Data analytics for improved closest hospital suggestion for EMS operations in New York City. (November 2022)

• Record Type: Journal Article
• Authors: Olivier, Audrey; Adams, Matt; Mohammadi, Sevin; Smyth, Andrew; Thomson, Kathleen; Kepler, Timothy; Dadlani, Monish
• Is Part Of: Sustainable cities and society. Volume 86(2022)
• Publication Date: 2022-11
• Subjects: Ambulance travel time modeling -- Statistical data analysis -- Aleatory and epistemic uncertainties -- EMS operations optimization -- Closest hospital suggestion; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2022.104104
• Languages: English
• ISSNs: 2210-6707

Abstract

Abstract: In addition to fires and various other emergencies, the Fire Department of New York City (FDNY) responds to medical emergencies throughout New York City. When a patient requires hospital care, the FDNY uses a hospital suggestion pattern to recommend the closest hospital that can appropriately treat the patient, given the geographic location of the incident. In a city like New York with a high density of hospitals, determining the closest hospital (in terms of travel time) is not trivial. This paper presents the results of a collaboration between the FDNY and Columbia University which integrates data analytics in the derivation, implementation and monitoring of this hospital suggestion pattern. Both telematics data of city-owned vehicles and historical ambulance travel times to hospitals are utilized to estimate the order of closest hospitals for a given incident location. Implementation constraints (coarse spatial and temporal resolutions of the pattern) and sparsity of the historical ambulance dataset generate large aleatory and epistemic uncertainties that are accounted for using a probabilistic data fusion procedure. The result of this analysis, hospital suggestion pattern N, was implemented in December 2020. A statistical analysis shows an overall decrease in travel times to hospitals as an effect of this pattern. Highlights: A data-driven methodology is used to optimize hospital transports in New York City. The methodology leverages a road network and origin–destination ambulance data. Uncertainties in travel times are accounted for in the data fusion procedure. The proposed solution was successfully deployed in December 2020 in New York City.