Improving Tuberculosis Treatment Adherence Support: The Case for Targeted Behavioral Interventions

• 成果类型: Article
• 署名作者: Boutilier, Justin J.; Jonasson, Jonas Oddur; Yoeli, Erez
• 署名单位: University of Wisconsin System; University of Wisconsin Madison; Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT)
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2021.1046
• 发表日期: 2022
• 页码: 2925-2943
• 关键词: Behavioral Operations; Empirical Research; Global Operations Management; healthcare management; nonprofit management

摘要

Problem definition: Lack of patient adherence to treatment protocols is a main barrier to reducing the global disease burden of tuberculosis (TB). We study the operational design of a treatment adherence support (TAS) platform that requires patients to verify their treatment adherence on a daily basis. Academic/practical relevance: Experimental results on the effectiveness of TAS programs have beenmixed; and rigorous research is needed on how to structure these motivational programs, particularly in resource-limited settings. Our analysis establishes that patient engagement can be increased by personal sponsor outreach and that patient behavior data can be used to identify at-risk patients for targeted outreach. Methodology: We partner with a TB TAS provider and use data from a completed randomized controlled trial. We use administrative variation in the timing of peer sponsor outreach to evaluate the impact of personal messages on subsequent patient verification behavior. We then develop a rolling-horizonmachine learning (ML) framework to generate dynamic risk predictions for patients enrolled on the platform. Results: We find that, on average, sponsor outreach to patients increases the odds ratio of next-day treatment adherence verification by 35%. Furthermore, patients' prior verification behavior can be used to accurately predict short-term(treatment adherence verification) and long-term(successful treatment completion) outcomes. These results allow the provider to target and implement behavioral interventions to at-risk patients. Managerial implications: Our results indicate that, compared with a benchmark policy, the TAS platform could reach the same number of at-risk patients with 6%-40% less capacity, or reach 2%-20% more at-risk patients with the same capacity, by using various ML-based prioritization policies that leverage patient engagement data. Personal sponsor outreach to all patients is likely to be very costly, so targeted TASmay substantially improve the cost-effectiveness of TAS programs.