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Objective: To identify determinants that contribute to the length of homeless shelter stay. Methods: We utilized a unique dataset from the Homeless Management Information Systems from Boston, Massachusetts, United States, which contains 44,197 shelter stays for 17,070 adults between Jan. 2014 and May 2018. Results: Our statistical analyses and regression model analyses show that factors that contribute to the length of a homeless shelter stay include being female, senior, disability, being Hispanic, or being Asian or Black African. A significant fraction of homeless shelter stays (76%) are experienced by individuals with at least one of three disabilities: physical disability, mental health issues, or substance use disorder. Recidivism also contributes to longer homeless shelter stays. Conclusion: The results suggest possible program and policy implications. Several factors that contribute to longer homeless shelter stay, such as gender, age, disability, race, and ethnicity, may have funding implications. Age may point to the need for early interventions. Disability is developmental and may benefit from treatment and intervention. Finally, we find that length of stay and recidivism are not independent, and may form a vicious cycle that requires additional investigation. 

In spite of significant prior work, few scholars have addressed the unique problems of business process modelling in humanitarian settings. We surface and explore these challenges by treating the ‘guest intake process’ at homeless shelters as an exemplar. In most cities, the homeless shelters are a part of the ecosystem of care that includes hospitals, training agencies, housing agencies and others to support homeless individuals. The exemplar we have chosen represents the first line of defence for the homeless guests: the Intake practice at a homeless shelter, a process that remains fairly knowledge-intensive. Our work follows a collaborative research approach, working with the largest homeless shelter in the region. We conduct observations and interviews at the shelter to construct process models with the Petri Net formalism, followed by analytical experimentation. Lessons learned from the work are compared against prior research in modelling of knowledge-intensive processes. We find that process modelling in humanitarian settings such as homelessness remains fraught with problems. We identify new directions, where prior work in knowledge-intensive processes may require extensions in response to the unique nature of work in humanitarian settings. The paper elaborates these findings, illustrates with authentic examples, and outlines possibilities for future research and implications for practice 

In spite of numerous programs and interventions, homelessness remains a significant societal concern. Long-term homelessness is particularly problematic because it can be increasingly difficult to escape from, and because it represents a continuous drain on societal resources. This paper develops a model for predicting long-term homelessness in response to a simple question: if an individual becomes homeless, what influences the individual's slide to long-term homelessness? The data we analyze to answer the question comes from the City of Boston. The model points to race, veteran status, disability, and age as key factors that predict this slide. The paper describes and illustrates the model along with problems encountered in data preparation and cleansing, prior scholarly work that helped to shape our decisions, and collaboration with participants in the ecosystem for homeless care that complemented the model-building effort. The results are important because they point to possible policy interventions (programs and funding) and process improvements (at homeless shelters) to mitigate this slide.