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

Area of Science:

• Healthcare Management
• Operations Research
• Medical Imaging

Background:

• Increasing demand for Computer Tomography (CT) services, driven by an aging population, is leading to heightened staff workload in radiology departments.
• Anecdotal evidence suggests a significant impact of patient profiles on the workload of radiology staff.

Purpose of the Study:

• To propose and apply a framework combining discrete event simulation (DES) and soft systems methodology (SSM) to quantify and analyze staff workload in a CT department.
• To provide evidence-based recommendations for improving efficiency and managing workload in diagnostic imaging services.

Main Methods:

• A three-stage framework integrating workflow mapping, system dynamics, SSM, and DES was developed and applied to a single-scanner CT department.
• Qualitative and quantitative data were collected and analyzed, with DES utilized as a decision support tool.

Main Results:

• Discrete event simulation (DES) modeling revealed significant differences in workload between inpatient (IP) and outpatient (OP) cohorts.
• For non-contrast examinations, inpatient workload was 2.5 times greater than outpatient, with average IP process delays of 11.9 minutes compared to less than 1 minute for OP.

Conclusions:

• The study recommends the separation of outpatient and inpatient diagnostic imaging services.
• Implementing separate services can enhance efficiency, improve workload management, and strengthen infection control measures within CT departments.