Learning to See: Using Mixed or Methods to Model Radiology Staff Workload and Support Decision Making in CT

Abstract

Demand for Computer Tomography (CT) is growing year on year and the population of Ireland is increasingly aging and ailing. Anecdotally, radiology staff reported increasing levels of workload associated with the patient profile. In this paper, we propose a framework combining discrete event simulation (DES) modeling and soft systems methodologies (SSM) for use in healthcare which captures the staff experience and metrics to evidence workload. The framework was applied in a single-scanner CT department, which completes circa 6000 examinations per year. The scanner case load consists of unscheduled work [inpatient (IP) and emergency department (ED)] and scheduled work [outpatient (OP) and general practitioner (GP)]. The three stage framework is supported by qualitative and quantitative methods and uses DES as a decision support tool. Firstly, workflow mapping and system dynamics are used to conceptualize the problem situation and instigate a preliminary data analysis. Secondly, SSM tools are used to identify components for a DES model and service improvement scenarios. Lastly, the DES model results are used to inform decision-making and identify a satisficing solution. Data from the DES model provided evidence of the differing workload (captured in staff time) for the IP and OP cohorts. For non-contrast examinations, inpatient workload is 2.5 times greater than outpatient. Average IP process delays of 11.9 min were demonstrated compared to less than 1 min for OP. The findings recommend that OP and IP diagnostic imaging be provided separately, for efficiency, workload management and infection control reasons.