Evidence map›Paper›PMID 39250788›Full record

ArticleJMIR human factors2024

Evaluating the Usability and Quality of a Clinical Mobile App for Assisting Physicians in Head Computed Tomography Scan Ordering: Mixed Methods Study.

Zahra Meidani, Aydine Omidvar, Hossein Akbari, Fatemeh Asghari, Reza Khajouei, Zahra Nazemi, Ehsan Nabovati, Felix Holl

Abstract read
In one paragraph

Article in JMIR human factors, 2024. 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
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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

8 authors.

Zahra MeidaniHealth Information Management Research Center, Kashan University of Medical Sciences, Kashan, Iran.ORCID 0000-0001-8626-0513
Aydine OmidvarDepartment of Neurosurgery, Kashan University of Medical Sciences, Kashan, Iran.ORCID 0000-0003-3259-1231
Hossein AkbariDepartment of Epidemiology & Biostatistics, Kashan University of Medical Sciences, Kashan, Iran.ORCID 0000-0001-7486-8580
Fatemeh AsghariHealth Information Management Research Center, Kashan University of Medical Sciences, Kashan, Iran.ORCID 0009-0008-1716-8420
Reza KhajoueiDepartment of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran.ORCID 0000-0001-6094-5127
Zahra NazemiHealth Information Management Research Center, Kashan University of Medical Sciences, Kashan, Iran.ORCID 0000-0002-2019-3124
Ehsan NabovatiHealth Information Management Research Center, Kashan University of Medical Sciences, Kashan, Iran.ORCID 0000-0003-0430-4637
Felix HollDigiHealth Institute, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany.ORCID 0000-0002-4020-9509

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAmong the numerous factors contributing to health care providers' engagement with mobile apps, including user characteristics (eg, dexterity, anatomy, and attitude) and mobile features (eg, screen and button size), usability and quality of apps have been introduced as the most influential factors.

objectiveThis study aims to investigate the usability and quality of the Head Computed Tomography Scan Appropriateness Criteria (HAC) mobile app for physicians' computed tomography scan ordering.

methodsOur study design was primarily based on methodological triangulation by using mixed methods research involving quantitative and qualitative think-aloud usability testing, quantitative analysis of the Mobile Apps Rating Scale (MARS) for quality assessment, and debriefing across 3 phases. In total, 16 medical interns participated in quality assessment and testing usability characteristics, including efficiency, effectiveness, learnability, errors, and satisfaction with the HAC app.

resultsThe efficiency and effectiveness of the HAC app were deemed satisfactory, with ratings of 97.8% and 96.9%, respectively. MARS assessment scale indicated the overall favorable quality score of the HAC app (82 out of 100). Scoring 4 MARS subscales, Information (73.37 out of 100) and Engagement (73.48 out of 100) had the lowest scores, while Aesthetics had the highest score (87.86 out of 100). Analysis of the items in each MARS subscale revealed that in the Engagement subscale, the lowest score of the HAC app was "customization" (63.6 out of 100). In the Functionality subscale, the HAC app's lowest value was "performance" (67.4 out of 100). Qualitative think-aloud usability testing of the HAC app found notable usability issues grouped into 8 main categories: lack of finger-friendly touch targets, poor search capabilities, input problems, inefficient data presentation and information control, unclear control and confirmation, lack of predictive capabilities, poor assistance and support, and unclear navigation logic.

conclusionsEvaluating the quality and usability of mobile apps using a mixed methods approach provides valuable information about their functionality and disadvantages. It is highly recommended to embrace a more holistic and mixed methods strategy when evaluating mobile apps, because results from a single method imperfectly reflect trustworthy and reliable information regarding the usability and quality of apps.

Indexed as

Mobile ApplicationsTomography, X-Ray ComputedAdultFemaleHeadHumansMalePhysiciansmobile appsmobile phonephysicianstomographyuser-centered designuser-computer interfacex-ray computed

Identifiers

PMID39250788
PMCPMC11420597

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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.