Evidence map›Paper›PMID 37449057›Full record

ReviewJAMIA open2023

Methods used to evaluate usability of mobile clinical decision support systems for healthcare emergencies: a systematic review and qualitative synthesis.

Jared M Wohlgemut, Erhan Pisirir, Evangelia Kyrimi, Rebecca S Stoner, William Marsh, Zane B Perkins, Nigel R M Tai

Registry-linked trialAbstract readReview
In one paragraph

Review in JAMIA open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07634185 (Clinical Evaluation of an AI Risk Prediction and Decision Support System for Early Management of Injured Patients), which is not on this map. Cited by 8 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing 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.

NCT07634185 early_phase1not yet recruitingnot on this mapstarted 2026, after this paper: background citation

Clinical Evaluation of an AI Risk Prediction and Decision Support System for Early Management of Injured Patients: a Stepped-wedge Cluster Randomised Trial

TypeinterventionalSponsorQueen Mary University of LondonRan2026 to 2027Enrolled1,200ConditionsTrauma, Injury, Decision Support Systems, ClinicalArmsAI-TRiPS Device
3 · Its place in the literature

Who cites it

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

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Palliative care and social practice · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. 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

7 authors.

Jared M WohlgemutCentre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London, UK.ORCID https://orcid.org/0000-0001-8276-0465
Erhan PisirirDepartment of Electrical Engineering and Computer Science, Queen Mary University of London, London, UK.
Evangelia KyrimiDepartment of Electrical Engineering and Computer Science, Queen Mary University of London, London, UK.
Rebecca S StonerCentre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London, UK.
William MarshDepartment of Electrical Engineering and Computer Science, Queen Mary University of London, London, UK.
Zane B PerkinsCentre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London, UK.
Nigel R M TaiCentre for Trauma Sciences, Blizard Institute, Queen Mary University of London, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The aim of this study was to determine the methods and metrics used to evaluate the usability of mobile application Clinical Decision Support Systems (CDSSs) used in healthcare emergencies. Secondary aims were to describe the characteristics and usability of evaluated CDSSs. Materials and Methods: A systematic literature review was conducted using Pubmed/Medline, Embase, Scopus, and IEEE Xplore databases. Quantitative data were descriptively analyzed, and qualitative data were described and synthesized using inductive thematic analysis. Results: Twenty-three studies were included in the analysis. The usability metrics most frequently evaluated were efficiency and usefulness, followed by user errors, satisfaction, learnability, effectiveness, and memorability. Methods used to assess usability included questionnaires in 20 (87%) studies, user trials in 17 (74%), interviews in 6 (26%), and heuristic evaluations in 3 (13%). Most CDSS inputs consisted of manual input (18, 78%) rather than automatic input (2, 9%). Most CDSS outputs comprised a recommendation (18, 78%), with a minority advising a specific treatment (6, 26%), or a score, risk level or likelihood of diagnosis (6, 26%). Interviews and heuristic evaluations identified more usability-related barriers and facilitators to adoption than did questionnaires and user testing studies. Discussion: A wide range of metrics and methods are used to evaluate the usability of mobile CDSS in medical emergencies. Input of information into CDSS was predominantly manual, impeding usability. Studies employing both qualitative and quantitative methods to evaluate usability yielded more thorough results. Conclusion: When planning CDSS projects, developers should consider multiple methods to comprehensively evaluate usability.

Indexed as

clinical decision support systemshealthcare emergenciesmobile healthsystematic reviewusability

Identifiers

PMID37449057
PMCPMC10336299

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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.