Cost-effectiveness of artificial intelligence monitoring for active tuberculosis treatment: A modeling study

Background: Tuberculosis (TB) incidence in Los Angeles County, California, USA (5.7 per 100,000) is significantly higher than the U.S. national average (2.9 per 100,000). Directly observed therapy (DOT) is the preferred strategy for active TB treatment but requires substantial resources. We partnered with the Los Angeles County Department of Public Health (LACDPH) to evaluate the cost-effectiveness of AiCure, an artificial intelligence (AI) platform that allows for automated treatment monitoring. Methods: We used a Markov model to compare DOT versus AiCure for active TB treatment in LA County. Each cohort transitioned between health states at rates estimated using data from a pilot study for AiCure (N = 43) and comparable historical controls for DOT (N = 71). We estimated total costs (2017, USD) and quality-adjusted life years (QALYs) over a 16-month horizon to calculate the incremental cost-effectiveness ratio (ICER) and net monetary benefits (NMB) of AiCure. To assess robustness, we conducted deterministic (DSA) and probabilistic sensitivity analyses (PSA). Results: For the average patient, AiCure was dominant over DOT. DOT treatment cost $4,894 and generated 1.03 QALYs over 16-months. AiCure treatment cost $2,668 for 1.05 QALYs. At willingness-to-pay threshold of $150K/QALY, incremental NMB per-patient under AiCure was $4,973. In univariate DSA, NMB were most sensitive to monthly doses and vocational nurse wage; however, AiCure remained dominant. In PSA, AiCure was dominant in 93.5% of 10,000 simulations (cost-effective in 96.4%). Conclusions: AiCure for treatment of active TB is cost-effective for patients in LA County, California. Increased use of AI platforms in other jurisdictions could facilitate the CDC’s vision of TB elimination.