Integrating mHealth for Alcohol Use Disorders into Clinical Practice

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Project Summary/Abstract Mobile health (mHealth) technologies (e.g., smartphone applications) have the potential to continuously monitor patients and deliver interventions when and where they are needed. These features make mHealth particularly promising for the treatment of alcohol and other substance use disorders. Patient-provided data from mHealth devices could also help clinicians plan treatment and respond to patients. Yet mHealth is rarely integrated into treatment, in part because so few evidence-based mHealth systems are available. This project addresses the potential of using mHealth to treat alcohol use disorder (AUD) in primary care clinics. Two questions drive the project: (1) Is A-CHESS, an mHealth system that has been proven effective for patients leaving residential treatment for AUD, effective with patients in primary care? (2) Are the costs associated with integrating A-CHESS into clinical processes worthwhile, or does it suffice for patients to use A-CHESS independently, without integration into the electronic health record and clinician monitoring? The project uses a Type 1 hybrid design (examining both patient outcomes and implementation) to answer these questions. Patients will be randomly assigned to (1) usual care for AUD, (2) a clinician-mediated group in which patients receive A-CHESS and clinicians monitor patient-supplied A-CHESS data from a dashboard integrated into the electronic health record, or (3) a patient-directed group in which clinicians simply encourage patients to use A-CHESS on their own. The quantitative analysis will examine differences between the groups in risky drinking days and quality of life to test the hypothesis that patients in the clinician-mediated group will have greater improvements than patients in the usual care group. Additionally, we hypothesize that patients in both A-CHESS groups will have greater improvements in risky drinking days and quality of life vs. patients in the usual care group, and those in the clinician-mediated group will see the greatest improvements. Subgroup analyses will be conducted to understand the relationship between the outcomes and (1) gender, (2) patient and clinician responses to alerts generated by A-CHESS, and (3) use of A-CHESS. The qualitative analysis will mainly seek to understand how clinicians use the dashboard to monitor patient care. Semi- structured interviews will inquire about the amount of dashboard use, potential contamination (whether clinicians used approaches suggested by the dashboard with patients in the other two groups), and the quality of intervention delivery. Finally, the cost analysis will determine the cost of integrating A-CHESS into clinical practice vs. the cost of patients using A-CHESS independently, and whether the cost of clinically integrating A- CHESS is offset by savings from reduced AUD-related care (e.g., hospitalizations, ER visits, residential treatment, and detoxification). This research will inform clinical leaders and policymakers on whether and how mHealth systems should be incorporated into clinical care to treat AUD and potentially other chronic diseases.