The overarching goal of the Brigham and Women’s Hospital / Harvard Medical School Roybal Center for Therapeutic Optimization using Behavioral Science is to develop principle-driven interventions to enhance the evidence-based use of prescription medications. The Center is directed by C4HDS Executive Director, Niteesh Choudhry, MD, PhD, and is overseen by an Executive Committee that includes Elad Yom-Tov, PhD (Bar-Ilan University), Ted Robertson, MPA (Berkeley), and Alia Crum (Stanford).
roybal projects
The Roybal Center for Therapeutic Optimization Using Behavioral Science is supported by National Institute on Aging, Grant # P30AG064199.
To see our Roybal affiliated publications, visit Roybal Publications.
The structure and activities of the BWH Roybal Center are based on 4 key principles:
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a multi-disciplinary approach integrating collaborators with expertise in medication use, theory-based behavioral science, implementation research, and data science
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the testing of principle-driven interventions in real-world settings
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the explicit testing of mechanisms of action for each intervention
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the use of novel analytic methods and technological approaches to gain a deeper understanding of behavior and to facilitate the creation of interventions that are both personalized but applicable to population health improvement
The NIA’s Division of Behavioral and Social Research supports several Roybal Centers across the nation, as well as a Coordinating Center.
Reinforcement learning to personalize message framing for healthy habits (REINFORCE)
This project conducted a randomized trial to evaluate the effectiveness of using reinforcement learning, a machine-learning based approach, to optimize and personalize medication adherence health communication for patients with diabetes.
Year
1 (2019-2020)
Personalizing interventions to reduce clinical inertia in the treatment of hypertension
This project tested the impact of social norming and social marketing on clinical inertia for hypertension and sought to empirically identify the relationship between patient features, physician behavioral characteristics and intervention responsiveness.
Year
2 (2020-2021)
Novel application of simulation for providers to overcome the hot-cold empathy gap in high-risk medication prescribing
This project sought to evaluate whether a simulation-based approach could address the hot-cold empathy gap and improve the prescribing of medications such as antipsychotics and benzodiazepines to hospitalized older adults.
Year
2 (2020-2021)
Breaking implicit bias habits: An individuation pilot to promote equity in rheumatic disease care
The goal of this project is to test the efficacy of an individuation-based intervention among rheumatologists and racially discordant patients at two large, multisite practices to improve racial and SES equity in receipt of high-quality care.
Year
3 (2021-2022)
Refinement and adaption of reinforcement learning to personalize behavioral messaging for healthy habits (REINFORCE2)
This project aims to adapt and test the impact of the reinforcement learning intervention tested in the REINFORCE trial to a diverse community setting.
Year
4 (2022-2023)
Addressing diffusion of responsibility and prescribing burden to improve use of diabetes medications
This project tested whether an intervention targeting diffusion of responsibility with and without additional resources to reduce the administrative burden of prescribing improved the use of evidence-based medications for diabetes in primary care.
Year
4 (2022-2023)
Refinement and testing of recruitment methodology for behavioral medication adherence intervention using behavioral science-based approaches
This project aims to test whether incorporating construal-level and prospect theories will facilitate patient engagement in a multi-component pharmacist intervention developed as part of the NIH Stage IV STIC2IT trial.
Year
5 (2023-2024)
Social norms, messengers, and processing fluency to increase hypertension medication adherence
This large NIH Stage III study aims to test whether social norms, messenger effects, and processing fluency improve hypertension medication adherence among Medicare Advantage beneficiaries.
Year
5 (2023-2024)
Using reinforcement learning to personalize electronic health record tools to facilitate deprescribing
This project aims to refine and adapt (NIH Stage I), and perform real-world efficacy testing (NIH Stage III) of a novel reinforcement learning-based approach to personalizing EHR based tools for PCPs on deprescribing of high-risk medications.
Year
6 (2024-2025)
Impact of a Behaviorally Designed Gamification Intervention on Adherence to Medication for Cardiometabolic Disease
This project aims to evaluate the effectiveness of using a gamification intervention on cholesterol medication adherence (NIH Stage III) and to conduct a mechanistic evaluation determining pathways through which the gamification intervention could improve adherence and classify patient responsiveness to gamification tactics (NIH Stage I).