Evidence map›Paper›PMID 33822970›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2021

Characterizing physician EHR use with vendor derived data: a feasibility study and cross-sectional analysis.

Edward R Melnick, Shawn Y Ong, Allan Fong, Vimig Socrates, Raj M Ratwani, Bidisha Nath, Michael Simonov, Anup Salgia, Brian Williams, Daniel Marchalik and 2 more

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

46 citing papers in PubMed.

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  17. Addressing Note Bloat: Solutions for Effective Clinical Documentation.Journal of the American College of Emergency Physicians open · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Edward R MelnickDepartment of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA.ORCID 0000-0002-6509-9537
Shawn Y OngDepartment of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
Allan FongMedStar Health National Center for Human Factors in Healthcare, Washington, DC, USA.ORCID 0000-0002-7550-1569
Vimig SocratesComputational Biology and Bioinformatics, Yale School of Medicine, New Haven, Connecticut, USA.ORCID 0000-0001-7955-9875
Raj M RatwaniMedStar Health National Center for Human Factors in Healthcare, Washington, DC, USA.
Bidisha NathDepartment of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
Michael SimonovDepartment of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
Anup SalgiaNortheast Ohio Medical University and Cerner Corporation, Kansas City, Missouri, USA.
Brian WilliamsNortheast Medical Group, Yale-New Haven Health, Stratford, Connecticut, USA.
Daniel MarchalikDepartment of Urology, MedStar Health, Washington, DC, USA.
Richard GoldsteinNortheast Medical Group, Yale-New Haven Health, Stratford, Connecticut, USA.
Christine A SinskyProfessional Satisfaction and Practice Sustainability, American Medical Association, Chicago, Illinois, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo derive 7 proposed core electronic health record (EHR) use metrics across 2 healthcare systems with different EHR vendor product installations and examine factors associated with EHR time. MATERIALS AND

methodsA cross-sectional analysis of ambulatory physicians EHR use across the Yale-New Haven and MedStar Health systems was performed for August 2019 using 7 proposed core EHR use metrics normalized to 8 hours of patient scheduled time.

resultsFive out of 7 proposed metrics could be measured in a population of nonteaching, exclusively ambulatory physicians. Among 573 physicians (Yale-New Haven N = 290, MedStar N = 283) in the analysis, median EHR-Time8 was 5.23 hours. Gender, additional clinical hours scheduled, and certain medical specialties were associated with EHR-Time8 after adjusting for age and health system on multivariable analysis. For every 8 hours of scheduled patient time, the model predicted these differences in EHR time (P < .001, unless otherwise indicated): female physicians +0.58 hours; each additional clinical hour scheduled per month -0.01 hours; practicing cardiology -1.30 hours; medical subspecialties -0.89 hours (except gastroenterology, P = .002); neurology/psychiatry -2.60 hours; obstetrics/gynecology -1.88 hours; pediatrics -1.05 hours (P = .001); sports/physical medicine and rehabilitation -3.25 hours; and surgical specialties -3.65 hours.

conclusionsFor every 8 hours of scheduled patient time, ambulatory physicians spend more than 5 hours on the EHR. Physician gender, specialty, and number of clinical hours practicing are associated with differences in EHR time. While audit logs remain a powerful tool for understanding physician EHR use, additional transparency, granularity, and standardization of vendor-derived EHR use data definitions are still necessary to standardize EHR use measurement.

Indexed as

MedicinePhysiciansChildCross-Sectional StudiesElectronic Health RecordsFeasibility StudiesFemaleHumans

Identifiers

PMID33822970
PMCPMC8279798

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.