Improving Adherence and Outcomes by Artificial Intelligence-Adapted Text Messages

Self-management of chronic conditions involves complex behaviors, and patients vary in their adherence to these behaviors. The focus of this proposal is medication adherence because patients' failure to take their medications as prescribed is a major cause of excess morbidity and mortality and increased health care costs. Studies suggest that 33-50% of patients do not take their medications properly, contributing to nearly 100,000 premature deaths each year and $290 billion in health care costs. Adherence to antihypertensive medications is of particular importance in its own right, and hypertension can serve as an important tracer condition to better understand and improve medication adherence more generally. Uncontrolled hypertension is a major cause of stroke, coronary heart disease, heart failure and mortality, and medication non-adherence is a major cause of uncontrolled hypertension. For example, in a one-year study of ~5,000 hypertensive patients, most patients took their medications only intermittently with half of patients eventually discontinuing their medications against medical advise.

Improving medication adherence requires addressing multiple challenges because patients typically have a variety of reasons for not taking their medication as prescribed, such as beliefs about their disease and its treatment, organizational challenges, and cost barriers. Moreover, as patients' regimens, health status, and social context change over time, adherence support interventions need to adapt, but most services lack the flexibility to do so.

Mobile health (mHealth) services such as patient text messaging or SMS have shown some promise in improving medication adherence. However, since almost all mHealth services are based on simplistic, deterministic protocols, these interventions lack the capacity to meet patients' complex changing needs. As a consequence, these rudimentary systems have demonstrated only modest effects that tend to decrease over time. The investigators propose to apply artificial intelligence (AI) methods, specifically Reinforcement Learning (one type of AI), to develop a model medication adherence system that can automatically adapt SMS communication to improve individual medication taking.

The proposed project is the result of a new multidisciplinary collaboration between UM experts from the College of Pharmacy, College of Engineering, and School of Medicine. Our long-term goal is to improve health outcomes using artificial intelligence (AI) enhanced mobile health tools. The objective in the proposed pilot study is to develop a Reinforcement Learning-based mHealth program focused on medication adherence among patients with poorly controlled hypertension. Our central hypotheses are that a SMS system that uses Reinforcement Learning (RL) will: be acceptable to patients, adapt to hypertension patients' unique adherence-related needs and preferences and changes in these needs over time, and improve medication adherence and blood pressure control. The specific aims are:

1. Develop RL methods for adaptive decision-making in human-centered environments and demonstrate the feasibility of the resulting RL-based adaptive SMS medication adherence intervention,
2. Demonstrate "learning" by the RL-base adaptive system using data showing adaptation of the SMS message stream according to variation across patients and over time in the reasons for non-adherence, and
3. Examine the potential efficacy of the RL-based adaptive SMS intervention with respect to improvements in medication adherence and systolic blood pressure.