ArticleInternational journal of technology assessment in health care2025
A decision-analytic method to evaluate the cost-effectiveness of remote monitoring technology for chronic depression.
Article in International journal of technology assessment in health care, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A Scoping Review of Methods for Extrapolating Costs and Effects in Economic Evaluations of Remote Monitoring.PharmacoEconomics - open · 2026Review
- Pharmacoeconomic perspectives on HIV: integrating data for long-term treatment and prevention.Frontiers in medicine · 2025Review
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Authors and funding
3 authors.
Funding
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Abstract
objectivesAdvances 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.
methodsUsing 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).
resultsBase 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.
conclusionsRemote 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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