Modelling a Computed Tomography service using mixed Operational Research methods

Abstract

ABSTRACT Demand for computed tomography (CT) and CT waiting lists are growing, a problem exacerbated by the postponement of scheduled services during the Covid-19 pandemic. In this case study operational research (OR) methods were used to investigate resource utilisation and CT waiting list growth. Stakeholder involvement was facilitated using system dynamics (SD) for problem conceptualisation and Soft Systems Methodology (SSM) to identify service issues, data requirements, and scenarios for testing. Discrete event simulation (DES) was used to generate metrics pertaining to daily staff work load, process performance (delays) and waiting list evolution, for the current and alternative scenarios. Lessons learnt from the perspective of a clinical modeller are discussed throughout. DES model outputs illustrated the high daily variation in resource utilisation and process delays for the current service where inpatients and outpatients share a single CT scanner. Inpatient examinations were found to consume on average 23% more staff time than outpatient. For non-contrast CT scans, outpatients consumed 63% less time than inpatients. Simulation results for an outpatient-only service demonstrated higher CT and healthcare assistant utilisation, with low variation and process delays. This work recommends the separation of inpatient and outpatient CT services to address the problem of growing CT waiting lists.