多学科发展老年人居家健康监测平台的必要性:系统综述。

IF 2.6 Q2 HEALTH CARE SCIENCES & SERVICES
JMIR Human Factors Pub Date : 2025-02-27 DOI:10.2196/59458
Chris Lochhead, Robert B Fisher
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引用次数: 0

摘要

背景:全球老龄化人口的增长增加了对新护理模式的需求。计算机化的居家保健监测是一种不断发展的模式,它探索了减少工作量、降低对资源密集型二级护理的需求以及提供更精确和个性化护理的可能性。尽管自主监控系统在实施得当的情况下具有潜在的社会效益,但在医疗机构中的应用却十分缓慢,大量跨学科的研究在实际应用中遇到了类似的共同障碍:本系统性综述的目的是构建一个评估框架,该框架可以根据研究在多大程度上解决了已经确定的应用障碍来评估研究,然后利用该框架分析各学科的文献,并确定多学科性与研究得到稳健发展的可能性之间的趋势:本文介绍了一个评分框架,该框架利用在对医疗保健监测领域不同学科的元综述中发现的 10 个已确定的常见应用障碍,来评估各项研究在解决关键开发问题方面的表现。然后,我们利用该框架进行了一次范围审查,以确定多学科参与对有效开发新监护技术的影响。具体来说,我们利用这一框架来衡量在研究中使用多学科性与一项研究以真正实际应用的方式得到发展的可能性之间的关系:我们的研究表明,根据评估标准,多学科观点(即计算机科学和医学以及公众和患者参与(PPI))对于解决应用研究与开发中常见的障碍具有显著的积极影响。根据我们的评估标准,多学科团队的平均得分率为 54.3%,而由医学专家和社会科学家组成的团队的得分率为 35%,由计算机科学和工程学组成的技术型团队的得分率为 2.68%。此外,让护理人员或最终用户以共同设计或基于公众参与的方式参与研究对评估得分也有显著影响(没有任何投入的平均得分率为 29.3%,有最终用户或护理人员投入的平均得分率分别为 48.3% 和 36.7%):本综述建议,为了限制新研究在其工作局限中任意重复相同问题的数量,从而提高研究的效率和效果,应优先促进多学科性,以加快该领域的发展速度,并鼓励在该领域开发更多可带来切实社会效益的技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On the Necessity of Multidisciplinarity in the Development of at-Home Health Monitoring Platforms for Older Adults: Systematic Review.

Background: The growth of aging populations globally has increased the demand for new models of care. At-home, computerized health care monitoring is a growing paradigm, which explores the possibility of reducing workloads, lowering the demand for resource-intensive secondary care, and providing more precise and personalized care. Despite the potential societal benefit of autonomous monitoring systems when implemented properly, uptake in health care institutions is slow, and a great volume of research across disciplines encounters similar common barriers to real-world implementation.

Objective: The goal of this systematic review was to construct an evaluation framework that can assess research in terms of how well it addresses already identified barriers to application and then use that framework to analyze the literature across disciplines and identify trends between multidisciplinarity and the likelihood of research being developed robustly.

Methods: This paper introduces a scoring framework for evaluating how well individual pieces of research address key development considerations using 10 identified common barriers to uptake found during meta-review from different disciplines across the domain of health care monitoring. A scoping review is then conducted using this framework to identify the impact that multidisciplinarity involvement has on the effective development of new monitoring technologies. Specifically, we use this framework to measure the relationship between the use of multidisciplinarity in research and the likelihood that a piece of research will be developed in a way that gives it genuine practical application.

Results: We show that viewpoints of multidisciplinarity; namely across computer science and medicine alongside public and patient involvement (PPI) have a significant positive impact in addressing commonly encountered barriers to application research and development according to the evaluation criteria. Using our evaluation metric, multidisciplinary teams score on average 54.3% compared with 35% for teams made up of medical experts and social scientists, and 2.68 for technical-based teams, encompassing computer science and engineering. Also identified is the significant effect that involving either caregivers or end users in the research in a co-design or PPI-based capacity has on the evaluation score (29.3% without any input and between 48.3% and 36.7% for end user or caregiver input respectively, on average).

Conclusions: This review recommends that, to limit the volume of novel research arbitrarily re-encountering the same issues in the limitations of their work and hence improve the efficiency and effectiveness of research, multidisciplinarity should be promoted as a priority to accelerate the rate of advancement in this field and encourage the development of more technology in this domain that can be of tangible societal benefit.

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来源期刊
JMIR Human Factors
JMIR Human Factors Medicine-Health Informatics
CiteScore
3.40
自引率
3.70%
发文量
123
审稿时长
12 weeks
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