Seminar – Personalising interventions in behavioural health

Professor Jónas Oddur Jónasson, MIT Sloan School of Management

Problem Definition: Lack of patient adherence to treatment protocols is a main barrier to reducing the global disease burden of tuberculosis (TB). Using data from a completed RCT, we study the operational design of a treatment adherence support (TAS) platform that requires patients to verify their treatment adherence on a daily basis, with a focus on improving personalisation.

Methods: We first focus on personalised enrollment. We trained a causal forest model to answer three research questions: (1) Was the effect of the intervention heterogeneous across individuals? (2) Was the intervention less effective for high-risk patients? (3) Can differentiated care improve program effectiveness and equity in treatment outcomes? We then focus on personalised outreach. Inspired by reinforcement learning, we provide a model-free approach to solving the problem of optimising personalised interventions for patients to maximise some long-term outcome, in a setting where interventions are costly and capacity-constrained.

Results: For personalised enrollment, we find that individual intervention effects—the percentage point reduction in the likelihood of an unsuccessful treatment outcome—ranged from 4.2 to 12.4, with an average of 8.2. The intervention was beneficial for 76% of patients, and most beneficial for high-risk patients. Differentiated enrolment policies, targeted at high-risk patients, have the potential to (1) increase the average intervention effect of DAT services by up to 28.5% and (2) decrease the population average and standard deviation (across patients) of the probability of an unsuccessful treatment outcome by up to 8.5% and 31.5%, respectively. For personalised outreach, we show that under a natural set of structural assumptions on patient dynamics, our approach recovers at least 1/2 of the improvement possible between a naïve baseline policy and the optimal policy. At the same time, our policy is both robust to estimation errors and interpretable. Numerically, we find that our policy can provide the same efficacy as the status quo with approximately half the capacity for interventions.

The talk is based on the following three papers:

• “Policy Optimization for Personalized Interventions in Behavioral Health” (working paper) by Jackie Baek, Justin Boutilier, Vivek Farias and Jónas Oddur Jónasson
• “Can digital adherence technologies reduce inequity in tuberculosis treatment success? Evidence from a randomized controlled trial”, BMJ Global Health (2022) by Justin J. Boutilier, Erez Yoeli, Jon Rathauser, Philip Owiti, Ramnath Subbaraman, and Jónas Oddur Jónasson.
• “Improving TB Treatment Adherence Support: The Case for Targeted Behavioral Interventions”, Manufacturing and Service Operations Management (2022) by Justin J. Boutilier, Jónas Oddur Jónasson, and Erez Yoeli.

Speaker bio

Jónas Oddur Jónasson is the Robert G. James Career Development Associate Professor in Operations Management and an Associate Professor of Operations Management at the MIT Sloan School of Management. His primary research focus is on understanding and improving the effectiveness and efficiency of decentralized healthcare delivery systems. His research has been published in Operations Research, Management Science, and Manufacturing and Service Operations Management. Jónas was awarded the 2014 Bonder Scholarship for applied Operations Research in Health Services and received first prizes in the 2014 INFORMS Manufacturing & Service Operations Management (MSOM) student paper competition as well as the 2021 MSOM Practice-Based Research Competition. Prior to joining MIT, Jónas received his PhD in management science and operations from London Business School, an MPhil from the University of Cambridge, an MSc from the London School of Economics and Political Science, and his BSc from the University of Iceland.

For more information, please contact with Luke Slater.