RESUMEN
This study documents more than five years of analysis that drove the policy case, deployment, and retrospective evaluation for an innovative service model that enables Boston Emergency Medical Services (EMS) to respond quickly and effectively to investigation incidents in an area of heavy need in Boston. These investigation incidents are typically calls for service from passers-by or other third-party callers requesting that Boston EMS check in on individuals, often those who may appear to have an altered mental status or to be unhoused. First, this study reports the pre-intervention analytics in 2017 that built the policy case for service segmentation, a new Community Assistance Team designated "Squad 80" that primarily responds to investigation incidents in one broad area of the city with high rates of substance abuse and homelessness, helping patients who often refuse ambulance transport connect to social services. Second, this study reports a post-intervention, observational evaluation of its operational advantages and trade-offs. We observe that incidents involving the Community Assistance Team have significantly shorter response times and result in fewer transports to emergency departments than investigation incidents not involving the unit, leading to fewer ambulance unit-hours utilized across the system. This study documents the descriptive analytics that built the successful policy case for a substantive change in the healthcare-delivery supply chain in Boston and how this change offers operational advantages. It is written to be an accessible guide to the analysts and policy makers considering emergency services segmentation, an important frontier in equitable public-service delivery.
Asunto(s)
Servicios Médicos de Urgencia , Humanos , Boston , Estudios Retrospectivos , Ambulancias , Servicio de Urgencia en HospitalRESUMEN
In the first two years of the COVID-19 pandemic, members of Boston Emergency Medical Services, the City of Boston's municipal ambulance service, had 7,689 encounters with confirmed-positive Boston residents. As COVID-19 virus strains continue to infect residents in Boston and across the country, understanding the correlation between population positivity, EMS encounters, and hospitalizations can inform healthcare response. This study examines urban virus-surveillance indicators that can serve as an early warning of the volume of Emergency Medical Services (EMS) encounters with COVID-19 positive patients and subsequently how EMS encounters with confirmed COVID-19 patients can serve as an early indicator of future hospital-demand surges. With daily data from Boston EMS and three other public agencies, we evaluate the relationship between five indicators and confirmed Boston EMS COVID-19 encounters by estimating separate Auto Regressive Integrated Moving Average models and cross-correlating their residuals. This study finds a significant and positive correlation between new COVID-19 cases citywide and EMS encounters 6 days later (p < 0.01), as well as between confirmed EMS encounters with COVID-19 patients and the number of intensive care unit beds occupied 7- and 18 -days later (p < 0.01). This study provides city health leadership needed clarity on the specific ordering and associated time lag in which infections in the population increase, EMS members encounter positive patients, and hospitals deliver care.