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.

The results of this pilot project will include a novel AI/RL technology and evidence
regarding its real-world use based on experience with a sample of adults with poorly
controlled hypertension. These results will be used to support an R01 application for a
larger and more definitive study of the intervention's impact on patients' health and
long-term adherence behaviors. Over the longer term, this AI-enhanced mHealth
self-management support infrastructure and unprecedented collaboration between investigators
in Pharmacy, Medicine, and Computer Science will lay the foundation for a larger program of
NIH-funded research using similar AI approaches to addressing behavior change challenges in
a large number of health and healthcare problems.

Inclusion Criteria:

- Patient must have Priority Health Care Health Insurance Coverage

- Patient must have PDC of < 0.5 for anti-hypertensive medications

Exclusion Criteria:

- No hypertension medicine currently taken

- Patient doesn't text message (no cell phone) in an average week

- No access to the internet

- Patient has heart failure which makes it difficult to catch breath and move around

- Patient uses artificial oxygen to breathe

- Patient is currently under treatment for cancer

- Patient currently has kidney disease that requires dialysis

- Patient self reports a mental health diagnosis (from a health professional)

- Patient reports having schizophrenia

- Patient reports currently being treated bipolar disorder or manic-depressive illness
or schizophrenia

- Patients reports ever been diagnosed with dementia or Alzheimer's disease