Abstract:
Background: Improved multiple sclerosis (MS) diagnosis and increased availability of intravenous disease-modifying treatments can lead to overburdening of infusion centres. This study was focused on developing a decision-support tool to help infusion centres plan their operations.
Methods: A discrete event simulation model (‘ENTIMOS’) was developed using Simul8 software in collaboration with clinical experts. Model inputs included treatment-specific clinical parameters, resources such as infusion chairs and nursing staff, and costs, while model outputs included patient throughput, waiting time, queue size, resource utilisation, and costs. The model was parameterised using characteristics of the Charing Cross Hospital Infusion Centre in London, UK, where 12 infusion chairs were deployed for 170 non-MS and 860 MS patients as of March 2021. The number of MS patients was projected to increase by seven new patients per week.
Results: The model-estimated waiting time for an infusion is, on average, 8 days beyond clinical recommendation in the first year of simulation. Without corrective action, the delay in receiving due treatment is anticipated to reach 30 days on average at 30 months from the start of simulation. Such system compromise can be prevented either by adding one infusion chair annually or switching 7% of existing patients or 24% of new patients to alternative MS treatments not requiring infusion.
Conclusion: ENTIMOS is a flexible model of patient flow and care delivery in infusion centres serving MS patients. It allows users to simulate specific local settings and therefore identify measures that are necessary to avoid clinically significant treatment delay resulting in suboptimal care.
Reference:Lacinova K, Thokala P, Nicholas R, Dobay P, Scalfaro E, Angehrn Z, Brennan R, Boer I, Lines C, Adlard N. ENTIMOS: A Discrete Event Simulation Model for Maximising Efficiency of Infusion Suites in Centres Treating Multiple Sclerosis Patients. Appl Health Econ Health Policy. 2022 May 19:1–12. doi: 10.1007/s40258-022-00733-0. Epub ahead of print. PMID: 35585305; PMCID: PMC9117085.