Evidence map›Paper›PMID 34288951›Full record

Trial reportPloS one2021

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

Jonathan Salcedo, Monica Rosales, Jeniffer S Kim, Daisy Nuno, Sze-Chuan Suen, Alicia H Chang

Abstract readClinical Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  16. Innovative Approaches to Enhance and Measure Medication Adherence in Chronic Disease Management: A Review.Medical science monitor : international medical journal of experimental and clinical research · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jonathan SalcedoDepartment of Pharmaceutical and Health Economics, School of Pharmacy, University of Southern California, Los Angeles, California, United States of America.ORCID 0000-0003-2748-1011
Monica RosalesLos Angeles County Department of Public Health, Office of Health Assessment and Epidemiology, Los Angeles, California, United States of America.
Jeniffer S KimDepartment of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America.
Daisy NunoLos Angeles County Department of Public Health, Tuberculosis Control Program, Los Angeles, California, United States of America.
Sze-Chuan SuenLeonard D. Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, California, United States of America.ORCID 0000-0001-9453-5863
Alicia H ChangLos Angeles County Department of Public Health, Tuberculosis Control Program, Los Angeles, California, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTuberculosis (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.

methodsWe 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).

resultsFor 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%).

conclusionsAiCure 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.

Indexed as

AdultAgedArtificial IntelligenceCaliforniaCost-Benefit AnalysisFemaleHumansMaleMiddle AgedMonitoring, PhysiologicPilot ProjectsTuberculosis

Identifiers

PMID34288951
PMCPMC8294556

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.