Using predictive risk modelling to identify patients with hidden health needs in an Aboriginal and Torres Strait Islander health service.

Abstract

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. 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. According to the standard metrics, the PRMs were good at predicting health checks but showed low reliability for detecting poor wellbeing. Results and feedback from clinicians were encouraging. With additional refinement, informed by clinic staff feedback, a deployable model should befeasible.