Psychosis is a major public health challenge, with approximately 100,000 adolescents and young adults in the US experiencing a first episode of psychosis (FEP) every year. Early intervention following FEP is critical for achieving improved outcomes, yet treatment of FEP is often delayed between 1 and 3 years in the US due to delays in detection and referral. The World Health Organization has advocated shortening the duration of untreated psychosis (DUP) to three months or less. The goal of this study is to develop and validate a universal EHR-based screening tool for early detection of FEP across large clinical populations in diverse healthcare settings. In order to maximize the impact and generalizability of the tool across a wide range of healthcare settings, we will rely only on coded medical information collected in the course of care and thus widely available in EHRs. The tool will be developed and validated with data from three diverse health systems that cover over 8 million patients spanning a wide range of demographic, socioeconomic and ethnic backgrounds: Partners Healthcare System, Boston Children's Hospital, and Boston Medical Center. The study will be conducted by a closely collaborating interdisciplinary team of clinical specialists, psychosis researchers, and risk modeling experts based at these health systems and Harvard Medical School, with extensive experience in treating psychosis patients, and developing strategies for detecting FEP and EHR-based risk screening tools for early detection of various clinical conditions. Our preliminary studies show that EHR-based risk models can be used to sensitively and specifically detect FEP cases, on average 2 years before the first psychosis diagnosis appears in their EHR.
Our specific aims i nclude: 1. Define a robust cross-site case definition for FEP that relies only on information commonly available in EHRs and validate it through expert chart review; 2. Train and validate a predictive model for early detection of FEP based on large samples of patient data from the three sites; 3. Develop and validate FEP early detection models for key subpopulations, including patients receiving care at mental health clinics, adolescent medicine outpatient programs, and substance abuse treatment programs; and 4. Engage clinical stakeholders in the process of developing a prototype clinician-facing EHR-based risk screening tool for FEP, and release it as an open source SMART App, enabling further validation and clinical integration across a wide range of healthcare settings. Completion of these aims would provide a novel, clinically deployable, and potentially transformative tool for improving the trajectory of those affected with psychosis and reducing the burden and costs of untreated illness.

Public Health Relevance

Psychosis is a major public health challenge, with difficulties and delays in detecting its onset that can lead to worse clinical outcomes. The proposed research will develop a clinician-facing electronic-health-record-based automated screening tool for early detection of the first episode of psychosis, with implications for reducing the duration of untreated psychosis as recommended by the NIMH and World Health Organization. The tool will be validated across three large and diverse health systems and released as an open source application (SMART App), increasing its potential for rapid implementation in health systems and clinical care.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Biomedical Computing and Health Informatics Study Section (BCHI)
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Azrin, Susan
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Massachusetts General Hospital
United States
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