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Background and objectiveIn partnership with an Aboriginal and Torres Strait Islander communitycontrolled 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. MethodsUsing 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. ResultsAccording to the standard metrics, the PRMs were good at predicting health checks but showed low reliability for detecting poor wellbeing. DiscussionResults and feedback from clinicians were encouraging.With additional refinement, informed by clinic staff feedback, a deployable model should be feasible.Using predictive risk modelling to identify patients with hidden health needs in an Aboriginal and Torres Strait Islander health service Research
Tennakoon et al. (Fri,) studied this question.