A decision-analytic method to evaluate the cost-effectiveness of remote monitoring technology for chronic depression.

IF 2.6 4区 医学 Q2 HEALTH CARE SCIENCES & SERVICES International Journal of Technology Assessment in Health Care Pub Date : 2025-01-16 DOI:10.1017/S0266462324004677
Xiaonan Sun, Lawrence Wissow, Shan Liu
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Abstract

Objectives: Advances in mobile apps, remote sensing, and big data have enabled remote monitoring of mental health conditions, but the cost-effectiveness is unknown. This study proposed a systematic framework integrating computational tools and decision-analytic modeling to assess cost-effectiveness and guide emerging monitoring technologies development.

Methods: Using a novel decision-analytic Markov-cohort model, we simulated chronic depression patients' disease progression over 2 years, allowing treatment modifications at follow-up visits. The cost-effectiveness, from a payer's viewpoint, of five monitoring strategies was evaluated for patients in low-, medium-, and high-risk groups: (i) remote monitoring technology scheduling follow-up visits upon detecting treatment change necessity; (ii) rule-based follow-up strategy assigning the next follow-up based on the patient's current health state; and (iii-v) fixed frequency follow-up at two-month, four-month, and six-month intervals. Health outcomes (effects) were measured in quality-adjusted life-years (QALYs).

Results: Base case results showed that remote monitoring technology is cost-effective in the three risk groups under a willingness-to-pay (WTP) threshold of U.S. GDP per capita in year 2023. Full scenario analyses showed that, compared to rule-based follow-up, remote technology is 74 percent, 67 percent, and 74 percent cost-effective in the high-risk, medium-risk, and low-risk groups, respectively, and it is cost-effective especially if the treatment is effective and if remote monitoring is highly sensitive and specific.

Conclusions: Remote monitoring for chronic depression proves cost-effective and potentially cost-saving in the majority of simulated scenarios. This framework can assess emerging remote monitoring technologies and identify requirements for the technologies to be cost-effective in psychiatric and chronic care delivery.

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评估慢性抑郁症远程监测技术成本效益的决策分析方法。
目标:移动应用程序、遥感和大数据的进步使远程监测精神健康状况成为可能,但成本效益尚不清楚。本研究提出了一个集成计算工具和决策分析模型的系统框架,以评估成本效益并指导新兴监测技术的发展。方法:使用一种新的决策分析马尔可夫队列模型,我们模拟了慢性抑郁症患者2年以上的疾病进展,允许在随访时修改治疗方案。从付款人的角度,对低、中、高风险人群患者的五种监测策略的成本效益进行了评估:(1)远程监测技术在发现治疗改变的必要性时安排随访;(ii)基于规则的随访策略,根据患者目前的健康状况安排下一次随访;(iii-v)固定频率随访,间隔2个月、4个月和6个月。以质量调整生命年(QALYs)衡量健康结果(效应)。结果:基本案例结果表明,在2023年美国人均GDP的支付意愿(WTP)阈值下,远程监控技术在三个风险组中具有成本效益。完整情景分析表明,与基于规则的随访相比,远程技术在高风险、中等风险和低风险群体中的成本效益分别为74%、67%和74%,特别是在治疗有效和远程监测高度敏感和特异性的情况下,它具有成本效益。结论:在大多数模拟情况下,远程监测慢性抑郁症证明具有成本效益,并可能节省成本。该框架可以评估新兴的远程监测技术,并确定在精神病和慢性护理提供方面具有成本效益的技术要求。
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来源期刊
International Journal of Technology Assessment in Health Care
International Journal of Technology Assessment in Health Care 医学-公共卫生、环境卫生与职业卫生
CiteScore
4.40
自引率
15.60%
发文量
116
审稿时长
6-12 weeks
期刊介绍: International Journal of Technology Assessment in Health Care serves as a forum for the wide range of health policy makers and professionals interested in the economic, social, ethical, medical and public health implications of health technology. It covers the development, evaluation, diffusion and use of health technology, as well as its impact on the organization and management of health care systems and public health. In addition to general essays and research reports, regular columns on technology assessment reports and thematic sections are published.
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