Enabling better management of patients: discrete event simulation combined with the STAR approach

Eren Demir, David Southern

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
71 Downloads (Pure)


Squeezed budgets and funding cuts are expected to become a feature of the healthcare landscape in the future, forcing decision makers such as service managers, clinicians and commissioners to find effective ways of allocating scarce resources. This paper discusses the development of a decision support toolkit (DST) that facilitates the improvement of services by identifying cost savings and efficiencies within the pathway of care. With the help of National Health Service and commercial experts, we developed a discrete event simulation model for Deep Vein Thrombosis (DVT) patients and adapted the socio technical allocation of resources (STAR) approach to answer crucial questions like: what sort of interventions should we spend our money on? Where will we get the most value for our investment? How will we explain the choices we have made? The DST enables users to model their own services by working with the DST interface allowing users to specify local DVT services. They can input local estimates, or data of service demands and capacities, thus creating a baseline discrete event simulation model. The user can then compare the baseline with potential changes in the patient pathway in the safety of a virtual environment. By making such changes key decision makers can easily understand the impact on activity, cost, staffing levels, skill-mix, utilisation of resources and, more importantly, it allows them to find the interventions that have the highest benefit to patients and provide best value for money.
Original languageEnglish
Pages (from-to)577-590
Number of pages14
JournalJournal of the Operational Research Society
Issue number5
Early online date1 May 2017
Publication statusPublished - 21 Dec 2017


  • discrete event simulation
  • STAR
  • decision support toolkit
  • deep vein thrombosis
  • patient flow modelling


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