IntroductionNon-adherence to antipsychotic medications for individuals with serious mental illness increases risk of relapse and hospitalisation. Real time monitoring of adherence would allow for early intervention. AI2 is a both a personal nudging system and a clinical decision support tool that applies machine learning on Medicare prescription and benefits data to raise alerts when patients have discontinued antipsychotic medications without supervision, or when essential routine health checks have not been performed.Methods and analysisWe outline two intervention models using AI2. In the first use-case, the personal nudging system, patients receive text messages when an alert of a missed medication or routine health check is detected by AI2. In the second use-case, as a clinical decision support tool, AI2 generated alerts are presented as flags through a dashboard to the community mental health professionals. Implementation protocols for different scenarios of AI2, along with a mixed-methods evaluation, are planned to identify pragmatic issues necessary to inform a larger randomised control trial, as well as improve the application.Ethics and disseminationThis study protocol has been approved by The Southern Adelaide Clinical Human Research Ethics Committee. The dissemination of this trial will serve to inform further implementation of the AI2 into daily personal and clinical practice.

The current COVID-19 pandemic has highlighted the limitations of relying solely on in-person contact for diagnosis, monitoring and treatment of mental health conditions. Mobile health approaches can be used to monitor mental health patients remotely, but they are not properly integrated with existing models of healthcare service delivery. We present findings from a case study of a mobile app enabled cloud-based software program rolled out in a phone based psychological service to enable real-time/temporal monitoring. The program offered patients an app to record measures of symptoms in everyday contexts and provided clinicians with access to an accompanying dashboard to use information from the app to tailor treatments and monitor progress and ultimately facilitate earlier and personalised care decisions. Feedback related to implementation and utility was gathered from clinicians through a focus group conducted two months post-roll-out. Findings identified that the system is valuable and feasible, however implementation issues were identified. These are discussed in order to inform future work in this area to support the delivery of timely and responsive mental health care in the community.