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dc.contributor.authorBoutilier, Justin J
dc.contributor.authorJónasson, Jónas Oddur
dc.contributor.authorYoeli, Erez
dc.date.accessioned2022-09-14T14:59:18Z
dc.date.available2022-09-14T14:59:18Z
dc.date.issued2021
dc.identifier.urihttps://hdl.handle.net/1721.1/145407
dc.description.abstract<jats:p> 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 been mixed; 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-horizon machine 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 TAS may substantially improve the cost-effectiveness of TAS programs. </jats:p>en_US
dc.language.isoen
dc.publisherInstitute for Operations Research and the Management Sciences (INFORMS)en_US
dc.relation.isversionof10.1287/MSOM.2021.1046en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceProf. Jonassonen_US
dc.titleImproving Tuberculosis Treatment Adherence Support: The Case for Targeted Behavioral Interventionsen_US
dc.typeArticleen_US
dc.identifier.citationBoutilier, Justin J, Jónasson, Jónas Oddur and Yoeli, Erez. 2021. "Improving Tuberculosis Treatment Adherence Support: The Case for Targeted Behavioral Interventions." Manufacturing and Service Operations Management.
dc.contributor.departmentSloan School of Managementen_US
dc.relation.journalManufacturing and Service Operations Managementen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2022-09-14T14:54:15Z
dspace.orderedauthorsBoutilier, JJ; Jónasson, JO; Yoeli, Een_US
dspace.date.submission2022-09-14T14:54:17Z
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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