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评估一款用于协助医师下达头部计算机断层扫描医嘱的临床移动应用程序的可用性和质量:混合方法研究。

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

机构信息

Health Information Management Research Center, Kashan University of Medical Sciences, Kashan, Iran.

Department of Neurosurgery, Kashan University of Medical Sciences, Kashan, Iran.

出版信息

JMIR Hum Factors. 2024 Sep 9;11:e55790. doi: 10.2196/55790.


DOI:10.2196/55790
PMID:39250788
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11420597/
Abstract

BACKGROUND: Among 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. OBJECTIVE: This 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. METHODS: Our 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. RESULTS: The 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. CONCLUSIONS: Evaluating 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.

摘要

背景:在影响医疗保健提供者使用移动应用程序的众多因素中,包括用户特征(如灵活性、解剖结构和态度)和移动功能(如屏幕和按钮大小),应用程序的可用性和质量已被证明是最具影响力的因素。

目的:本研究旨在调查 Head Computed Tomography Scan Appropriateness Criteria(HAC)移动应用程序在医生进行计算机断层扫描(CT)扫描时的可用性和质量。

方法:我们的研究设计主要基于混合方法研究的方法学三角测量,包括使用定量和定性的出声思维可用性测试、使用移动应用程序评级量表(MARS)进行质量评估的定量分析,以及在 3 个阶段进行的汇报。共有 16 名实习医生参与了质量评估和测试可用性特征,包括效率、有效性、易学性、错误和对 HAC 应用程序的满意度。

结果:HAC 应用程序的效率和有效性被认为是令人满意的,评分分别为 97.8%和 96.9%。MARS 评估量表显示 HAC 应用程序的整体质量得分较好(100 分中的 82 分)。在 4 个 MARS 子量表中,信息(100 分中的 73.37 分)和参与度(100 分中的 73.48 分)得分较低,而美学得分最高(100 分中的 87.86 分)。对每个 MARS 子量表中的项目进行分析后发现,在参与度子量表中,HAC 应用程序的最低得分是“定制”(100 分中的 63.6 分)。在功能子量表中,HAC 应用程序的最低值是“性能”(100 分中的 67.4 分)。对 HAC 应用程序的定性出声思维可用性测试发现了一些明显的可用性问题,这些问题分为 8 个主要类别:缺乏适合手指触摸的目标、搜索功能不佳、输入问题、数据呈现和信息控制效率低、控制和确认不明确、缺乏预测能力、缺乏辅助和支持、导航逻辑不清晰。

结论:使用混合方法评估移动应用程序的质量和可用性可提供有关其功能和缺点的有价值信息。强烈建议在评估移动应用程序时采用更全面和混合的方法策略,因为单一方法的结果并不能完全反映应用程序可用性和质量方面值得信赖和可靠的信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4534/11420597/548bfaf81ac0/humanfactors_v11i1e55790_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4534/11420597/e62e4d2c6e30/humanfactors_v11i1e55790_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4534/11420597/71341f56e175/humanfactors_v11i1e55790_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4534/11420597/5a5ca646bc47/humanfactors_v11i1e55790_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4534/11420597/f913ffc25e29/humanfactors_v11i1e55790_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4534/11420597/548bfaf81ac0/humanfactors_v11i1e55790_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4534/11420597/e62e4d2c6e30/humanfactors_v11i1e55790_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4534/11420597/71341f56e175/humanfactors_v11i1e55790_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4534/11420597/5a5ca646bc47/humanfactors_v11i1e55790_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4534/11420597/f913ffc25e29/humanfactors_v11i1e55790_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4534/11420597/548bfaf81ac0/humanfactors_v11i1e55790_fig5.jpg

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