Evidence map›Paper›PMID 33340326›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2021

Building the evidence-base to reduce electronic health record-related clinician burden.

Christine Dymek, Bryan Kim, Genevieve B Melton, Thomas H Payne, Hardeep Singh, Chun-Ju Hsiao

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 41 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 2 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

41 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Observational
  5. Article
  6. Article
  7. Review
  8. Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and Prediction.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2025
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  20. Medical and nursing clinician perspectives on the usability of the hospital electronic medical record: A qualitative analysis.Health information management : journal of the Health Information Management Association of Australia · 2024
    Article
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

6 authors.

Christine DymekDivision of Digital Healthcare Research, Agency for Healthcare Research and Quality, Rockville, Maryland, USA.
Bryan KimHealthcare Delivery and Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland, USA.
Genevieve B MeltonDepartment of Surgery and Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.
Thomas H PayneUniversity of Washington School of Medicine, Seattle, Washington, USA.
Hardeep SinghCenter for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, Texas, USA.
Chun-Ju HsiaoDivision of Digital Healthcare Research, Agency for Healthcare Research and Quality, Rockville, Maryland, USA.

Funding

Discovery and Visualization of New Information from Clinical Reports in the EHRR01HS022085 · AHRQ · UNIVERSITY OF MINNESOTA · PI MELTON-MEAUX, GENEVIEVE B · 2013 to 2016
$1.9M
Application of a Machine Learning to Enhance e-Triggers to Detect and Learn from Diagnostic Safety EventsR01HS027363 · AHRQ · BAYLOR COLLEGE OF MEDICINE · PI SINGH, HARDEEP · 2019 to 2021
$1.5M
Understanding Provider Workflow Needs in Electronic CommunicationK08HS022901 · AHRQ · BAYLOR COLLEGE OF MEDICINE · PI MURPHY, DANIEL R · 2014 to 2018
$766k
Improving Direct Notification of Abnormal Test Results via Patient PortalsR21HS023602 · AHRQ · BAYLOR COLLEGE OF MEDICINE · PI SINGH, HARDEEP · 2014 to 2015
$300k
NLP to Identify and Rank Clinically Relevant Information for EHRs in the Emergency DepartmentR21HS024541 · AHRQ · UNIVERSITY OF COLORADO DENVER · PI GOSS, FOSTER R · 2016 to 2017
$298k
Improving Accuracy of Electronic Notes Using A Faster, Simpler ApproachR21HS023631 · AHRQ · UNIVERSITY OF WASHINGTON · PI PAYNE, THOMAS H. · 2014 to 2015
$294k
AHRQ HHS K08 HS022901AHRQ HHS R01 HS022085AHRQ HHS R01 HS027363AHRQ HHS R21 HS023602AHRQ HHS R21 HS023631AHRQ HHS R21 HS024541
6 · The paper itself

Abstract

Clinicians face competing pressures of being clinically productive while using imperfect electronic health record (EHR) systems and maximizing face-to-face time with patients. EHR use is increasingly associated with clinician burnout and underscores the need for interventions to improve clinicians' experiences. With an aim of addressing this need, we share evidence-based informatics approaches, pragmatic next steps, and future research directions to improve 3 of the highest contributors to EHR burden: (1) documentation, (2) chart review, and (3) inbox tasks. These approaches leverage speech recognition technologies, natural language processing, artificial intelligence, and redesign of EHR workflow and user interfaces. We also offer a perspective on how EHR vendors, healthcare system leaders, and policymakers all play an integral role while sharing responsibility in helping make evidence-based sociotechnical solutions available and easy to use.

Indexed as

Electronic Health RecordsBurnout, ProfessionalDocumentationElectronic MailHumansTime FactorsWorkflowWorkloadclinician burnoutelectronic health recordshealth information technologyusabilityworkflow

Identifiers

PMID33340326
PMCPMC8068419

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.