“The NHS is to introduce Electric Ambulances” with a pilot due to start “across swaithes of the country” according to today’s Daily Telegraph. Apparently, an evaluation in West Midlands Ambulance Service has already been undertaken with concerns about the 70-mile range and 4-hour charging time expressed.
Predictive modelling, in this case discrete event simulation, could be used to reduce the scope of such a pilot, as there are a number of operational challenges that presumably need to be overcome if electric ambulances are to prove cost effective.
I recall from my own experience in applying predictive modelling techniques to emergency medical services that increases in response times were frequently observed at around shift change over time. This is not a phenomenom unique to the Ambulance Service, as these findings can also be observed in Police Service and Fire & Rescue Service incident data. These increases can be caused by:
- A desire not to allocate new jobs to crews about to come off shift, thereby avoiding overtime and crew fatigue
- The need to “make ready” an ambulance for the next shift, e.g. cleaning and re-stocking, causing either a delay in new crews going out or crews at the end of their shift returning early
In some ambulance services, make-ready teams were used to minimise the impact of this degradation in response time by reducing the time that ambulances were unavailalable to deploy at shift changeover.
The prevalance of this type of delay would surely increase with the potential that battery charges might not last a full shift and in the face of four-hour battery recharge times.
Clearly a 4-hour charge time would be an unacceptable delay for crews to be unavailable to respond and therefore additional ambulances would presumably need to be available to cover this period. If it was 4 hours recharge for every 12-hour shift worked, for example, then this means 25% more ambulances to provide the same level of availability. In reality, it could be more, unless crews swap ambulances once they have recharged.
To mitigate the potential for ambulances to run low on charge during a shift, mechanisms for changing ambulances mid-shift, allocating jobs cognisant of remaining charge and provision of charging points at hospitals (where ambulances often wait anyway) may need to be considered.
Testing combinations of these solutions and evidenceing which ones do or don’t work in a robust way takes considerable time in a real life pilot. By simulating these behaviours in a computer model, different strategies can be tested very quickly and without using valuable capacity or compromising patient safety on strategies that don’t work. Further, minimum requirements in terms of battery life and charge time could be specified to potential suppliers and real life trials implemented only when these can be met.
An NHS spokesman is alleged to have stated that green products are sought “only when they save the taxpayer money”. Even if electric ambulances are considerably cheaper to buy than their conventional alternatives, analysis of the operational cost implications is needed to ensure that an appropriate saving to the taxpayer is realised. By simulating alternative strategies for mitigating the limitations of electric ambulances, this return on investment can be optimised, and more importantly, any adverse effect on patient care minimised.