Evidence map›Paper›PMID 32479181›Full record

ReviewAnnals of internal medicine2020

Studying Workflow and Workarounds in Electronic Health Record-Supported Work to Improve Health System Performance.

Kai Zheng, Raj M Ratwani, Julia Adler-Milstein

Abstract readReview
In one paragraph

Review in Annals of internal medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
53citing 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

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

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  10. Digital Transformation in Health Care: Are We on the Right Track?Journal of medical Internet research · 2026
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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

3 authors.

Kai ZhengSchool of Information and Computer Sciences and School of Medicine, University of California, Irvine, Irvine, California (K.Z.).
Raj M RatwaniMedStar Health National Center for Human Factors in Healthcare, Washington, DC (R.M.R.).
Julia Adler-MilsteinSchool of Medicine, University of California, San Francisco, San Francisco, California (J.A.).

Funding

University of California Health Participation in the National COVID Cohort Collaborative (N3C)UL1TR001414 · NCATS · UNIVERSITY OF CALIFORNIA-IRVINE · PI COOPER, DAN M, VILAIN, ERIC J. · 2015 to 2023
$35.1M
NCATS NIH HHS UL1 TR001414
6 · The paper itself

Abstract

Clinical workflow is the enactment of a series of steps to perform a clinical activity. The transition from paper to electronic health records (EHRs) over the past decade has been characterized by profound challenges supporting clinical workflow, impeding frontline clinicians' ability to deliver safe, efficient, and effective care. In response, there has been substantial effort to study clinical workflow as well as workarounds-exceptions to routine workflow-in order to identify opportunities for improvement. This article describes predominant methods of studying workflow and workarounds and provides examples of the applications of these methods along with the resulting insights. Challenges to studying workflow and workarounds are described, and recommendations for how to approach such studies are given. Although there is not yet a set of standard approaches, this article helps advance workflow research that ultimately serves to inform how to coevolve the design of EHR systems and organizational decisions about processes, roles, and responsibilities in order to support clinical workflow that more consistently delivers on the potential benefits of a digitized health care system.

Indexed as

Quality ImprovementWorkflowDelivery of Health CareElectronic Health RecordsHumans

Identifiers

PMID32479181
PMCPMC8061456

What OpenQuestion holds

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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.