基于其质量和用户评级的数字健康应用程序的分组使用K-Medoids集群:跨部分研究
Maciej Marek Zych1, Raymond Bond1, Maurice Mulvenna1
1School of Computing, Ulster University, 2-24 York Street, Belfast, BT15 1AP, United Kingdom, 44 7526852505.
JMIR mHealth and uHealth
|July 23, 2025
概括
数字健康应用程序的质量差异很大,许多应用程序缺乏专业和临床保证 (PCA). 用户评级并不总是反映PCA,突出显示数字健康解决方案需要更好的质量控制.
科学领域:
- 数字健康数字健康
- 医疗信息学 医疗信息学
- 软件质量保证 软件质量保证
背景情况:
- 现有超过35万个数字健康应用程序,需要对用户安全进行质量评估.
- 数字健康应用程序提供主动的医疗保健,可能减少提供者的负担.
- 了解数字健康应用程序类型学可以确定需要改进的领域.
研究的目的:
- 根据质量指标识别数字健康应用程序集群:专业和临床保证 (PCA),用户体验 (UX),数据隐私 (DP) 和用户评级.
- 调查应用类型学与NICE证据标准框架 (ESF) 等因素之间的关联,目标用户,类别和功能.
主要方法:
- 利用了1402个数字健康应用评估的数据,使用了护理和健康应用基线审查组织 (OBR) 的数据.
- 采用k-medoids集群来确定应用程序类型和最佳集群识别的肘部方法.
- 使用Shapiro-Wilk测试评估用户评级和OBR分数的正常性,并将群集差异与Wilcoxon等级总和测试进行比较.
主要成果:
- 确定了四个不同的应用程序集群:用户评分差 (15.7%),PCA和DP差 (18%),PCA差 (29.6%) 和高质量 (36.7%).
- 在统计学上显著,但效果小,在集群和NICE ESF等级,类别和特征之间发现了关联.
- "服务标志"功能和NICE ESF B层显示与应用程序集群的关联最强.
结论:
- 很大一部分数字健康应用程序,即使是那些用户评分很高的应用程序,在专业和临床保证 (PCA) 中也存在缺陷.
- 识别的类型突出显示PCA是数字健康应用程序改进的关键领域.
- 应用程序特征和质量集群之间的有限关联表明需要进一步研究影响数字健康应用程序质量的因素.
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