Measuring and managing radiologist workload: Application of lean and constraint theories and production planning principles to planning radiology services in a major tertiary hospital. Issue 5 (12th July 2013) - MacDonald, Sharyn LS; Cowan, Ian A; Floyd, Richard; Mackintosh, Stuart; Graham, Rob; Jenkins, Emma; Hamilton, Richard
Abstract Introduction We describe how techniques traditionally used in the manufacturing industry (lean management, the theory of constraints and production planning) can be applied to planning radiology services to reduce the impact of constraints such as limited radiologist hours, and to subsequently reduce delays in accessing imaging and in report turnaround. Methods Targets for imaging and reporting were set aligned with clinical needs. Capacity was quantified for each modality and for radiologists and recorded in activity lists. Demand was quantified and forecasting commenced based on historical referral rates. To try and mitigate the impact of radiologists as a constraint, lean management processes were applied to radiologist workflows. A production planning process was implemented. Results Outpatient waiting times to access imaging steadily decreased. Report turnaround times improved with the percentage of overnight/on‐call reports completed by a 1030 target time increased from approximately 30% to 80 to 90%. The percentage of emergency and inpatient reports completed within one hour increased from approximately 15% to approximately 50% with 80 to 90% available within 4 hours. The number of unreported cases on the radiologist work‐list at the end of the working day reduced. The average weekly accuracy for demand forecasts for emergency and inpatient CT, MRI and plain film imaging was 91%, 83% and 92% respectively. For outpatient CT, MRI and plain film imaging the accuracy was 60%, 55% and 77% respectively. Reliable routine weekly and medium to longer term service planning is now possible. Conclusions Tools from industry can be successfully applied to diagnostic imaging services to improve performance. They allow an accurate understanding of the demands on a service, capacity, and can reliably predict the impact of changes in demand or capacity on service delivery.