OBJECTIVES 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 .
METHODS 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 .
RESULTS 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 .
CONCLUSIONS 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.