Evidence map›Paper›PMID 33315048›Full record

ArticleJAMA internal medicine2021

Assessment of Electronic Health Record Use Between US and Non-US Health Systems.

A Jay Holmgren, N Lance Downing, David W Bates, Tait D Shanafelt, Arnold Milstein, Christopher D Sharp, David M Cutler, Robert S Huckman, Kevin A Schulman

Erratum issuedAbstract readComparative Study
In one paragraph

Article in JAMA internal medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 74 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
74citing 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

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

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  20. Association of delayed asthma diagnosis with asthma exacerbations in children.The journal of allergy and clinical immunology. Global · 2025
    Article

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

A Jay HolmgrenInterfaculty Initiative in Health Policy, Harvard University, Cambridge, Massachusetts.
N Lance DowningDepartment of Medicine, Stanford University, Stanford, California.
David W BatesDepartment of General Internal Medicine, Brigham & Women's Hospital, Boston, Massachusetts.
Tait D ShanafeltDivision of Hematology, Department of Medicine, Stanford University, Palo Alto, California.
Arnold MilsteinClinical Excellence Research Center, Stanford University, Stanford, California.
Christopher D SharpDepartment of Medicine, Stanford University, Stanford, California.
David M CutlerDepartment of Economics, Harvard University, Cambridge, Massachusetts.
Robert S HuckmanHarvard Business School, Boston, Massachusetts.
Kevin A SchulmanDepartment of Medicine, Stanford University, Stanford, California.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Understanding how the electronic health record (EHR) system changes clinician work, productivity, and well-being is critical. Little is known regarding global variation in patterns of use. Objective: To provide insights into which EHR activities clinicians spend their time doing, the EHR tools they use, the system messages they receive, and the amount of time they spend using the EHR after hours. Design, Setting, and Participants: This cross-sectional study analyzed the deidentified metadata of ambulatory care health systems in the US, Canada, Northern Europe, Western Europe, the Middle East, and Oceania from January 1, 2019, to August 31, 2019. All of these organizations used the EHR software from Epic Systems and represented most of Epic Systems's ambulatory customer base. The sample included all clinicians with scheduled patient appointments, such as physicians and advanced practice practitioners. Exposures: Clinician EHR use was tracked by deidentified and aggregated metadata across a variety of clinical activities. Main Outcomes and Measures: Descriptive statistics for clinician EHR use included time spent on clinical activities, note documentation (as measured by the percentage of characters in the note generated by automated or manual data entry source), messages received, and time spent after hours. Results: A total of 371 health systems were included in the sample, of which 348 (93.8%) were located in the US and 23 (6.2%) were located in other countries. US clinicians spent more time per day actively using the EHR compared with non-US clinicians (mean time, 90.2 minutes vs 59.1 minutes; P < .001). In addition, US clinicians vs non-US clinicians spent significantly more time performing 4 clinical activities: notes (40.7 minutes vs 30.7 minutes; P < .001), orders (19.5 minutes vs 8.75 minutes; P < .001), in-basket messages (12.5 minutes vs 4.80 minutes; P < .001), and clinical review (17.6 minutes vs 14.8 minutes; P = .01). Clinicians in the US composed more automated note text than their non-US counterparts (77.5% vs 60.8% of note text; P < .001) and received statistically significantly more messages per day (33.8 vs 12.8; P < .001). Furthermore, US clinicians used the EHR for a longer time after hours, logging in 26.5 minutes per day vs 19.5 minutes per day for non-US clinicians (P = .01). The median US clinician spent as much time actively using the EHR per day (90.1 minutes) as a non-US clinician in the 99th percentile of active EHR use time per day (90.7 minutes) in the sample. These results persisted after controlling for organizational characteristics, including structure, type, size, and daily patient volume. Conclusions and Relevance: This study found that US clinicians compared with their non-US counterparts spent substantially more time actively using the EHR for a wide range of clinical activities or tasks. This finding suggests that US clinicians have a greater EHR burden that may be associated with nontechnical factors, which policy makers and health system leaders should consider when addressing clinician wellness.

Indexed as

PhysiciansCross-Sectional StudiesElectronic Health RecordsHumansInternationalityTime FactorsUnited States

Identifiers

PMID33315048
PMCPMC7737152

What OpenQuestion holds

Textmetadata
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.