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Improving patient waiting times: a simulation study of an obesity care service

Tako, Antuela, Kotiadis, Kathy, Vasilakis, Christos, Miras, Alexander, Le Roux, Carel W (2014) Improving patient waiting times: a simulation study of an obesity care service. Improving patient waiting times: a simulation study of an obesity care service, 23 (5). pp. 373-381. ISSN 2044-5415. (doi:10.1136/bmjqs-2013-002107) (KAR id:51027)


Background Obesity care services are often faced with the need to adapt their resources to rising levels of demand. The main focus of thisstudy was to help prioritise planned investments in new capacity allowing the service to improve patient experience and meet future anticipated demand.

Methods We developed computer models of patient flows in an obesity service in an Academic Health Science Centre that provides lifestyle, pharmacotherapy and surgery treatment options for the UK’s National Health Service. Using these models we experiment with different scenarios to investigate the likely impact of

alternative resource configurations on patient waiting times.

Results Simulation results show that the timing and combination of adding extra resources (eg, surgeons and physicians) to the service are important. For example, increasing the capacity of the pharmacotherapy clinics equivalent to adding one physician reduced the relevant waiting list size and waiting times, but it then led to increased waiting times for surgical patients. Better service levels were achieved when the service operates with the resource capacity of two physicians and three surgeons. The results obtained from this study had an impact on the planning and organisation of the obesity service.

Conclusions Resource configuration combined with demand management (reduction in referral rates) along the care service can help improve patient waiting time targets for obesity services, such as the 18 week target of UK’s National Health Service. The use of simulation models can help stakeholders understand the

interconnectedness of the multiple microsystems (eg, clinics) comprising a complex clinical service for the same patient population, therefore, making stakeholders aware of the likely impact of resourcing decisions on the different microsystems.

Item Type: Article
DOI/Identification number: 10.1136/bmjqs-2013-002107
Uncontrolled keywords: Simulation
Subjects: Q Science > Operations Research - Theory
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Funders: Organisations -1 not found.
Depositing User: Kathy Kotiadis
Date Deposited: 14 Oct 2015 20:39 UTC
Last Modified: 08 Dec 2022 11:46 UTC
Resource URI: (The current URI for this page, for reference purposes)

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