Evaluation and Benchmarking Trauma Patients in Intensive Care Units Using a Novel Decision-Making Framework

Trauma patients often face complex health consequences and are at a risk of rapid deterioration. Prioritizing trauma patients in intensive care units (ICUs) is essential for timely intervention. Current prioritization approaches are challenging tasks that involve considering multiple evaluation criteria, trade-offs and criteria importance, thus requiring a robust multicriteria decision-making (MCDM) approach. In this study, we propose a novel MCDM framework for the evaluation and benchmarking of trauma patients on the basis of health control (HC) criteria derived from real trauma data. The framework consists of two main phases. In the first phase, a dataset of adult trauma patients referred to a trauma network in the West Midlands region of the United Kingdom is identified and preprocessed. This dataset covers the period from 15 May 2014 to 16 December 2016, providing continuous monitoring of the patients. In the second phase, MCDM methods are employed to develop a dynamic decision matrix (DM) that assesses 35 trauma patients on the basis of 16 HC trauma criteria. Using 2-tuple spherical fuzzy linguistic numbers with fuzzy-weighted zero-inconsistency (2TSFLNs-FWZIC), the framework ensures accurate weighting of the criteria, with C1=PS14 and C5=NISS receiving the highest weight values (0.062606) and C2=CD3+8+(106/L) receiving the lowest weight value (0.062353). This weighting process is guided by the input and expertise of a panel of five emergency medicine specialists who have experience in trauma patient management. The results indicate that CoCoSo effectively ranks patients from the least critical (k = 1.348725) to the most critical cases (k = 1.622053) for ICU admission. The proposed framework is evaluated via systematic ranking and sensitivity analysis, providing validation measures for its performance, robustness and reliability.