Data-driven development of clinically translatable EHR-based models to estimate severe mood episode risk for young people with bipolar disorder

The National Institute of Mental Health awarded HealthPartners Institute $199,747 on April 8, 2026, under the Mental Health Research Grants program (CFDA 93.242) to develop machine-learning-driven electronic health record-based predictive models that identify young people with bipolar disorder at risk for severe mood episodes and support clinical decision-making to prevent crisis events.

The award funds a mentored K23 research program led by a PhD-prepared psychiatric nurse practitioner with expertise in early intervention for bipolar disorder and data-driven analytic approaches. The candidate will use longitudinal clinical and administrative data from HealthPartners and Kaiser Permanente Northern California within the Mental Health Research Network to build advanced predictive analytics and machine learning models. The program combines training in predictive analytics, mixed methods for stakeholder-engaged intervention development, embedded health systems research, and bipolar disorder clinical epidemiology with development of a deployment-focused clinical decision support tool designed for rapid translation to clinical care settings. Work is performed in Bloomington, Minnesota, with a period of performance from April 8, 2026, through March 31, 2031.

Abstract

Bipolar disorder (BD) is among the deadliest and most costly psychiatric disorders in young people due to its severe and recurrent mood episodes of depression and mania which disrupt functioning, substantially increase the risk for suicide and premature death, and frequently require emergency or inpatient care. As each subsequent mood episode worsens prognosis, prevention of severe mood events in young people with BD is central to mitigating its enormous personal and societal burden. However, prevention is hindered by the lack of widely deployable tools to identify which affected individuals are at risk of a severe mood crisis event within a specific interval and which can guide individualized care. Through this mentored K23 award, the candidate, a PHD-prepared psychiatric nurse practitioner, will build upon her background in early intervention for BD, data- driven analytic approaches, and qualitative methods. Her program of training and research are designed to leverage real-world data and advanced analytic machine learning methods to efficiently identify young individuals with BD at risk for severe mood events and develop a deployment-focused clinical decision support intervention in partnership with clinicians and patients that could be rapidly translated to clinical care (nimh strategic objectives 4.1 and 4.2). Through planned training activities, the candidate will gain a strong skillset in advanced predictive analytics and machine learning using electronic health record (EHR) and administrative data, mixed methods for stakeholder engaged intervention development, embedded health systems research, and BD clinical epidemiology. She will leverage robust, longitudinal health system data from two learning healthcare systems in the mental health research network, healthpartners and kaiser permanente northern california, and engagement with clinicians and patients where care is delivered. In aim 1, rigorous machine learning methods will be used to estimate risk of severe mood crisis events, as indicated by mood-related inpatient hospitalization or emergency visits, over six-month intervals based on rich longitudinal EHR and claims data in a large sample of over 13,200 young patients with BD aged 15-39 years. In aim 2, to maximize the translational impact of the models, clinicians and patients will be engaged, using a modified delphi approach and qualitative interviews, in development and evaluation of a clinical decision support tool to guide personalized prevention and early intervention for BD mood crises. This research is a critical step in the candidate's long-term goal of leveraging data-driven approaches to improve individualized, patient-centered delivery of mental health services for individuals in the early course of BD and other serious mental illnesses. Her clinical and research background, expert mentoring team, and embedded research environment ideally positions her to accomplish the research and training aims, building the foundation for a next-step R01 that will externally validate and rigorously evaluate the risk prediction models and decision support tool developed in this proposal.