Evidence map›Paper›PMID 31039120›Full record

ArticleBMJ health & care informatics2019

Clinician preferences for computerised clinical decision support for medications in primary care: a focus group study.

Katy E Trinkley, Weston W Blakeslee, Daniel D Matlock, David P Kao, Amanda G Van Matre, Robert Harrison, Cynthia L Larson, Nic Kostman, Jennifer A Nelson, Chen-Tan Lin and 1 more

Abstract read
In one paragraph

Article in BMJ health & care informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing 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

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

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  6. Interruptive Electronic Alerts for Choosing Wisely Recommendations: A Cluster Randomized Controlled Trial.Journal of the American Medical Informatics Association : JAMIA · 2022
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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

11 authors.

Katy E TrinkleyDepartment of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, USA katy.trinkley@ucdenver.edu.
Weston W BlakesleeRxRevu Inc, Denver, Colorado, USA.
Daniel D MatlockDivision of Geriatric Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA.
David P KaoDepartment of Clinical Informatics, University of Colorado Health, Aurora, Colorado, USA.
Amanda G Van MatreDepartment of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, USA.
Robert HarrisonDepartment of Clinical Informatics, University of Colorado Health, Aurora, Colorado, USA.
Cynthia L LarsonDepartment of Clinical Informatics, University of Colorado Health, Aurora, Colorado, USA.
Nic KostmanRxRevu Inc, Denver, Colorado, USA.
Jennifer A NelsonDepartment of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, USA.
Chen-Tan LinDivision of General Internal Medicine, Department of Medicine, University of Colorado School of Medicine Department of Medicine, Aurora, Colorado, USA.
Daniel C MaloneDepartment of Pharmacy Practice and Science, University of Arizona, Tucson, Arizona, USA.

Funding

A Closed-Loop Integrated Framework for development of Novel Treatment Strategies in Heart Failure (CLIFFNOTES-HF)K08HL125725 · NHLBI · UNIVERSITY OF COLORADO DENVER · PI KAO, DAVID PETER · 2017 to 2021
$826k
6 · The paper itself

Abstract

backgroundTo improve user-centred design efforts and efficiency; there is a need to disseminate information on modern day clinician preferences for technologies such as computerised clinical decision support (CDS).

objectiveTo describe clinician perceptions regarding beneficial features of CDS for chronic medications in primary care.

methodsThis study included focus groups and clinicians individually describing their ideal CDS. Three focus groups were conducted including prescribing clinicians from a variety of disciplines. Outcome measures included identification of favourable features and unintended consequences of CDS for chronic medication management in primary care. We transcribed recordings, performed thematic qualitative analysis and generated counts when possible.

resultsThere were 21 participants who identified four categories of beneficial CDS features during the group discussion: non-interruptive alerts, clinically relevant and customisable support, presentation of pertinent clinical information and optimises workflow. Non-interruptive alerts were broadly defined as passive alerts that a user chooses to review, whereas interruptive were active or disruptive alerts that interrupted workflow and one is forced to review before completing a task. The CDS features identified in the individual descriptions were consistent with the focus group discussion, with the exception of non-interruptive alerts. In the individual descriptions, 12 clinicians preferred interruptive CDS compared with seven clinicians describing non-interruptive CDS.

conclusionClinicians identified CDS for chronic medications beneficial when they are clinically relevant and customisable, present pertinent clinical information (eg, labs, vitals) and improve their workflow. Although clinicians preferred passive, non-interruptive alerts, most acknowledged that these may not be widely seen and may be less effective. These features align with literature describing best practices in CDS design and emphasise those features clinicians prioritise, which should be considered when designing CDS for medication management in primary care. These findings highlight the disparity between the current state of CDS design and clinician-stated design features associated with beneficial CDS.

Indexed as

Decision Support Systems, ClinicalMedical Order Entry SystemsPractice Patterns, Physicians'Primary Health CareAdultAgedFemaleFocus GroupsGrounded TheoryHumansMaleMiddle Agedclinical decision support systemselectronic prescribingprimary health care

Identifiers

PMID31039120
PMCPMC7062316

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.