Leveraging Digitization, Archiving and Artificial Intelligence to Re-examine Predictors of Sustained Mental Health Care Engagement in Ugandan First-Episode Psychosis Patients: A Study Protocol

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Abstract
Background: We previously examined the burden and predictors of sustained mental health care engagement in Ugandan first episode psychosis patients by retrospective chart review methods. However, the extensive requirements of chart reviews meant that we could only extract data from a random 10% sample of 1,677 newly enrolled Ugandan first episode psychosis patients at Butabika National Referral Mental Hospital in 2018. The Hekima Platform has been designed to transform handwritten files into datasets for analysis. Objectives: This study aims to: (1) utilize the Hekima Platform to transform paper-based clinical charts of all 1,677 Ugandan psychosis patients enrolled at Butabika Hospital for the first time in 2018 into a standardized, anonymized longitudinal database; and (2) re-examine predictors of sustained MHC engagement in this cohort. Methods: We will digitize and archive all patient charts. We will then use the Hekima Platform to extract handwritten clinical data into machine-readable text using user-trained machine learning and deep learning models and natural language processing (NLP) techniques to generate a structured, anonymized database. A minimum 10% random sample of extracted data will be manually validated using Cohen’s kappa. For the analytical aim, descriptive statistics, bivariate analysis, and multivariable logistic regression will model predictors of sustained engagement, with exploratory machine learning approaches used as a complementary analytical strategy. Ethical approval has been obtained from the Uganda National Council for Science and Technology and Butabika Hospital’s Research Ethics Committee. Expected outcomes: Patient clinical charts are a rich data source but there are extensive requirements to be able to use them for research. This study will generate the first AI-assisted, standardized longitudinal database from handwritten psychiatric records in Uganda, enabling well-powered analyses of predictors of MHC engagement. Findings will inform targeted interventions to improve retention in care and will offer a scalable model for mental health research in low- and middle-income countries.

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