A Data-Driven Framework for Planning the Expansion of the Trauma Healthcare Network in Texas
Trauma care services are a vital part of all healthcare-based networks as timely accessibility is important for citizens. Trauma care access is even more relevant when unexpected events overload the capacity of the hospitals. Research literature has highlighted that access to trauma care is not even for all populations, especially when comparing rural and urban groups. Historically, the configuration of a trauma system was often not considered but instead hinged on the designation and verification of individual hospitals as trauma care centers. Recognition of the benefits of an inclusive trauma system has precipitated a more holistic approach. The optimal geographic configuration of trauma care centers is key to maximizing accessibility while promoting the efficient use of resources. This research proposes the development of a two-stage stochastic optimization model for geospatial expansion of a trauma network in the state of Texas. The stochastic optimization model recommends the siting of new trauma care centers according to the geographic distribution of the injured population. Data analytics are used to represent the demand for services in different regions. The model has the potential to benefit both patients and institutions, by facilitating prompt access and promoting the efficient use of resources.
trauma, data analytics, optimization, patients, facility location, stochastic programming, population coverage, COVID-19, Engineering
Saha, S. (2023). A data-driven framework for planning the expansion of the trauma healthcare network in Texas (Unpublished thesis). Texas State University, San Marcos, Texas.