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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Jul 13, 2020
Open Peer Review Period: Jul 13, 2020 - Jul 20, 2020
Date Accepted: Jan 10, 2021
Date Submitted to PubMed: Mar 5, 2021
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

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

Kohane IS, Aronow BJ, Avillach P, Beaulieu-Jones BK, Bellazzi R, Bradford RL, Brat GA, Cai T, Cannataro M, Cimino JJ, García-Barrio N, Gehlenborg N, Ghassemi M, Gutiérrez-Sacristán A, Hanauer DA, Holmes JH, Hong C, Klann JG, Loh NHW, Luo Y, Mandl KD, Mohamad D, Moore JH, Murphy SN, Neuraz A, Ngiam KY, Omenn GS, Palmer N, Patel LP, Pedrera-Jiménez M, Sliz P, South AM, Tan ALM, Taylor DM, Taylor BW, Torti C, Vallejos AK, Wagholikar KB, The Consortium For Clinical Characterization Of COVID-19 By EHR (4CE) , Weber GM

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

J Med Internet Res 2021;23(3):e22219

DOI: 10.2196/22219

PMID: 33600347

PMCID: 7927948

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

  • Isaac S Kohane; 
  • Bruce J Aronow; 
  • Paul Avillach; 
  • Brett K Beaulieu-Jones; 
  • Riccardo Bellazzi; 
  • Robert L Bradford; 
  • Gabriel A Brat; 
  • Tianxi Cai; 
  • Mario Cannataro; 
  • James J Cimino; 
  • Noelia García-Barrio; 
  • Nils Gehlenborg; 
  • Marzyeh Ghassemi; 
  • Alba Gutiérrez-Sacristán; 
  • David A Hanauer; 
  • John H Holmes; 
  • Chuan Hong; 
  • Jeffrey G Klann; 
  • Ne Hooi Will Loh; 
  • Yuan Luo; 
  • Kenneth D Mandl; 
  • Daniar Mohamad; 
  • Jason H Moore; 
  • Shawn N Murphy; 
  • Antoine Neuraz; 
  • Kee Yuan Ngiam; 
  • Gilbert S Omenn; 
  • Nathan Palmer; 
  • Lav P Patel; 
  • Miguel Pedrera-Jiménez; 
  • Piotr Sliz; 
  • Andrew M South; 
  • Amelia Li Min Tan; 
  • Deanne M Taylor; 
  • Bradley W Taylor; 
  • Carlo Torti; 
  • Andrew K Vallejos; 
  • Kavishwar B Wagholikar; 
  • The Consortium For Clinical Characterization Of COVID-19 By EHR (4CE); 
  • Griffin M Weber

ABSTRACT

Coincident with the tsunami of Covid19-related manuscripts, there has been a surge of studies using Real World Data (RWD), including those obtained from electronic health records. Unfortunately, several of these studies have resulted in withdrawn publication because of concerns regarding their soundness and quality. We argue here that there are pre-analytic hints and warning signs that are useful in judging RWD studies that might otherwise pass statistical muster. We outline several of these signs and suggest that review of RWD manuscripts include those who are familiar with how such data are generated.


 Citation

Please cite as:

Kohane IS, Aronow BJ, Avillach P, Beaulieu-Jones BK, Bellazzi R, Bradford RL, Brat GA, Cai T, Cannataro M, Cimino JJ, García-Barrio N, Gehlenborg N, Ghassemi M, Gutiérrez-Sacristán A, Hanauer DA, Holmes JH, Hong C, Klann JG, Loh NHW, Luo Y, Mandl KD, Mohamad D, Moore JH, Murphy SN, Neuraz A, Ngiam KY, Omenn GS, Palmer N, Patel LP, Pedrera-Jiménez M, Sliz P, South AM, Tan ALM, Taylor DM, Taylor BW, Torti C, Vallejos AK, Wagholikar KB, The Consortium For Clinical Characterization Of COVID-19 By EHR (4CE) , Weber GM

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

J Med Internet Res 2021;23(3):e22219

DOI: 10.2196/22219

PMID: 33600347

PMCID: 7927948

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© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.