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Modelling Emergency Medical Services

This study by Paul Harper, Vince Knight, and others focuses on forecasting location response and demand patterns for emergency medical services, particularly analyzing WAST daily demand data from 01/04/2005 to 31/12/2009. It explores time-dependent demands per shift, staffing shift patterns, constraints on working hours, and objectives to minimize labor hours, crew sizes, and overtime using a spreadsheet tool. The research includes location analysis, computer simulations, and 'What if?' scenarios to optimize responses in varying scenarios.

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Modelling Emergency Medical Services

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  1. Modelling Emergency Medical Services Paul Harper, Vince Knight, Janet Williams Leanne Smith, Julie Vile, Jonathan Gillard, Israel Vieira

  2. Forecasting Location Response

  3. Forecasting Location Response

  4. Data & Demand Patterns WAST daily demand (01/04/2005-31/12/2009)

  5. Forecasts for December

  6. Forecasting Location Response

  7. Time-dependency

  8. Demand per Shift

  9. Time-dependent Queues • If all servers are busy and only Category B/C patients are in the system, the equilibrium conditions for the state triple S=[i,h,l] are given by:

  10. Staffing

  11. Shift Patterns CONSTRAINTS: • Max weekly working hours • Max night time hours • Rest breaks / days off OBJECTIVES: • Minimise labour hours • Minimise crew size • Minimise overtime

  12. Spreadsheet Tool

  13. Forecasting Location Response

  14. Travel Times - Google Maps API

  15. Location Analysis

  16. Location Analysis EAs RRVs

  17. Computer Simulation

  18. ‘What if?’ Scenarios • Alter demand (e.g. increase by 10%) • Major event • Change in overall fleet capacity • Determine vehicle allocations given different fleet capacities • Reduce turnaround time

  19. Illustrative Results

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