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

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Publikašuvnnas:International Journal of Technology Assessment in Health Care vol. 40, no. 1 (Jan 2025)
Váldodahkki: Sun, Xiaonan
Eará dahkkit: Wissow, Lawrence, Liu, Shan
Almmustuhtton:
Cambridge University Press
Fáttát:
Patients
Remote sensing
Simulation
Electronic health records
Datasets
Applications programs
Telemedicine
Mental disorders
Questionnaires
Mobile computing
Remote monitoring
Design specifications
Cost analysis
Mental depression
Mental health
Risk groups
Risk
Software
Evaluation
Cost effectiveness
Outpatient care facilities
Medical technology
Monitoring systems
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Abstrákta: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.
ISSN:0266-4623
1471-6348
DOI:10.1017/S0266462324004677
Gáldu:ABI/INFORM Global