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

<h4>Background</h4>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 res...

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Autores principales:	Jonathan Salcedo, Monica Rosales, Jeniffer S Kim, Daisy Nuno, Sze-Chuan Suen, Alicia H Chang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/83c85b42368844d588c9db0ca67a68be
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spelling	oai:doaj.org-article:83c85b42368844d588c9db0ca67a68be2021-12-02T20:06:43ZCost-effectiveness of artificial intelligence monitoring for active tuberculosis treatment: A modeling study.1932-620310.1371/journal.pone.0254950https://doaj.org/article/83c85b42368844d588c9db0ca67a68be2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254950https://doaj.org/toc/1932-6203<h4>Background</h4>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.<h4>Methods</h4>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).<h4>Results</h4>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%).<h4>Conclusions</h4>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.Jonathan SalcedoMonica RosalesJeniffer S KimDaisy NunoSze-Chuan SuenAlicia H ChangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254950 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jonathan Salcedo Monica Rosales Jeniffer S Kim Daisy Nuno Sze-Chuan Suen Alicia H Chang Cost-effectiveness of artificial intelligence monitoring for active tuberculosis treatment: A modeling study.
description	<h4>Background</h4>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.<h4>Methods</h4>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).<h4>Results</h4>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%).<h4>Conclusions</h4>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.
format	article
author	Jonathan Salcedo Monica Rosales Jeniffer S Kim Daisy Nuno Sze-Chuan Suen Alicia H Chang
author_facet	Jonathan Salcedo Monica Rosales Jeniffer S Kim Daisy Nuno Sze-Chuan Suen Alicia H Chang
author_sort	Jonathan Salcedo
title	Cost-effectiveness of artificial intelligence monitoring for active tuberculosis treatment: A modeling study.
title_short	Cost-effectiveness of artificial intelligence monitoring for active tuberculosis treatment: A modeling study.
title_full	Cost-effectiveness of artificial intelligence monitoring for active tuberculosis treatment: A modeling study.
title_fullStr	Cost-effectiveness of artificial intelligence monitoring for active tuberculosis treatment: A modeling study.
title_full_unstemmed	Cost-effectiveness of artificial intelligence monitoring for active tuberculosis treatment: A modeling study.
title_sort	cost-effectiveness of artificial intelligence monitoring for active tuberculosis treatment: a modeling study.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/83c85b42368844d588c9db0ca67a68be
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Cost-effectiveness of artificial intelligence monitoring for active tuberculosis treatment: A modeling study.

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