Methods: A conceptual model was built to reproduce the process of cataract, from incidence, defined as need of surgery, through demand, inclusion in a waiting list and surgery. In order to estimate each parameter of the model, a specific method was used, using administrative data and studies of our research team or others. Discrete-event simulation was used to implement the model. Sensitivity analysis was performed in order to assess the impact of introducing a prioritization system in the waiting list, compared to the usual FIFO (first-in, first-out) system. The prioritization system used included clinical, functional and social criteria. The increase of the priority score through time was taken into account. Each execution consisted in 10 replications of a 5-year simulation horizon. Outcomes were assessed through the time with disability, that is, waiting time weighted by priority score, for operated cases and cases still waiting. The benefit of applying the prioritization system was calculated as time gained minus time lost, both weighted by priority, and with respect to the mean waiting time with the FIFO discipline. Different scenarios of mean waiting time were used to compare the two alternatives.

Results: Waiting time for patients operated under the FIFO discipline was homogeneous (standard deviations between 0.5 and 0.8). When the prioritization system was applied, the waiting time distribution had a higher variability (standard deviations between 7 and 11) and was positively skewed, with 10% or less of cases with extremely high waiting times. For waiting times lower than 24 months, the time gained with the prioritization system was lower than the time lost. However, the opposite happened when the FIFO waiting time was 24 months or higher: the prioritization system saved 1.3 months weighted by priority.

Conclusions: The simulation model allowed to analyze the benefit of introducing a prioritization system in a waiting list for cataract surgery, in different scenarios of mean waiting time. This tool will allow to test different scenarios such as changes in practice patterns (e.g., increasing the probability of second eye surgery) or assessment of needs for surgery (prevalence).