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Privacy-protecting, reliable response data discovery using COVID-19 patient observations.
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.
  • Kim J; UC San Diego Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, USA.
  • Neumann L; Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany.
  • Paul P; LMU Klinikum, Department of Anesthesiology, Ludwig Maximilian University of Munich, Munich, Germany.
  • Day ME; UC San Diego Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, USA.
  • Aratow M; UC San Diego Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, USA.
  • Bell DS; San Mateo Medical Center, San Mateo, California, USA.
  • Doctor JN; Biomedical Informatics Program, UCLA Clinical and Translational Science Institute (CTSI), Los Angeles, California, USA.
  • Hinske LC; USC Schaeffer Center for Health Policy and Economics, Price School of Policy, University of Southern California, Los Angeles, California, USA.
  • Jiang X; Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany.
  • Kim KK; LMU Klinikum, Department of Anesthesiology, Ludwig Maximilian University of Munich, Munich, Germany.
  • Matheny ME; School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
  • Meeker D; Betty Irene Moore School of Nursing, University of California Davis Medical Center, Sacramento, California, USA.
  • Pletcher MJ; Health Informatics Division, Department of Public Health Sciences, School of Medicine, UC Davis Health, Sacramento, California, USA.
  • Schilling LM; GRECC Tennessee Valley Healthcare System, Nashville, Tennessee, USA.
  • SooHoo S; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  • Xu H; Department of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, California, USA.
  • Zheng K; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA.
  • Ohno-Machado L; Data Science and Patient Value Program, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.
J Am Med Inform Assoc ; 28(8): 1765-1776, 2021 07 30.
Article in English | MEDLINE | ID: covidwho-1246728
ABSTRACT

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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Algorithms / Computer Communication Networks / Natural Language Processing / Information Storage and Retrieval / Confidentiality / Electronic Health Records / COVID-19 Type of study: Observational study / Prognostic study Limits: Female / Humans / Male Language: English Journal: J Am Med Inform Assoc Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: Jamia

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Algorithms / Computer Communication Networks / Natural Language Processing / Information Storage and Retrieval / Confidentiality / Electronic Health Records / COVID-19 Type of study: Observational study / Prognostic study Limits: Female / Humans / Male Language: English Journal: J Am Med Inform Assoc Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: Jamia