# Development and validation of an electronic health record prediction tool for first-episode psychosis

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $847,935

## Abstract

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 include: 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.

## Key facts

- **NIH application ID:** 9864102
- **Project number:** 5R01MH116042-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Ben Y Reis
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $847,935
- **Award type:** 5
- **Project period:** 2019-02-05 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9864102

## Citation

> US National Institutes of Health, RePORTER application 9864102, Development and validation of an electronic health record prediction tool for first-episode psychosis (5R01MH116042-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9864102. Licensed CC0.

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