Developed countries are struggling with an ageing population and its effects on healthcare services. One of the reasons is that changes in population structure as well as other contributing factors such as changes in peoples’ lifestyles tend to increase the number of people with long term conditions. The management of long term conditions necessitates periodical and frequent visits to a specialist, which generates additional demand for healthcare services. Partly as a result, healthcare professionals and managers have been looking into different ways of expanding capacity in innovative and more efficient manner. One such service innovation is the introduction of remote-review or virtual clinics, primarily for those patients with long term conditions who are deemed to be stable or low risk. The idea is to increase capacity for outpatient appointments while avoiding the high costs associated with setting up and running a full consultant led clinic. There are plenty of empirical studies in the literature addressing different aspects of virtual clinics: e.g. the false negative/positive rates for patient risk group classification, the patient experience during and after implementation, their use in different diseases and conditions, etc. There are fewer examples in the literature that consider the problem from an operations research/systems modelling perspective. The modelling is not trivial given the need to capture the relevant population dynamics including notions of disease progression in the patients. In this paper we describe a system dynamics model that is designed to evaluate the likely impact of the implementation of virtual clinics under changing population dynamics and dynamic disease progression of the patients.
|Publication status||Published - Aug 2020|
|Event||46th meeting of the EURO Working Group on Operational Research Applied to Health Services - University of Vienna, Vienna, Austria|
Duration: 26 Jul 2020 → 31 Jul 2020
Conference number: 46
|Conference||46th meeting of the EURO Working Group on Operational Research Applied to Health Services|
|Period||26/07/20 → 31/07/20|