Modelling patient flows and resource use within a sexual health clinic through discrete event simulation to inform...

Continuous improvement in the delivery of health services is increasingly being demanded in the UK at a time when budgets are being cut. Simulation is one approach used for understanding and assessing the likely impact of changes to the delivery of health services. However, little is known about the usefulness of simulation for analysing the delivery of sexual health services (SHSs). We propose a simulation method to model and evaluate patient flows and resource use within an SHS to inform service redesign.

We developed a discrete event simulation (DES) model to identify the bottlenecks within the Unity SHS (Bristol, UK) and find possible routes for service improvement. Using the example of the introduction of an online service for sexually transmitted infection (STI) and HIV self-sampling for asymptomatic patients, the impact on patient waiting times was examined as the main outcome measure. The model included data such as patient arrival time, staff availability and duration of consultation, examination and treatment. We performed several sensitivity analyses to assess uncertainty in the model parameters.

We identified some bottlenecks under the current system, particularly in the consultation and treatment queues for male and female walk-in patients. Introducing the provision of STI and HIV self-sampling alongside existing services decreased the average waiting time (88 vs 128 min) for all patients and reduced the cost of staff time for managing each patient (£72.64 vs £88.74) compared with the current system without online-based self-sampling.

The provision of online-based STI and HIV self-sampling for asymptomatic patients could be beneficial in reducing patient waiting times and the model highlights the complexities of using this to cut costs. Attributing recognition for any improvement requires care, but DES modelling can provide valuable insights into the design of SHSs ensuing in quantifiable improvements. Extension of this method with the collection of additional data and the construction of more informed models seems worthwhile.