ENTIMOS: A Discrete Event Simulation Model for Maximising Efficiency of Infusion Suites in Centres Treating Multiple Sclerosis Patients.
Journal
Applied health economics and health policy
ISSN: 1179-1896
Titre abrégé: Appl Health Econ Health Policy
Pays: New Zealand
ID NLM: 101150314
Informations de publication
Date de publication:
09 2022
09 2022
Historique:
accepted:
03
04
2022
pubmed:
19
5
2022
medline:
20
8
2022
entrez:
18
5
2022
Statut:
ppublish
Résumé
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. 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. 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. 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.
Sections du résumé
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.
Identifiants
pubmed: 35585305
doi: 10.1007/s40258-022-00733-0
pii: 10.1007/s40258-022-00733-0
pmc: PMC9117085
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
731-742Informations de copyright
© 2022. The Author(s).
Références
Gelfand JM. Chapter 12—multiple sclerosis: diagnosis, differential diagnosis, and clinical presentation. In: Goodin DS, editor. Handbook of clinical neurology, vol. 122. Amsterdam: Elsevier; 2014. p. 269–90.
Stenager E. A global perspective on the burden of multiple sclerosis. Lancet Neurol. 2019;18(3):227–8. https://doi.org/10.1016/s1474-4422(18)30498-8 .
doi: 10.1016/s1474-4422(18)30498-8
pubmed: 30679041
MS Society. MS in the UK. Available at: https://www.mssociety.org.uk/sites/default/files/2020-08/MS-in-the-UK_2020.pdf .
Wallin MTCW, Nichols E, Bhutta Z, et al. Global, regional, and national burden of motor neuron diseases 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2018;17(12):1083–97. https://doi.org/10.1016/s1474-4422(18)30404-6 .
doi: 10.1016/s1474-4422(18)30404-6
Gold R, Wolinsky JS, Amato MP, Comi G. Evolving expectations around early management of multiple sclerosis. Ther Adv Neurol Disord. 2010;3(6):351–67. https://doi.org/10.1177/1756285610385608 .
doi: 10.1177/1756285610385608
pubmed: 21179596
pmcid: 3002639
Berger JR. Functional improvement and symptom management in multiple sclerosis: clinical efficacy of current therapies. Am J Manag Care. 2011;17(Suppl 5 Improving):S146–53.
pubmed: 21761953
National MS Society. Disease-modifying therapies for MS. New York: National MS Society; 2020.
Holm M. MS treatment strategy: Hitting hard and early, or minimising risks through stepwise escalation? Brainwork; 2018. Available at: https://www.brainwork.md/ms-treatment-strategy-hitting-hard-and-early-or-minimising-risks-through-stepwise-escalation/ .
Plourde CL, Varnado WT, Gleaton BJ, Das DG. Reducing infusion clinic wait times using quality improvement. JCO Oncol Pract. 2020;16(8):e807–13. https://doi.org/10.1200/JOP.19.00643 .
doi: 10.1200/JOP.19.00643
pubmed: 32142391
Simacek KF, Ko JJ, Moreton D, Varga S, Johnson K, Katic BJ. The impact of disease-modifying therapy access barriers on people with multiple sclerosis: mixed-methods study. J Med Internet Res. 2018;20(10): e11168. https://doi.org/10.2196/11168 .
doi: 10.2196/11168
pubmed: 30377144
pmcid: 6234348
Thokala P, Dixon S, Jahn B. Resource modelling: the missing piece of the HTA jigsaw? Pharmacoeconomics. 2015;33(3):193–203. https://doi.org/10.1007/s40273-014-0228-9 .
doi: 10.1007/s40273-014-0228-9
pubmed: 25411095
Melao A. Oral DMTs still common 1st therapy for new ms patients but ocrevus having impact, market report says. Multiple Sclerosis News Today; 2018. Available at: https://multiplesclerosisnewstoday.com/2018/07/27/spherix-global-insights-new-ms-patient-audit-reveals-dtm-use-trends/ .
Bindu DY. Time from ‘treatment decision’ to ‘actual start of DMT’ in MS – a local clinical survey. Available at: https://multiplesclerosisacademy.org/resources/delegate-projects/projects-medication/time-from-treatment-decision-to-actual-start-of-dmt-in-ms-a-local-clinical-survey/ .
Multiple Sclerosis Trust. 20% rise in the estimated number of people living with MS in the UK. Multiple Sclerosis Trust; 2020. Available at: https://mstrust.org.uk/news/20-rise-estimated-number-people-living-ms-uk .
McPherson S. Infusion suites are adapting in response to COVID-19. WeInfuse News; 2020. Available at: https://weinfuse.com/infusion-suites-are-adapting-in-response-to-covid-19/ .
De Cock E, Pivot X, Hauser N, Verma S, Kritikou P, Millar D, et al. A time and motion study of subcutaneous versus intravenous trastuzumab in patients with HER2-positive early breast cancer. Cancer Med. 2016;5(3):389–97. https://doi.org/10.1002/cam4.573 .
doi: 10.1002/cam4.573
pubmed: 26806010
pmcid: 4799946
Sugalski JM, Kubal T, Mulkerin DL, Caires RL, Moore PJ, Fiorarancio Fahy R, et al. National comprehensive cancer network infusion efficiency workgroup study: optimizing patient flow in infusion centers. J Oncol Pract. 2019;15(5):e458–66. https://doi.org/10.1200/jop.18.00563 .
doi: 10.1200/jop.18.00563
pubmed: 30964732
Salleh S, Thokala P, Brennan A, Hughes R, Dixon S. Discrete event simulation-based resource modelling in health technology assessment. Pharmacoeconomics. 2017;35(10):989–1006. https://doi.org/10.1007/s40273-017-0533-1 .
doi: 10.1007/s40273-017-0533-1
pubmed: 28674845
Biogen. Tysabri prescribing information 2020. Available at: https://www.tysabri.com/content/dam/commercial/tysabri/pat/en_us/pdf/tysabri_prescribing_information.pdf .
Genentech. Twice-yearly dosing of OCREVUS. 2020. Available at: https://www.ocrevus.com/hcp/dosing/administration.html .
Genzyme S. How lemtrada is given: sanofi genzyme. 2020. Available at: https://www.lemtrada.com/about/how-lemtrada-is-given .
Concannon K, Elder M, Hindle K, Tremble J, Tse S. Simulation modeling with SIMUL8 by Kieran Concannon, et al. Mississauga: Visual Thinking International; 2007.
Franken MG, Kanters TA, Coenen JL, de Jong P, Koene HR, Lugtenburg PJ, et al. Potential cost savings owing to the route of administration of oncology drugs: a microcosting study of intravenous and subcutaneous administration of trastuzumab and rituximab in the Netherlands. Anticancer Drugs. 2018;29(8):791–801. https://doi.org/10.1097/cad.0000000000000648 .
doi: 10.1097/cad.0000000000000648
pubmed: 29846248
Liang B, Turkcan A, Ceyhan ME, Stuart K. Improvement of chemotherapy patient flow and scheduling in an outpatient oncology clinic. Int J Prod Res. 2015;53(24):7177–90. https://doi.org/10.1080/00207543.2014.988891 .
doi: 10.1080/00207543.2014.988891
Huggins A, Claudio D, Waliullah M. A detailed simulation model of an infusion treatment center. Proc Winter Simul Conf. 2014;2014:1198–209. https://doi.org/10.1109/WSC.2014.7019977 .
doi: 10.1109/WSC.2014.7019977
Baril C, Gascon V, Miller J. Design of experiments and discrete-event simulation to study oncology nurse workload. IISE Trans Healthc Syst Eng. 2020;10(1):74–86. https://doi.org/10.1080/24725579.2019.1680581 .
doi: 10.1080/24725579.2019.1680581
Alvarado MM, Cotton TG, Ntaimo L, Pérez E, Carpentier WR. Modeling and simulation of oncology clinic operations in discrete event system specification. SIMULATION. 2017;94(2):105–21. https://doi.org/10.1177/0037549717708246 .
doi: 10.1177/0037549717708246
Bernatchou M, Ouzayd F, Bellabdaoui A, Hamdaoui M. Towards a simulation model of an outpatient chemotherapy unit. In: International Colloquium on Logistics and Supply chain Management. 2017. pp. 177–82.
Lamé G, Dixon-Woods M. Using clinical simulation to study how to improve quality and safety in healthcare. BMJ Simul Technol Enhanc Learn. 2020;6(2):87–94. https://doi.org/10.1136/bmjstel-2018-000370 .
doi: 10.1136/bmjstel-2018-000370
pubmed: 32133154
pmcid: 7056349
Simulation for predictive analytics in healthcare: answering “What If?”. SDLC Partners; 2020. Available at: https://sdlcpartners.com/insights/simulation-for-predictive-analytics-in-healthcare-answering-what-if/ .
Harper PR, Gamlin HM. Reduced outpatient waiting times with improved appointment scheduling: a simulation modelling approach. OR Spectrum. 2003;25(2):207–22. https://doi.org/10.1007/s00291-003-0122-x .
doi: 10.1007/s00291-003-0122-x
Topaloglu S. A shift scheduling model for employees with different seniority levels and an application in healthcare. Eur J Oper Res. 2009;198(3):943–57. https://doi.org/10.1016/j.ejor.2008.10.032 .
doi: 10.1016/j.ejor.2008.10.032