Qingdao Municipal Center for Disease Control and Prevention, Qingdao, China.
Affiliated Qingdao Third People's Hospital, Qingdao University, Qingdao, China.
Front Public Health. 2024 Jan 8;11:1341871. doi: 10.3389/fpubh.2023.1341871. eCollection 2023.
The surge in computer-based health surveillance applications, leveraging technologies like big data analytics, artificial intelligence, and the Internet of Things, aims to provide personalized and streamlined medical services. These applications encompass diverse functionalities, from portable health trackers to remote patient monitoring systems, covering aspects such as heart rate tracking, task monitoring, glucose level checking, medication reminders, and sleep pattern assessment. Despite the anticipated benefits, concerns about performance, security, and alignment with healthcare professionals' needs arise with their widespread deployment. This study introduces a Hybrid Multi-Criteria Decision Analysis (MCDA) paradigm, combining the strengths of Additive Ratio Assessment (ARAS) and Analytic Hierarchy Process (AHP), to address the intricate nature of decision-making processes. The method involves selecting and structuring criteria hierarchically, providing a detailed evaluation of application efficacy. Professional stakeholders quantify the relative importance of each criterion through pairwise comparisons, generating criteria weights using AHP. The ARAS methodology then ranks applications based on their performance concerning the weighted criteria. This approach delivers a comprehensive assessment, considering factors like real-time capabilities, surgical services, and other crucial aspects. The research results provide valuable insights for healthcare practitioners, legislators, and technologists, aiding in deciding the adoption and integration of computer-based health monitoring applications, ultimately enhancing medical services and healthcare outcomes.
计算机健康监测应用程序的激增，利用大数据分析、人工智能和物联网等技术，旨在提供个性化和简化的医疗服务。这些应用程序涵盖了从便携式健康追踪器到远程患者监测系统等各种功能，涵盖了心率追踪、任务监测、血糖水平检查、药物提醒和睡眠模式评估等方面。尽管预计会带来好处，但随着它们的广泛部署，人们对其性能、安全性以及与医疗保健专业人员需求的一致性提出了担忧。本研究引入了一种混合多准则决策分析（MCDA）范式，结合了加性比率评估（ARAS）和层次分析（AHP）的优势，以解决决策过程的复杂性。该方法涉及选择和分层构建标准，对应用程序的疗效进行详细评估。专业利益相关者通过两两比较对每个标准的相对重要性进行量化，使用 AHP 生成标准权重。然后，ARAS 方法根据应用程序在加权标准方面的性能对其进行排名。该方法提供了全面的评估，考虑了实时能力、手术服务和其他关键方面等因素。研究结果为医疗保健从业者、立法者和技术人员提供了有价值的见解，有助于决定采用和整合基于计算机的健康监测应用程序，最终提高医疗服务和医疗保健效果。
