A data-driven methodology for supporting resource planning of health services

In recent years, healthcare systems have been forced to better organize their services in the final attempt to maximize both care effectiveness and efficiency. In particular, emergent trends are prompting hospitals to pay more attention to the effective and efficient planning of resources and to the creation of patient-centred services, in which current activities and resources are reorganized around patients. This paper proposes a process mining based methodology to systematically support the resource planning of health services. Specifically, combining Time-Driven Activity Based Costing and process mining approaches, it automatically identifies the patient flow and analytically evaluates activities, service times, and resource consumptions for a specific class (-es) of patients (e.g., a DRG, patients with specific medical condition, etc.). Thus, it allows to reliably estimate the expected resource consumptions for the patient group under investigation. Thanks to process mining, the method overcomes the limitations of existing quantitative approaches that are often time-consuming, based on subjective observations, and too case specific. The method was applied to a real case study of lung cancer patients in an Italian hospital.