Post-COVID Orthopaedic Elective Resource Planning Using Simulation Modelling

ObjectivesTo develop a simulation model to support orthopaedic elective capacity planning. MethodsAn open-source, generalisable discrete-event simulation was developed, including a web-based application. The model used anonymised patient records between 2016-2019 of elective orthopaedic procedures from an NHS Trust in England. In this paper, it is used to investigate scenarios including resourcing (beds and theatres) and productivity (lengths-of-stay, delayed discharges, theatre activity) to support planning for meeting new NHS targets aimed at reducing elective orthopaedic surgical backlogs in a proposed ring fenced orthopaedic surgical facility. The simulation is interactive and intended for use by health service planners and clinicians. ResultsA higher number of beds (65-70) than the proposed number (40 beds) will be required if lengths-of-stay and delayed discharge rates remain unchanged. Reducing lengths-of-stay in line with national benchmarks reduces bed utilisation to an estimated 60%, allowing for additional theatre activity such as weekend working. Further, reducing the proportion of patients with a delayed discharge by 75% reduces bed utilisation to below 40%, even with weekend working. A range of other scenarios can also be investigated directly by NHS planners using the interactive web app. ConclusionsThe simulation model is intended to support capacity planning of orthopaedic elective services by identifying a balance of capacity across theatres and beds and predicting the impact of productivity measures on capacity requirements. It is applicable beyond the study site and can be adapted for other specialties. Strengths and Limitations of this studyO_LIThe simulation model provides rapid quantitative estimates to support post-COVID elective services recovery toward medium-term elective targets. C_LIO_LIParameter combinations include changes to both resourcing and productivity. C_LIO_LIThe interactive web app enables intuitive parameter changes by users while underlying source code can be adapted or re-used for similar applications. C_LIO_LIPatient attributes such as complexity are not included in the model but are reflected in variables such as length-of-stay and delayed discharge rates. C_LIO_LITheatre schedules are simplified, incorporating the five key orthopaedic elective surgical procedures. C_LI