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Accepted for/Published in: JMIR Medical Informatics

Date Submitted: Nov 14, 2021
Open Peer Review Period: Nov 14, 2021 - Jan 9, 2022
Date Accepted: Mar 11, 2022
Date Submitted to PubMed: Apr 18, 2022
(closed for review but you can still tweet)

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

Cluster Analysis of Primary Care Physician Phenotypes for Electronic Health Record Use: Retrospective Cohort Study

Fong A, Iscoe M, Sinsky CA, Haimovich A, Williams B, O'Connell RT, Goldstein R, Melnick E

Cluster Analysis of Primary Care Physician Phenotypes for Electronic Health Record Use: Retrospective Cohort Study

JMIR Med Inform 2022;10(4):e34954

DOI: 10.2196/34954

PMID: 35275070

PMCID: 9055474

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Exploration of Ambulatory Care Physician Phenotypes for Electronic Health Record Use

  • Allan Fong; 
  • Mark Iscoe; 
  • Christine A Sinsky; 
  • Adrian Haimovich; 
  • Brian Williams; 
  • Ryan T. O'Connell; 
  • Richard Goldstein; 
  • Edward Melnick

ABSTRACT

Background:

Electronic health records (EHRs) have become ubiquitous in United States office-based physician practices. However, the different ways users engage with EHRs remains poorly characterized.

Objective:

The objective of this paper is to explore EHR usage phenotypes amongst ambulatory care physicians.

Methods:

We applied affinity propagation, an unsupervised clustering machine learning technique, to identify EHR user types amongst primary care physicians.

Results:

We identified four distinct clusters generalized across internal medicine, family medicine, and pediatric specialties. Two groups, or phenotype clusters, of physicians with higher-than-average work outside of scheduled hours ratios had varied EHR usage suggesting one group may have worked from home out of necessity while the other preferred ad hoc work hours. From the two remaining groups, one group represented physicians with lower-than-average EHR time. The last group represented physicians who spend the largest proportion of their EHR time documenting notes.

Conclusions:

These findings demonstrate the utility of cluster analysis for exploring EHR phenotypes and may offer opportunities for interventions to improve EHR design and use to better support EHR users’ needs.


 Citation

Please cite as:

Fong A, Iscoe M, Sinsky CA, Haimovich A, Williams B, O'Connell RT, Goldstein R, Melnick E

Cluster Analysis of Primary Care Physician Phenotypes for Electronic Health Record Use: Retrospective Cohort Study

JMIR Med Inform 2022;10(4):e34954

DOI: 10.2196/34954

PMID: 35275070

PMCID: 9055474

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