This award advances the national health by supporting research that will improve trauma care network design. Trauma is a leading cause of death in younger populations. Trauma care networks include trauma centers, staffed and equipped round the clock to treat the most severely injured cases, community hospitals, equipped to stabilize and transport patients to appropriate trauma centers, and air/ground transport services. The trauma care network should ensure that severely injured patients receive the right care in the right amount of time, but maldistribution of trauma care facilities can result in undertreatment of severely injured patients as well as expensive overtreatment of less-severely injured patients. This project will facilitate coordinated network design between state agencies and hospital networks, two key stakeholders in the trauma care system. It will enable trauma policy makers to quantitatively benchmark trauma care regionally and nationally in terms of triage errors and costs. The research team will collaborate with state officials, hospitals, and emergency care providers to validate the network optimization methods. The award will support graduate student research and provide experiential learning opportunities for undergraduate and graduate students in operations research related to healthcare policy and management. Research findings will be widely distributed to both the academic and practitioner communities.
Innovative optimization methods to coordinate the bilevel decision making process for trauma center location are developed in this project. The research frames the trauma network design problem as a bilevel biobjective optimization problem to capture the hierarchical interaction between the upper level (regional authority) decisions involving subsidy allocation to improve social wellbeing and minimize public spending, and lower level (hospital networks) decisions involving upgrade or downgrade to maximize revenue. Novel techniques for cross-decomposition to bound the combinatorial lower level problem and adaptive scalarization to approximate the entire Pareto set of nonlinear nonconvex multiobjective bilevel programs will enable efficient problem solution. Robustness will be evaluated in response to spatial variations in on-scene decisions, and temporal deviations in projected trauma demand and transport resource availability. New surrogate modeling-based algorithms will help solve the resultant maximal parameter subspace identification problem. Data from two states will be used to validate the approach.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
