The City of Boston Sites Policy Interventions to Address Disparate Car-Crash Risk

Unsegmented emergency medical services (EMS) data obscure whether certain patients need care more than others, historically limiting cities from pursuing some preventative health interventions. The operations community has long pioneered prescriptive analytics for EMS response using data on incident location and time, though novel descriptive analytics that segment demand by patient demographics and exposure type can inform preventative policy efforts, such as making capital investments and conducting outreach to lessen the risk of people being struck by motorists. Segmentation is the analytics response to demand heterogeneity in operations. By linking ambulance records to patient age and neighborhood socioeconomic characteristics, Boston EMS and partner agencies developed a data-driven approach to segment demand and guide siting preventative interventions. The approach parses demand by the demographics of patients struck by motorists (age and neighborhood socioeconomic status) along with the exposure type (walking or cycling during school commute hours) and the location where they were struck. The process overcomes long-standing data handling, computational, and conceptual barriers. Traffic crashes disproportionately affect children walking and cycling to school who reside in Boston’s poorest areas. The city used these findings to locate six initial capital projects and identify seven schools in the most affected areas in which to conduct outreach. The city also expanded data sharing between agencies to enable regular preventative work. Compared with the current resource allocation process, this socioeconomically sensitive segmentation that captures the profile of patients struck by motorists elevates disadvantaged neighborhoods in the urban planning process.