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Data-driven Interpretation of Chronic Status: Understanding Inflow, Outflow, and Utilization Patterns

Data-driven Interpretation of Chronic Status: Understanding Inflow, Outflow, and Utilization Patterns

September 23, 2018 2018 Fall Conference

Data-driven Interpretation of Chronic Status: Understanding Inflow, Outflow, and Utilization Patterns

Boston’s Data Warehouse has reached a level of technical sophistication that allows data-driven classification of clients as chronically homeless. This enables more precise and objective distribution of housing resources but also limits our definition of chronic status to patterns of shelter utilization captured in our data. This session will propose best practices/ methods for understanding, visualizing, and analyzing longitudinal client history data, will acknowledge setbacks, and will propose ways forward.

Lubov McKone, HMIS Data Analyst, City of Boston

Lubov McKone serves as the HMIS Data Analyst for the City of Boston Department of Neighborhood Development’s Supportive Housing Division. She previously worked for the City of Boston’s Department of Innovation & Technology analyzing evictions and chronic homelessness in the City.

Presentation