A Rule Search Framework for the Early Identification of Chronic Emergency Homeless Shelter Clients
This work addresses the challenge of efficiently targeting housing interventions for high-risk homeless individuals, representing an incremental improvement in a domain-specific application.
The paper tackled the problem of early identification of homeless shelter clients at risk of chronic use by applying rule search techniques, resulting in a reduction of median identification time from 297 days to 162 days.
This paper uses rule search techniques for the early identification of emergency homeless shelter clients who are at risk of becoming long term or chronic shelter users. Using a data set from a major North American shelter containing 12 years of service interactions with over 40,000 individuals, the optimized pruning for unordered search (OPUS) algorithm is used to develop rules that are both intuitive and effective. The rules are evaluated within a framework compatible with the real-time delivery of a housing program meant to transition high risk clients to supportive housing. Results demonstrate that the median time to identification of clients at risk of chronic shelter use drops from 297 days to 162 days when the methods in this paper are applied.