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

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

SUBMITTER: Sun X 

PROVIDER: S-EPMC11811955 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature