Using Electronic Health Records to Facilitate Precision Psychiatry

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This study evaluates how advanced clinical prediction models can improve the identification of people at increased risk of developing psychosis by combining routinely collected healthcare data with digital technologies.

The researchers explored how real-world clinical information can be used to generate more accurate and personalised risk estimates, supporting clinicians in recognising individuals who may benefit from earlier assessment and intervention. The findings demonstrate the potential of data-driven approaches to strengthen preventive mental healthcare while making risk prediction more scalable and applicable in routine clinical practice.

By advancing the use of digital prediction models, this research contributes to the broader goals of ePreventPsych: supporting earlier detection, improving clinical decision-making and promoting more personalised approaches to mental health prevention.

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