Evidence map›Paper›PMID 32027360›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2020

Metrics for assessing physician activity using electronic health record log data.

Christine A Sinsky, Adam Rule, Genna Cohen, Brian G Arndt, Tait D Shanafelt, Christopher D Sharp, Sally L Baxter, Ming Tai-Seale, Sherry Yan, You Chen and 2 more

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 109 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
109citing papers in PubMed, 1 pooled it
–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

109 citing papers in PubMed, 1 synthesis or guideline pooled it.

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49 more citing papers are in PubMed but not listed here.

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.

Christine A SinskyDepartment of Medicine, American Medical Association, Chicago, Illinois, USA.
Adam RuleDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health Sciences University, Oregon, USA.
Genna CohenDepartment of Medicine, Mathematica, Washington, DC, USA.
Brian G ArndtDepartment of Family Medicine and Community Health, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin, USA.
Tait D ShanafeltDivision of Hematology, Department of Medicine, Stanford University, Stanford, California, USA.
Christopher D SharpDivision of Hematology, Department of Medicine, Stanford University, Stanford, California, USA.
Sally L BaxterDepartment of Biomedical Informatics, University of California, San Diego, San Diego, California, USA.
Ming Tai-SealeDepartment of Family Medicine and Public Health, University of California, San Diego, San Diego, California, USA.
Sherry YanDepartment of Medicine, Sutter Health, Walnut Creek, California, USA.
You ChenDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Julia Adler-MilsteinDepartment of Medicine, University of California, San Francisco, San Francisco, California, USA.
Michelle HribarDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health Sciences University, Oregon, USA.

Funding

TRAINING PROGRAM IN HEALTH INFORMATICST15LM007088 · NLM · OREGON HEALTH & SCIENCE UNIVERSITY · PI WILLIAM R HERSH, Vishnu Mohan · 1992 to 2026
$23.4M
San Diego Biomedical Informatics Education & Research (SABER)T15LM011271 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SHAMIM NEMATI · 2012 to 2026
$9.7M
Discovering Care Coordination Practice Patterns in the EMR: Interpretation and Impact on Patient OutcomesR01LM012854 · NLM · VANDERBILT UNIVERSITY MEDICAL CENTER · PI CHEN, YOU · 2019 to 2022
$1.5M
NLM NIH HHS R01 LM012854NLM NIH HHS T15 LM007088NLM NIH HHS T15 LM011271
6 · The paper itself

Abstract

Electronic health record (EHR) log data have shown promise in measuring physician time spent on clinical activities, contributing to deeper understanding and further optimization of the clinical environment. In this article, we propose 7 core measures of EHR use that reflect multiple dimensions of practice efficiency: total EHR time, work outside of work, time on documentation, time on prescriptions, inbox time, teamwork for orders, and an aspirational measure for the amount of undivided attention patients receive from their physicians during an encounter, undivided attention. We also illustrate sample use cases for these measures for multiple stakeholders. Finally, standardization of EHR log data measure specifications, as outlined here, will foster cross-study synthesis and comparative research.

Indexed as

EfficiencyElectronic Health RecordsPhysiciansTask Performance and AnalysisBurnout, ProfessionalDocumentationHumansTime FactorsEHR log datametricoperational efficiencytime studies, burnout

Identifiers

PMID32027360
PMCPMC7075531

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.