What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask - Archive ouverte HAL Access content directly
Journal Articles Journal of Medical Internet Research Year : 2021

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

(1) , (2) , (1) , (1) , (3, 4) , (5) , (1) , (6) , (7) , (8) , (1) , (9) , (1) , (10) , (11) , (1) , (1, 12) , (13) , (14) , (15) , (15) , (11) , (1, 12) , (16, 17, 18) , (13) , (19) , (1) , (20) , (8) , (15) , (21) , (1, 22) , (23, 11) , (24) , (6) , (24) , (1, 12) , (1) , (1)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
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 = University of Pavia
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 [Philadelphia]
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 [Chicago, Ill. USA]
Kenneth Mandl
Boston Children's Hospital
Mohamad Daniar
Boston Children's Hospital
Jason Moore
University of Pennsylvania [Philadelphia]
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 [Philadelphia]
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]

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.

Domains

Life Sciences [q-bio] Santé publique et épidémiologie Computer Science [cs] Operations Research [cs.RO]

Antoine Neuraz  : Connect in order to contact the contributor

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

Submitted on : Monday, December 13, 2021-10:48:42 AM

Last modification on : Tuesday, January 31, 2023-4:31:50 AM

Consult the full text

Dates and versions

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

Identifiers

Cite

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⟩

Export

BibTeX TEI Dublin Core DC Terms EndNote Datacite

Collections

EPHE INRIA CORDELIERS INRIA2 PSL SORBONNE-UNIVERSITE SU-SCIENCES UNIV-PARIS UP-SANTE
24 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More