Evidence map›Paper›PMID 34636911›Full record

ArticleJAMA network open2021

Analysis of Electronic Health Record Use and Clinical Productivity and Their Association With Physician Turnover.

Edward R Melnick, Allan Fong, Bidisha Nath, Brian Williams, Raj M Ratwani, Richard Goldstein, Ryan T O'Connell, Christine A Sinsky, Daniel Marchalik, Mihriye Mete

Abstract read
In one paragraph

Article in JAMA network open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

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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

10 authors.

Edward R MelnickDepartment of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut.
Allan FongMedStar Health National Center for Human Factors in Healthcare, Columbia, Maryland.
Bidisha NathDepartment of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut.
Brian WilliamsNortheast Medical Group, Yale-New Haven Health, Stratford, Connecticut.
Raj M RatwaniMedStar Health National Center for Human Factors in Healthcare, Columbia, Maryland.
Richard GoldsteinNortheast Medical Group, Yale-New Haven Health, Stratford, Connecticut.
Ryan T O'ConnellNortheast Medical Group, Yale-New Haven Health, Stratford, Connecticut.
Christine A SinskyAmerican Medical Association, Chicago, Illinois.
Daniel MarchalikDepartment of Urology, MedStar Health, Washington, DC.
Mihriye MeteMedStar Health Research Institute, Hyattsville, Maryland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Physician turnover takes a heavy toll on patients, physicians, and health care organizations. Survey research has established associations of electronic health record (EHR) use with professional burnout and reduction in professional effort, but these findings are subject to response fatigue and bias. Objective: To evaluate the association of physician productivity and EHR use patterns, as determined by vendor-derived EHR use data platforms, with physician turnover. Design, Setting, and Participants: This retrospective cohort study was conducted among nonteaching ambulatory physicians at a large ambulatory practice network based in New England. Data were collected from March 2018 to February 2020. Main Outcomes and Measures: Physician departure from the practice network; 4 time-based core measures of EHR use, normalized to 8 hours of scheduled clinical time; teamwork, percentage of a physician's orders that are placed by other members of the care team; and productivity measures of patient volume, intensity, and demand. Results: Among 335 physicians assessed for eligibility, 314 unique physicians (89.2%) were included in the analysis (123 [39%] women; 100 [32%] aged 45-54 years), with 5663 physician-months of data. The turnover rate was 5.1%/year (32 of 314 physicians). Physicians completed a mean 2.6 appointments/hour (95% CI, 2.5-2.6 appointments/hour) and 206 appointments/month (95% CI, 197-215 appointments/month) with 5.5 hours (95% CI, 5.3-5.8 hours) of EHR time for every 8 hours of scheduled patient time. After controlling for gender, medical specialty, and time, the following variables were associated with turnover: inbox time (odds ratio [OR], 0.70; 95% CI, 0.61-0.82; P < .001), teamwork (OR, 0.68; 95% CI, 0.52-0.87; P = .003), demand (ie, proportion of available appointments filled: OR, 0.49; 95% CI, 0.35-0.70; P < .001), and age 45 to 54 years vs 25 to 34 years (OR, 0.19; 95% CI, 0.04-0.93; P = .04). Conclusions and Relevance: In this study, physician productivity and EHR use metrics were associated with physician departure. Prospectively tracking these metrics could identify physicians at high risk of departure who would benefit from early, team-based, targeted interventions. The counterintuitive finding that less time spent on the EHR (in particular inbox management) was associated with physician departure warrants further investigation.

Indexed as

Area Under CurveClinical CompetenceCohort StudiesCorrelation of DataCross-Sectional StudiesDocumentationElectronic Health RecordsFemaleHumansMaleMiddle AgedOdds RatioPersonnel TurnoverPhysiciansProspective StudiesROC Curve

Identifiers

PMID34636911
PMCPMC8511970

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Registered trials

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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.