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Capacity planning at the interface of health and social care: A simulation approach to time-varying resource needs

  • University of Exeter
  • NHS Bristol, North Somerset and South Gloucestershire CCG
  • Lancaster University

Research output: Contribution to journalArticlepeer-review

Abstract

Delays to hospital discharge create system-wide pressures by reducing acute hospital capacity. As increasing numbers of higher-acuity patients are discharged into home-based care requiring multiple daily visits, modelling capacity at the interface of acute and community care becomes more complex. We introduce a visits-based, time-driven simulation model that incorporates variable daily visit requirements. We compare the simulation with tractable analytic queueing models using a consistent set of parameters, showing how each approach can provide complementary perspectives on system behaviour. This comparison highlights how simplified analytic representations may not reflect congestion dynamics when resource use varies over time, particularly at high traffic intensities. Our model has been implemented in practice by NHS partners for routine planning. We demonstrate how incorporating time-varying resource requirements into visits-based models supports understanding of system dynamics and informs workforce and capacity planning in stretched systems.

Original languageEnglish
Number of pages15
JournalJournal of Simulation
Early online date21 Jun 2026
DOIs
Publication statusE-pub ahead of print - 21 Jun 2026

Data Availability Statement

All data generated or analysed during this study are included in this published article.

Acknowledgements

Early versions of the analytical models used in this paperwere produced by two Business Analytics Masters degreestudents in the Department of Management Science,Lancaster University: Yakun Zhang in 2020/21 and AkhilaDevarapalli in 2021/22. Early versions of the simulationmodels were produced by Manon de Prez, MSc BusinessAnalytics student at the University of Bath School ofManagement in 2017/18.

Funding

This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, National Institute for Health Research, Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (South Western Ireland), British Heart Foundation and Wellcome (award number CFC0129).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

ASJC Scopus subject areas

  • Economics and Econometrics
  • Health Policy

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