The data which has generated the 583 figure has been derived from EMRTS’ independent data modelling. Figures indicate that there are currently 1331 patients who, according to their clinical presentation and situation as logged on the ambulance service system, are patients our clinicians believe would benefit from WAA/EMRTS attendance and who currently are not seen for a variety of reasons. Reasons for non-attendance vary but it is predominantly because crews are already committed (at night this could be because a response would be needed from the Cardiff team) or because poor road capability means that if an air response is not possible there would be an unacceptable delay. Through the simulations run by Optima (over 200 simulations and 40 scenarios), we have identified the optimum base configuration and staffing pattern which enables us to treat both our existing patient base plus an additional 583 out of those 1331 which currently make up the unmet need. We can identify the projected location of these patients and see that not only do all parts of Wales see an increase in patients attended by WAA/EMRTS, no parts of Wales see a reduction in patients attended by WAA/EMRTS – there is a net benefit in all parts of the country. 

 

A brief overview of the calculation process is as follows: 

 

  • Calculation of total demand from 4-year average activity, combined with prospectively identified unmet need over 2 years. 
  • Calculation of utilisation and activity by base, day, month, season, hour, including average incidents per day, and total time involved with incidents.  
  • Optima models tuned to match real life, and then 200+ simulations run, with 41 best performing scenarios continued, and tested.  This includes sensitivity analysis looking at all current base options, addition of resource, base moves, changes of shift times, and poor weather as well as road access. 
  • Data extracted from Optima models, and linked back to NHS data. 
  • ‘Deep dive’ into the clinical impact of current and proposed patients. 
  • Change in patients identified from this data by locality to give headline figures.