Development and validation of a predictive model for high-intensity mental health service use using electronic health record data
File(s)
Author(s)
Chada, Bharadwaj V
Stewart, Robert
Lai, James
Type
Journal Article
Abstract
Aims and method
This study aimed to develop and evaluate a predictive model using electronic health record (EHR) data from a large south London mental health service, in order to identify patients 3 months following first referral who are at risk of subsequent high-intensity service use over the subsequent 12 months. Early identification of such patients may support proactive and personalised care planning, reducing the need for high-cost episodes of care. Predictive models were developed using information from 18 869 patients newly referred between 2007 and 2011. High-intensity use was defined as the top 10% of estimated mental healthcare expenditure. The model was developed using demographic, clinical and service use variables, and was validated on data from the periods 2012–2017 and 2018–2023.
Results
A logistic regression model achieved an area under the receiver operating characteristic (AUROC) of 0.79 in development (sensitivity 0.82, specificity 0.54), with robust performance in validation sets (AUROC 0.81, 0.83, respectively). Key predictors included first 3 months service use, schizophrenia or eating disorder diagnoses and living alone. Natural language processing-derived features did not improve performance.
Clinical implications
Routine EHR data performed well in predicting the risk of high-cost care, potentially enabling targeted interventions and more efficient resource allocation.
This study aimed to develop and evaluate a predictive model using electronic health record (EHR) data from a large south London mental health service, in order to identify patients 3 months following first referral who are at risk of subsequent high-intensity service use over the subsequent 12 months. Early identification of such patients may support proactive and personalised care planning, reducing the need for high-cost episodes of care. Predictive models were developed using information from 18 869 patients newly referred between 2007 and 2011. High-intensity use was defined as the top 10% of estimated mental healthcare expenditure. The model was developed using demographic, clinical and service use variables, and was validated on data from the periods 2012–2017 and 2018–2023.
Results
A logistic regression model achieved an area under the receiver operating characteristic (AUROC) of 0.79 in development (sensitivity 0.82, specificity 0.54), with robust performance in validation sets (AUROC 0.81, 0.83, respectively). Key predictors included first 3 months service use, schizophrenia or eating disorder diagnoses and living alone. Natural language processing-derived features did not improve performance.
Clinical implications
Routine EHR data performed well in predicting the risk of high-cost care, potentially enabling targeted interventions and more efficient resource allocation.
Date Issued
2026-01-20
Date Acceptance
2025-12-11
Citation
BJPsych Bulletin, 2026
ISSN
2056-4694
Publisher
Royal College of Psychiatrists
Journal / Book Title
BJPsych Bulletin
Copyright Statement
© The Author(s), 2026. Published by Cambridge University Press on behalf of Royal College of Psychiatrists This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
License URL
Publication Status
Published online
Date Publish Online
2026-01-20