# Predictive Analytics Applied to Integrated Administrative Emergency Response Datasets in Chicago - Resubmission 01

> **NIH NIH R01** · UNIVERSITY OF CHICAGO · 2021 · $397,365

## Abstract

Project Summary
Individuals in behavioral crisis frequently come into contact with law enforcement and other first responders. In
Chicago, the study cite, emergency room visits for people experiencing psychiatric crisis increased by 19 percent
between 2009 and 2012, and 32 percent of the roughly 14,000 homeless people on any given night in Illinois
have a serious mental illness. Increasingly, police are often the first to respond to mental and behavioral health
crises. Unfortunately, these encounters have a higher chance of escalating into a violent or otherwise harmful
outcome than other emergency situation. In response to several high-profile, negative encounters between
Chicago police and persons with mental illness, including the tragic police shooting of a young man experiencing
a behavioral health (BH) crises, Chicago has committed to improving BH emergency response. The city is
implementing several proactive interventions aimed a deescalating BH crises, including increasing the number
of CIT-trained police officers and deploying co-mobile response teams. However, an ongoing challenge for
agencies is determining which emergency calls have a BH component and therefore should be prioritized for
tailored interventions like CIT and co-mobile teams. Additionally, without accurate baseline information, leaders
cannot fully understand the impact of implementing such initiatives. The present project seeks to acquire and
link retrospective administrative datasets from the three city agencies involved in emergency first response:
Chicago Police Department, Chicago Fire Department and the Office of Emergency Management and
Communication. Establishing an encompassing dataset will allow researchers to study BH response trends,
including analyzing high utilizers and geographic hot spots, with the goal to equip agencies with information to
proactively target scarce resources and monitor progress. We will also conduct stakeholder interviews regarding
improved emergency responses at these locations. Finally, the research team – in collaboration with the Advisory
Council - will develop and assess the operational feasibility of a real-time predictive tool that would help identify
BH emergencies for dispatchers. By incorporating multiple large administrative datasets, this predictive tool will
capitalize on diverse sources of “signal,” maximizing prediction accuracy. Development of these integrated data
systems and support tools, and ongoing partnership among the research team and agency stakeholders
promises to produce durable and generalizable improvements to Chicago's emergency response. Given
Chicago's position as America's third-largest city, such uses of our already-existing data infrastructure has the
potential to broadly influence other cities that face similar public safety challenges and possess similar
fragmented administrative data structures. As such, we also seek to widely disseminate project findings
throughout the policy and scientific communit...

## Key facts

- **NIH application ID:** 10200643
- **Project number:** 5R01MH117168-03
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** HAROLD Alexander POLLACK
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $397,365
- **Award type:** 5
- **Project period:** 2019-08-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10200643

## Citation

> US National Institutes of Health, RePORTER application 10200643, Predictive Analytics Applied to Integrated Administrative Emergency Response Datasets in Chicago - Resubmission 01 (5R01MH117168-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10200643. Licensed CC0.

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