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What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask.
Kohane, Isaac S; Aronow, Bruce J; Avillach, Paul; Beaulieu-Jones, Brett K; Bellazzi, Riccardo; Bradford, Robert L; Brat, Gabriel A; Cannataro, Mario; Cimino, James J; García-Barrio, Noelia; Gehlenborg, Nils; Ghassemi, Marzyeh; Gutiérrez-Sacristán, Alba; Hanauer, David A; Holmes, John H; Hong, Chuan; Klann, Jeffrey G; Loh, Ne Hooi Will; Luo, Yuan; Mandl, Kenneth D; Daniar, Mohamad; Moore, Jason H; Murphy, Shawn N; Neuraz, Antoine; Ngiam, Kee Yuan; Omenn, Gilbert S; Palmer, Nathan; Patel, Lav P; Pedrera-Jiménez, Miguel; Sliz, Piotr; South, Andrew M; Tan, Amelia Li Min; Taylor, Deanne M; Taylor, Bradley W; Torti, Carlo; Vallejos, Andrew K; Wagholikar, Kavishwar B; Weber, Griffin M; Cai, Tianxi.
  • Kohane IS; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Aronow BJ; Biomedical Informatics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, United States.
  • Avillach P; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Beaulieu-Jones BK; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Bellazzi R; Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
  • Bradford RL; ICS Maugeri, Pavia, Italy.
  • Brat GA; North Carolina Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
  • Cannataro M; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Cimino JJ; Data Analytics Research Center, University Magna Graecia of Catanzaro, Catanzaro, Italy.
  • García-Barrio N; Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy.
  • Gehlenborg N; Informatics Institute, University of Alabama at Birmingham, Birmingham, AL, United States.
  • Ghassemi M; Department of Informatics, 12 de Octubre University Hospital, Madrid, Spain.
  • Gutiérrez-Sacristán A; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Hanauer DA; Department of Computer Science and Medicine, University of Toronto, Toronto, ON, Canada.
  • Holmes JH; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Hong C; Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, United States.
  • Klann JG; Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
  • Loh NHW; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Luo Y; Department of Medicine, Harvard Medical School, Boston, MA, United States.
  • Mandl KD; Laboratory of Computer Science, Massachusetts General Hospital, Boston, MA, United States.
  • Daniar M; National University Health Systems, Singapore, Singapore.
  • Moore JH; Department of Preventive Medicine, Northwestern University, Chicago, IL, United States.
  • Murphy SN; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States.
  • Neuraz A; Clinical Research Informatics, Boston Children's Hospital, Boston, MA, United States.
  • Ngiam KY; Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States.
  • Omenn GS; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Palmer N; Department of Neurology, Massachusetts General Hospital, Boston, MA, United States.
  • Patel LP; Department of Biomedical Informatics, Necker-Enfant Malades Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France.
  • Pedrera-Jiménez M; Centre de Recherche des Cordeliers, INSERM UMRS 1138 Team 22, Université de Paris, Paris, France.
  • Sliz P; National University Health Systems, Singapore, Singapore.
  • South AM; Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, United States.
  • Tan ALM; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Taylor DM; Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, KS, United States.
  • Taylor BW; Department of Informatics, 12 de Octubre University Hospital, Madrid, Spain.
  • Torti C; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States.
  • Vallejos AK; Section of Nephrology, Department of Pediatrics, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, NC, United States.
  • Wagholikar KB; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
  • Weber GM; Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA, United States.
  • Cai T; Department of Pediatrics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, United States.
J Med Internet Res ; 23(3): e22219, 2021 03 02.
Article in English | MEDLINE | ID: covidwho-1088863
ABSTRACT
Coincident with the tsunami of COVID-19-related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.
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Full text: Available Collection: International databases Database: MEDLINE Document Type: Article Main subject: Data Collection / Electronic Health Records / COVID-19 Subject: Data Collection / Electronic Health Records / COVID-19 Type of study: Prognostic study Language: English Journal: J Med Internet Res Year: 2021

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Full text: Available Collection: International databases Database: MEDLINE Document Type: Article Main subject: Data Collection / Electronic Health Records / COVID-19 Subject: Data Collection / Electronic Health Records / COVID-19 Type of study: Prognostic study Language: English Journal: J Med Internet Res Year: 2021
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