Evidence map›Paper›PMID 37802122›Full record

ArticleApplied clinical informatics2023

Identifying and Addressing Barriers to Implementing Core Electronic Health Record Use Metrics for Ambulatory Care: Virtual Consensus Conference Proceedings.

Deborah R Levy, Amanda J Moy, Nate Apathy, Julia Adler-Milstein, Lisa Rotenstein, Bidisha Nath, S Trent Rosenbloom, Thomas Kannampallil, Rebecca G Mishuris, Aram Alexanian and 5 more

Abstract readConsensus Statement
In one paragraph

Article in Applied clinical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

15 authors.

Deborah R LevyDepartment of Veterans Affairs, VA Connecticut Healthcare System, West Haven, Connecticut, United States.
Amanda J MoyDepartment of Biomedical Informatics, Columbia University, New York, New York, United States.
Nate ApathyNational Center for Human Factors in Healthcare, MedStar Health Research Institute, Washington, District of Columbia, United States.
Julia Adler-MilsteinDepartment of Medicine, Center for Clinical Informatics and Improvement Research, University of California, San Francisco, California, United States.
Lisa RotensteinDivision of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States.
Bidisha NathDepartment of Emergency Medicine, Yale University School of Medicine, New Haven, Connecticut, United States.
S Trent RosenbloomDepartment of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, United States.
Thomas KannampallilDepartment of Anesthesiology, Washington University School of Medicine, St. Louis, Missouri, United States.
Rebecca G MishurisDivision of General Internal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States.
Aram AlexanianNovant Health, Charlotte, North Carolina, United States.
Amber SiejaDepartment of General Internal Medicine, University of Colorado School of Medicine, Aurora, Colorado, United States.
Michelle R HribarDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States.
Jigar S PatelOracle Corporation, Kansas City, Missouri, United States.
Christine A SinskyAmerican Medical Association, Chicago, Illinois, United States.
Edward R MelnickSection of Biomedical Informatics and Data Sciences, Yale University School of Medicine, New Haven, Connecticut, United States.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
NCATS NIH HHS UL1 TR001863
6 · The paper itself

Abstract

Precise, reliable, valid metrics that are cost-effective and require reasonable implementation time and effort are needed to drive electronic health record (EHR) improvements and decrease EHR burden. Differences exist between research and vendor definitions of metrics. PROCESS:  We convened three stakeholder groups (health system informatics leaders, EHR vendor representatives, and researchers) in a virtual workshop series to achieve consensus on barriers, solutions, and next steps to implementing the core EHR use metrics in ambulatory care.

conclusionActionable solutions identified to address core categories of EHR metric implementation challenges include: (1) maintaining broad stakeholder engagement, (2) reaching agreement on standardized measure definitions across vendors, (3) integrating clinician perspectives, and (4) addressing cognitive and EHR burden. Building upon the momentum of this workshop's outputs offers promise for overcoming barriers to implementing EHR use metrics.

Indexed as

Electronic Health RecordsMedical InformaticsAmbulatory CareBenchmarkingHumans

Identifiers

PMID37802122
PMCPMC10686750

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.