Leveraging Artificial Intelligence to Prevent Homelessness in Los Angeles County
Leveraging Artificial Intelligence to Prevent Homelessness in Los Angeles County
Publish Date: 2026-04-30 14:22:00
Source Domain: essentialhospitals.org
Homelessness and health are deeply intertwined, each shaping and reinforcing the other. Poor health can push individuals into housing instability, while homelessness introduces new challenges and worsens existing conditions. The result is a cycle that not only affects individuals’ well-being but also places growing strain on the health care system. For hospitals, this cycle translates into higher utilization, more complex care needs, and an urgent need to prevent homelessness before it occurs.
The Los Angeles County Department of Health Services, an association member, leverages artificial intelligence to respond to that need through the Los Angeles County Homeless Prevention Unit (HPU). In 2017, the University of California, Los Angeles’ California Policy Lab (UCLA CPL) studied whether data on an individual’s interactions with county institutions and programs could predict their risk of becoming unhoused. In partnership with county leadership, CPL leveraged millions of records linked across nine county administrative data sources. The lab determined that data could predict a person’s risk of homelessness as far as 12 months in advance, providing a significant opportunity for the county to provide stabilizing interventions.
Inspired by the UCLA CPL’s research, the LA County Department of Health Services established and staffed the HPU with funding from the American Rescue Plan Act. The HPU uses data from the county’s Health Services, Mental Health, Public Social Services, and Child and Family Services departments, as well as the sheriff’s office. The County Chief Information Office (CIO) manages the “InfoHub” database, which enables real-time, cross-departmental data sharing to support predictive modeling.
The CPL and CIO jointly monitor the model for accuracy and false negative rates across demographic groups. They adjust the model to mitigate bias, even when doing so reduces predictive performance. This…
