# Improving emergency psychiatric care through machine learning, causal inference, and psychometrics

> **NIH NIH K01** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $181,346

## Abstract

Background: The United States is experiencing a crisis in mental health care, in which long-term shifts
towards the emergency department (ED) for psychiatric treatment have been exacerbated by the COVID-19
pandemic – reduced inpatient hospitalization capacity, delayed outpatient appointments, extended ED
boarding, and staffing shortages, combined with substantial growth in demand for mental health services (due
in part to pandemic-related increases in stress, depression, social isolation, and anxiety, and with differential
impacts on vulnerable populations). Data-driven approaches have not been widely applied to understand
mechanisms and predictors of ED utilization for psychiatric patients, or to optimize clinical decision-making to
improve patient outcomes. Research: In this study, I will conduct secondary data analysis of 141,431 ED visits
for psychiatric care in a large regional health system from 2017-2023, extracting and preparing over 5,000
patient characteristics from the electronic health record (diagnoses, labs, medications, procedures, visits, and
notes). Aim 1: I will create a transdiagnostic subtyping system, using latent class analysis, hierarchical density-
based clustering, and neural network autoencoders, to reliably categorize and track major trends in patient
populations receiving emergency psychiatric care. Aim 2: I will develop a suite of ensemble machine learning
models to predict ED length of stay, ED boarding duration, short-term ED re-admission, transfer to inpatient
hospitalization, and post-discharge adverse events (suicide attempt, overdose, or psychotic episode). Aim 3: I
will estimate a precision treatment rule, using targeted causal inference and ensemble machine learning, to
capture heterogeneous treatment effects for primary ED disposition decisions (inpatient hospitalization, partial
hospitalization / intensive outpatient program, or outpatient monitoring) on adverse psychiatric events.
Candidate's Career Development, Goals, and Environment: This proposal's research aims and the
candidate's career development will be supported by the extensive resources available at Massachusetts
General Hospital (MGH) and Harvard Medical School, as well as formal training, coursework, and mentorship
in (T1) mental disorders and psychopathology, (T2) precision treatment optimization, (T3) hybrid pragmatic
effectiveness trials, and (T4) professional development in preparation for a future R01 submission. The
mentorship team includes Primary Mentor Dr. Jordan Smoller, a leading expert in precision psychiatry; Co-
Mentors Dr. Matthew Nock, a leader in mental disorders and suicide prevention; Dr. Susan Murphy, a leader in
precision treatment optimization; and Dr. Stephen Bartels, a leader in implementation science and hybrid
pragmatic trials; and Consultants Dr. Suzanne Bird, expert in emergency psychiatry and director of MGH Acute
Psychiatry Services; Dr. Edwin Boudreaux, expert in hybrid pragmatic trials in emergency psychiatry; an...

## Key facts

- **NIH application ID:** 10783889
- **Project number:** 1K01MH135131-01
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Christopher James Kennedy
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $181,346
- **Award type:** 1
- **Project period:** 2023-12-01 → 2027-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10783889

## Citation

> US National Institutes of Health, RePORTER application 10783889, Improving emergency psychiatric care through machine learning, causal inference, and psychometrics (1K01MH135131-01). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10783889. Licensed CC0.

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