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

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.

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).

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.

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.