{"title":"A decision-analytic method to evaluate the cost-effectiveness of remote monitoring technology for chronic depression.","authors":"Xiaonan Sun, Lawrence Wissow, Shan Liu","doi":"10.1017/S0266462324004677","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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).</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":14467,"journal":{"name":"International Journal of Technology Assessment in Health Care","volume":"40 1","pages":"e79"},"PeriodicalIF":2.6000,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Technology Assessment in Health Care","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1017/S0266462324004677","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
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.
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.