Planning for healthcare services during the COVID-19 pandemic in the Southeast of England: a system dynamics modelling approach
| Autor: | Paul Turner, Peter Lacey, Padmanabhan Badrinath, Abraham George, Alex Gray, Chris Harwood, Mark Gregson |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | BMJ Open, Vol 13, Iss 12 (2023) |
| Druh dokumentu: | article |
| ISSN: | 2023-0729 2044-6055 |
| DOI: | 10.1136/bmjopen-2023-072975 |
| Popis: | Objectives To develop, test, validate and implement a system dynamics model to simulate the pandemic progress and the impact of various interventions on viral spread, healthcare utilisation and demand in secondary care.Design We adopted the system dynamics model incorporating susceptible, exposed, infection and recovery framework to simulate the progress of the pandemic and how the interventions for the COVID-19 response influence the outcomes with a focus on secondary care.Setting This study was carried out covering all the local health systems in Southeast of England with a catchment population of six million with a specific focus on Kent and Medway system.Participants Six local health systems in Southeast of England using Kent and Medway as a case study.Interventions Short to medium ‘what if’ scenarios incorporating human behaviour, non-pharmaceutical interventions and medical interventions were tested using the model with regular and continuous feedback of the model results to the local health system leaders for monitoring, planning and rapid response as needed.Main outcome measures Daily output from the model which included number infected in the population, hospital admissions needing COVID-19 care, occupied general beds, continuous positive airway pressure beds, intensive care beds, hospital discharge pathways and deaths.Results We successfully implemented a regional series of models based on the local population needs which were used in healthcare planning as part of the pandemic response.Conclusions In this study, we have demonstrated the utility of system dynamics modelling incorporating local intelligence and collaborative working during the pandemic to respond rapidly and take decisions to protect the population. This led to strengthened cooperation among partners and ensured that the local population healthcare needs were met. |
| Databáze: | Directory of Open Access Journals |
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