Evidence map›Paper›PMID 30602195›Full record

ArticleApplied clinical informatics2019

CDS in a Learning Health Care System: Identifying Physicians' Reasons for Rejection of Best-Practice Recommendations in Pneumonia through Computerized Clinical Decision Support.

Barbara E Jones, Dave S Collingridge, Caroline G Vines, Herman Post, John Holmen, Todd L Allen, Peter Haug, Charlene R Weir, Nathan C Dean

Abstract read
In one paragraph

Article in Applied clinical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

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  12. Investigating Why Clinicians Deviate from Standards of Care: Liberating Patients from Mechanical Ventilation in the ICU.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2024
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  13. Article
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  15. Article
  16. Deploying an Electronic Clinical Decision Support Tool for Diagnosis and Treatment of Pneumonia Into Rural and Critical Access Hospitals: Utilization, Effect on Processes of Care, and Clinician Satisfaction.The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association · 2022
    Article
  17. Development and Internal Validation of a Prediction Model to Risk Stratify Children With Suspected Community-Acquired Pneumonia.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2021
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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

9 authors.

Barbara E JonesVA Salt Lake City IDEAS Center, VA Salt Lake City Healthcare System, Salt Lake City, Utah, United States.
Dave S CollingridgeIntermountain Healthcare, Murray, Utah, United States.
Caroline G VinesIntermountain Healthcare, Murray, Utah, United States.
Herman PostHomer Warner Center for Informatics, Intermountain Healthcare, Murray, Utah, United States.
John HolmenHomer Warner Center for Informatics, Intermountain Healthcare, Murray, Utah, United States.
Todd L AllenDepartment of Emergency Medicine, Intermountain Healthcare, Murray, Utah, United States.
Peter HaugHomer Warner Center for Informatics, Intermountain Healthcare, Murray, Utah, United States.
Charlene R WeirDepartment of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United States.
Nathan C DeanDivision of Pulmonary and Critical Care Medicine, Intermountain Healthcare and University of Utah, Murray, Utah, United States.

Funding

UNIVERSITY OF UTAH CENTER FOR CLINICAL AND TRANSLATIONAL SCIENCE-UL1: BPCAUL1RR025764 · NCRR · UNIVERSITY OF UTAH · PI MCCLAIN, DONALD A. · 2008 to 2011
$19.4M
Understanding and Improving Decision-making in Pneumonia with InformaticsIK2HX001908 · VA · VA SALT LAKE CITY HEALTHCARE SYSTEM · PI JONES, BARBARA ELLEN · 2018 to 2021
–
HSRD VA IK2 HX001908NCRR NIH HHS UL1 RR025764
6 · The paper itself

Abstract

backgroundLocal implementation of guidelines for pneumonia care is strongly recommended, but the context of care that affects implementation is poorly understood. In a learning health care system, computerized clinical decision support (CDS) provides an opportunity to both improve and track practice, providing insights into the implementation process.

objectivesThis article examines physician interactions with a CDS to identify reasons for rejection of guideline recommendations.

methodsWe implemented a multicenter bedside CDS for the emergency department management of pneumonia that integrated patient data with guideline-based recommendations. We examined the frequency of adoption versus rejection of recommendations for site-of-care and antibiotic selection. We analyzed free-text responses provided by physicians explaining their clinical reasoning for rejection, using concept mapping and thematic analysis.

resultsAmong 1,722 patient episodes, physicians rejected recommendations to send a patient home in 24%, leaving text in 53%; reasons for rejection of the recommendations included additional or alternative diagnoses beyond pneumonia, and comorbidities or signs of physiologic derangement contributing to risk of outpatient failure that were not processed by the CDS. Physicians rejected broad-spectrum antibiotic recommendations in 10%, leaving text in 76%; differences in pathogen risk assessment, additional patient information, concern about antibiotic properties, and admitting physician preferences were given as reasons for rejection.

conclusionWhile adoption of CDS recommendations for pneumonia was high, physicians rejecting recommendations frequently provided feedback, reporting alternative diagnoses, additional individual patient characteristics, and provider preferences as major reasons for rejection. CDS that collects user feedback is feasible and can contribute to a learning health system.

Indexed as

Decision Support Systems, ClinicalLearning Health SystemPneumoniaAdultAnti-Bacterial AgentsElectronic Health RecordsFemaleGuideline AdherenceHumansMaleMiddle AgedPractice Patterns, Physicians'Anti-Bacterial Agents

Identifiers

PMID30602195
PMCPMC6327742

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.