Aims: Medication cessation and service disengagement often precedes relapse in people with severe mental illnesses but currently specialist mental health services only become involved after a relapse. Early detection of non-adherence is needed to enable intervention to avert relapse. This paper aims to demonstrate how digitally automated non-adherence risk monitoring from Medicare data with active follow-up can work and perform in practice in a real-world mental health service setting. Methods: AI2 software is an automated risk monitoring tool to detect non-adherence using Medicare data. It was implemented prospectively in a cohort of 354 registered patients of a community mental health clinic between July 2019 and February 2020. Patients flagged as at risk by the software were reviewed by two clinicians. We describe the risks automatically flagged for non-adherence and the clinical responses. We examine differences in clinical and demographic factors in patients flagged at increased risk of non-adherence. Results: In total, 46.7% (142/304) were flagged by the software as at risk of non-adherence, and 22% (31/142) received an intervention following clinician review of their case notes. Patients flagged by the software were older in age and had more prior mental health treatment episodes. More alerts were associated with patients who had been transferred from the mental health service to the care of their general practitioners, and those with more alerts were more likely to receive a follow-up intervention. Conclusion: Digitally automated monitoring for non-adherence risk is feasible and can be integrated into clinical workflows in community psychiatric and primary care settings. The technology may assist clinicians and services to detect non-adherence behaviour early, thereby triggering interventions that have the potential to reduce rates of mental health deterioration and acute illness relapse.
- Digital health