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The adoption of the Prospective Payment System (PPS) in the UK NationalHealth Service (NHS) has led to the creation of patient groups called HealthResource Groups (HRG). HRGs aim to identify groups of clinically similarpatients that share similar resource usage for reimbursement purposes. Thesegroups are predominantly identified based on expert advice, with homogeneitychecked using the length of stay (LOS). However, for complex patients such asthose encountered in burn care, LOS is not a perfect proxy of resource usage,leading to incomplete homogeneity checks. To improve homogeneity in resourceusage and severity, we propose a data-driven model and the inclusion ofpatient-level costing. We investigate whether a data-driven approach thatconsiders additional measures of resource usage can lead to a morecomprehensive model. In particular, a cost-sensitive decision tree model isadopted to identify features of importance and rules that allow for a focusedsegmentation on resource usage (LOS and patient-level cost) and clinicalsimilarity (severity of burn). The proposed approach identified groups withincreased homogeneity compared to the current HRG groups, allowing for a moreequitable reimbursement of hospital care costs if adopted.

Towards a fairer reimbursement system for burn patients using cost-sensitive classification