Content area
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.
Details
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
; Wissow, Lawrence 2
; Liu, Shan 1 1 Department of Industrial and Systems Engineering, University of Washington, Seattle, WA, USA
2 Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA