.org/10.1055/s-0040-1722220 Text en The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. ( https://creativecommons.org/licenses/by-nc-nd/4.0/ ) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited.
spellingShingle Conway, Rebecca B. N.
Armistead, Matthew G.
Denney, Michael J.
Smith, Gordon S.
Validating the Matching of Patients in the Linkage of a Large Hospital System's EHR with State and National Death Databases
title Validating the Matching of Patients in the Linkage of a Large Hospital System's EHR with State and National Death Databases
title_full Validating the Matching of Patients in the Linkage of a Large Hospital System's EHR with State and National Death Databases
title_fullStr Validating the Matching of Patients in the Linkage of a Large Hospital System's EHR with State and National Death Databases
title_full_unstemmed Validating the Matching of Patients in the Linkage of a Large Hospital System's EHR with State and National Death Databases
title_short Validating the Matching of Patients in the Linkage of a Large Hospital System's EHR with State and National Death Databases
title_sort validating the matching of patients in the linkage of a large hospital system's ehr with state and national death databases
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7875675/
https://www.ncbi.nlm.nih.gov/pubmed/33567463
http://dx.doi.org/10.1055/s-0040-1722220
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