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The use of clinical prediction models to produce individualised risk estimates can facilitate the implementation of precision psychiatry. As a source of data from large, clinically representative patient samples, electronic health records (EHRs) provide a platform to develop and validate clinical prediction models, as well as potentially implementing them in routine clinical care. The present review describes promising use cases for the application of precision psychiatry to EHR data and considers their performance in terms of discrimination (ability to separate individuals with and without the outcome) and calibration (extent to which predicted risk estimates correspond to observed outcomes), as well as their potential clinical utility (weighing benefits and costs associated with the model compared to different approaches across different assumptions of the number-needed-to-test). We review four externally validated clinical prediction models designed to predict, respectively: psychosis onset, psychotic relapse, cardiometabolic morbidity, and suicide risk. We then discuss the prospects for clinically implementing these models, and the potential added value of integrating data from evidence syntheses, standardised psychometric assessments, and biological data into EHRs. Clinical prediction models can utilise routinely collected EHR data in an innovative way, representing a unique opportunity to inform real-world clinical decision making. Combining data from other sources (e.g. meta-analyses) or enhancing EHR data with information from research studies (clinical and biomarker data) may enhance our abilities to improve performance of clinical prediction models.

Original publication




Journal article


Biol Psychiatry

Publication Date



electronic health records, implementation, precision psychiatry, prediction modelling, psychosis, suicide