Privacy-protecting, reliable response data discovery using COVID-19 patient observations.
Objective: To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online.Materials and Methods: We developed a distributed, federated network of 12 h...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 28; no. 8; pp. 1765 - 1777 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Aug2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151699291&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151699291 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Aug2021 vid: 28 iid: 8 pid: 622 pub: Oxford University Press / USA artinfo: ui: 151699291 151699291 NLM34051088 151699291 10.1093/jamia/ocab054 NLM34051088 151699291 ppf: 1765 ppct: 12 formats: tig: atl: Privacy-protecting, reliable response data discovery using COVID-19 patient observations. aug: au: Kim, Jihoon Neumann, Larissa Paul, Paulina Day, Michele E Aratow, Michael Bell, Douglas S Doctor, Jason N Hinske, Ludwig C Jiang, Xiaoqian Kim, Katherine K Matheny, Michael E Meeker, Daniella Pletcher, Mark J Schilling, Lisa M SooHoo, Spencer Xu, Hua Zheng, Kai Ohno-Machado, Lucila Consortium, for the R2D2 affil: UC San Diego Health Department of Biomedical Informatics, University of California San Diego , La Jolla, California, USA sug: ab: Objective: To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online.Materials and Methods: We developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data.Results: Our public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems.Discussion and Conclusions: We present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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