What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask - Inria - Institut national de recherche en sciences et technologies du numérique
Article Dans Une Revue Journal of Medical Internet Research Année : 2021

What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask

Isaac Kohane (1) , Bruce Aronow (2) , Paul Avillach (1) , Brett Beaulieu-Jones (1) , Riccardo Bellazzi (3, 4) , Robert Bradford (5) , Gabriel Brat (1) , Mario Cannataro (6) , James Cimino (7) , Noelia García-Barrio (8) , Nils Gehlenborg (1) , Marzyeh Ghassemi (9) , Alba Gutiérrez-Sacristán (1) , David Hanauer (10) , John Holmes (11) , Chuan Hong (1) , Jeffrey Klann (1, 12) , Ne Hooi Will Loh (13) , Yuan Luo (14) , Kenneth Mandl (15) , Mohamad Daniar (15) , Jason Moore (11) , Shawn Murphy (1, 12) , Antoine Neuraz (16, 17, 18) , Kee Yuan Ngiam (13) , Gilbert Omenn (19) , Nathan Palmer (1) , Lav Patel (20) , Miguel Pedrera-Jiménez (8) , Piotr Sliz (15) , Andrew South (21) , Amelia Li Min Tan (1, 22) , Deanne Taylor (23, 11) , Bradley Taylor (24) , Carlo Torti (6) , Andrew Vallejos (24) , Kavishwar Wagholikar (1, 12) , Griffin Weber (1) , Tianxi Cai (1)
1 HMS - Harvard Medical School [Boston]
2 UC - University of Cincinnati
3 UNIPV - Università degli Studi di Pavia [Italia] = University of Pavia [Italy] = Université de Pavie [Italie]
4 IRCCS Pavia - ICS Maugeri - Istituti Clinici Scientifici Maugeri [Pavia]
5 UNC - University of North Carolina [Chapel Hill]
6 UMG - Università degli Studi "Magna Graecia" di Catanzaro = University of Catanzaro
7 UAB - University of Alabama at Birmingham [ Birmingham]
8 Hospital Universitario 12 de Octubre [Madrid]
9 University of Toronto
10 University of Michigan Medical School [Ann Arbor]
11 University of Pennsylvania
12 Massachusetts General Hospital [Boston]
13 NUH - National University Hospital [Singapore]
14 Northwestern University [Evanston]
15 Boston Children's Hospital
16 Service d'informatique médicale et biostatistiques [CHU Necker]
17 CRC (UMR_S_1138 / U1138) - Centre de Recherche des Cordeliers
18 HeKA - Health data- and model- driven Knowledge Acquisition
19 University of Michigan [Ann Arbor]
20 University of Kansas [Kansas City]
21 Wake Forest School of Medicine [Winston-Salem]
22 NUS - National University of Singapore
23 CHOP - Children’s Hospital of Philadelphia
24 MCW - Medical College of Wisconsin [Milwaukee]
Isaac Kohane
Harvard Medical School [Boston]
Bruce Aronow
University of Cincinnati
Paul Avillach
Harvard Medical School [Boston]
Brett Beaulieu-Jones
Harvard Medical School [Boston]
Riccardo Bellazzi
Università degli Studi di Pavia [Italia] = University of Pavia [Italy] = Université de Pavie [Italie]
Istituti Clinici Scientifici Maugeri [Pavia]
Robert Bradford
University of North Carolina [Chapel Hill]
Gabriel Brat
Harvard Medical School [Boston]
Mario Cannataro
Università degli Studi "Magna Graecia" di Catanzaro = University of Catanzaro
James Cimino
University of Alabama at Birmingham [ Birmingham]
Noelia García-Barrio
Hospital Universitario 12 de Octubre [Madrid]
Nils Gehlenborg
Harvard Medical School [Boston]
Marzyeh Ghassemi
University of Toronto
Alba Gutiérrez-Sacristán
Harvard Medical School [Boston]
David Hanauer
University of Michigan Medical School [Ann Arbor]
John Holmes
University of Pennsylvania
Chuan Hong
Harvard Medical School [Boston]
Jeffrey Klann
Harvard Medical School [Boston]
Massachusetts General Hospital [Boston]
Ne Hooi Will Loh
National University Hospital [Singapore]
Yuan Luo
Northwestern University [Evanston]
Kenneth Mandl
Boston Children's Hospital
Mohamad Daniar
Boston Children's Hospital
Jason Moore
University of Pennsylvania
Shawn Murphy
Harvard Medical School [Boston]
Massachusetts General Hospital [Boston]
Antoine Neuraz
Service d'informatique médicale et biostatistiques [CHU Necker]
Centre de Recherche des Cordeliers
Health data- and model- driven Knowledge Acquisition
Kee Yuan Ngiam
National University Hospital [Singapore]
Gilbert Omenn
University of Michigan [Ann Arbor]
Nathan Palmer
Harvard Medical School [Boston]
Lav Patel
University of Kansas [Kansas City]
Miguel Pedrera-Jiménez
Hospital Universitario 12 de Octubre [Madrid]
Piotr Sliz
Boston Children's Hospital
Andrew South
Wake Forest School of Medicine [Winston-Salem]
Amelia Li Min Tan
Harvard Medical School [Boston]
National University of Singapore
Deanne Taylor
Children’s Hospital of Philadelphia
University of Pennsylvania
Bradley Taylor
Medical College of Wisconsin [Milwaukee]
Carlo Torti
Università degli Studi "Magna Graecia" di Catanzaro = University of Catanzaro
Andrew Vallejos
Medical College of Wisconsin [Milwaukee]
Kavishwar Wagholikar
Harvard Medical School [Boston]
Massachusetts General Hospital [Boston]
Griffin Weber
Harvard Medical School [Boston]
Tianxi Cai
Harvard Medical School [Boston]

Résumé

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.

Domaines

Santé publique et épidémiologie Recherche opérationnelle [math.OC]

Antoine Neuraz  : Connectez-vous pour contacter le contributeur

https://inria.hal.science/hal-03476779

Soumis le : lundi 13 décembre 2021-10:48:42

Dernière modification le : samedi 7 décembre 2024-23:56:02

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Dates et versions

hal-03476779 , version 1 (13-12-2021)

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Citer

Isaac Kohane, Bruce Aronow, Paul Avillach, Brett Beaulieu-Jones, Riccardo Bellazzi, et al.. What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask. Journal of Medical Internet Research, 2021, 23 (3), pp.e22219. ⟨10.2196/22219⟩. ⟨hal-03476779⟩

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