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

Journal: PloS one
Published Date: Jan 1, 2021

Abstract

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.

Authors

  • Jonathan Salcedo
    Department of Pharmaceutical and Health Economics, School of Pharmacy, University of Southern California, Los Angeles, California, United States of America.
  • Monica Rosales
    Los Angeles County Department of Public Health, Office of Health Assessment and Epidemiology, Los Angeles, California, United States of America.
  • Jeniffer S Kim
    Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America.
  • Daisy Nuno
    Los Angeles County Department of Public Health, Tuberculosis Control Program, Los Angeles, California, United States of America.
  • Sze-Chuan Suen
    Leonard D. Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, California, United States of America.
  • Alicia H Chang
    Los Angeles County Department of Public Health, Tuberculosis Control Program, Los Angeles, California, United States of America.

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