  The adoption of the Prospective Payment System (PPS) in the UK National
Health Service (NHS) has led to the creation of patient groups called Health
Resource Groups (HRG). HRGs aim to identify groups of clinically similar
patients that share similar resource usage for reimbursement purposes. These
groups are predominantly identified based on expert advice, with homogeneity
checked using the length of stay (LOS). However, for complex patients such as
those encountered in burn care, LOS is not a perfect proxy of resource usage,
leading to incomplete homogeneity checks. To improve homogeneity in resource
usage and severity, we propose a data-driven model and the inclusion of
patient-level costing. We investigate whether a data-driven approach that
considers additional measures of resource usage can lead to a more
comprehensive model. In particular, a cost-sensitive decision tree model is
adopted to identify features of importance and rules that allow for a focused
segmentation on resource usage (LOS and patient-level cost) and clinical
similarity (severity of burn). The proposed approach identified groups with
increased homogeneity compared to the current HRG groups, allowing for a more
equitable reimbursement of hospital care costs if adopted.
