ABSTRACT
BACKGROUND AND OBJECTIVES: In partnership with an Aboriginal and Torres Strait Islander community-controlled health service, we explored the use of a machine learning tool to identify high-needs patients for whom services are harder to reach and, hence, who do not engage with primary care. METHOD: Using deidentified electronic health record data, two predictive risk models (PRMs) were developed to identify patients who were: (1) unlikely to have health checks as an indicator of not engaging with care; and (2) likely to rate their wellbeing as poor, as a measure of high needs. RESULTS: According to the standard metrics, the PRMs were good at predicting health checks but showed low reliability for detecting poor wellbeing. DISCUSSION: Results and feedback from clinicians were encouraging. With additional refinement, informed by clinic staff feedback, a deployable model should be feasible.
