1. What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask
- Author
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Yuan Luo, Nathan Palmer, David A. Hanauer, Shawn N. Murphy, Amelia L.M. Tan, Brett K. Beaulieu-Jones, Gilbert S. Omenn, Kavishwar B. Wagholikar, Riccardo Bellazzi, Bruce J. Aronow, Kee Yuan Ngiam, John H. Holmes, Antoine Neuraz, Gabriel A. Brat, Miguel Pedrera-Jiménez, Lav P Patel, Marzyeh Ghassemi, Mohamad Daniar, Griffin M. Weber, Tianxi Cai, Nils Gehlenborg, Alba Gutiérrez-Sacristán, Robert L. Bradford, Andrew M South, Isaac S. Kohane, Andrew Vallejos, Chuan Hong, Mario Cannataro, Kenneth D. Mandl, Piotr Sliz, Jeffrey G. Klann, Bradley W Taylor, Noelia García-Barrio, James J. Cimino, Paul Avillach, Ne Hooi Will Loh, Jason H. Moore, Carlo Torti, Deanne Taylor, Harvard Medical School [Boston] (HMS), University of Cincinnati (UC), University of Pavia, Istituti Clinici Scientifici Maugeri [Pavia] (IRCCS Pavia - ICS Maugeri), University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), Università degli Studi 'Magna Graecia' di Catanzaro [Catanzaro, Italie] (UMG), University of Alabama at Birmingham [ Birmingham] (UAB), 12 de Octubre University Hospital, University of Toronto, University of Michigan Medical School [Ann Arbor], University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, University of Pennsylvania [Philadelphia], Massachusetts General Hospital [Boston], Northwestern University [Chicago, Ill. USA], Boston Children's Hospital, Service d'informatique médicale et biostatistiques [CHU Necker], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), University of Michigan System, University of Kansas [Kansas City], Wake Forest School of Medicine [Winston-Salem], Wake Forest Baptist Medical Center, National University of Singapore (NUS), Children’s Hospital of Philadelphia (CHOP ), Medical College of Wisconsin [Milwaukee] (MCW), Università degli Studi di Pavia = University of Pavia (UNIPV), Università degli Studi 'Magna Graecia' di Catanzaro = University of Catanzaro (UMG), Hospital Universitario 12 de Octubre [Madrid], University of Pennsylvania, National University Hospital [Singapore] (NUH), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Health data- and model- driven Knowledge Acquisition (HeKA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité)-École Pratique des Hautes Études (EPHE)
- Subjects
Computer science ,reporting standards ,Field (computer science) ,0302 clinical medicine ,Multidisciplinary approach ,publishing ,MESH: COVID-19 ,MESH: Data Collection ,030212 general & internal medicine ,MESH: Publishing ,MESH: Electronic Health Records ,media_common ,0303 health sciences ,lcsh:Public aspects of medicine ,Data Collection ,[INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO] ,3. Good health ,MESH: Reproducibility of Results ,quality ,statistics ,lcsh:R858-859.7 ,Best practice ,media_common.quotation_subject ,review ,Health Informatics ,lcsh:Computer applications to medicine. Medical informatics ,03 medical and health sciences ,Viewpoint ,Humans ,data quality ,MESH: SARS-CoV-2 ,Quality (business) ,030304 developmental biology ,MESH: Humans ,Data collection ,real-world data ,SARS-CoV-2 ,literature ,Reproducibility of Results ,COVID-19 ,lcsh:RA1-1270 ,Transparency (behavior) ,Data science ,reporting checklist ,electronic health records ,Data quality ,Informatics ,[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie - Abstract
International audience; 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.
- Published
- 2021
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