Business Analytics Blog

Business Analytics Blog

Opinions expressed on this blog reflect the writer’s views and not the position of the Capgemini Group

Emergency services under severe weather conditions

Categories : BusinessOur World

We’ve all been struggling to travel recently as Britain has been hit by the worst weather for 30 years. On Tuesday night a fresh batch of snow led to 500 flights being cancelled from London airports whilst the wrong type of ‘fluffy French snow’ over Christmas was blamed for Eurostar trains getting stuck in the Channel Tunnel. Perhaps we’ve cancelled meetings or have turned up only to find our clients had failed to make it in. Fortunately, in most cases however, home internet connections, conference call facilities together with a bit of flexibility have meant we’ve been able to work around any problems. It’s a different story however for those working in the emergency services – ambulance drivers can’t simply dial into MeetingPlace when someone has skidded off one of the many non-gritted roads around England. For this week’s Figure It Out, to see how serious weather conditions affect the emergency services’ ability to deliver good services and meet performance targets, we’ve built on some of the ambulance modelling work that the OR team is doing in different parts of the country. Our model considers the demand for services, resources available together with the time components of the ambulance job cycle. Scenario 1 - Snowing Down Snow has an effect on travelling times - both travel time to get to the scene and journey time to the hospital. AJC.jpg A 25% increase in the average journey times has only a small effect on ambulance utilisation (approx 2%) but leads to a significant decrease in performance (around 30%). This is because ambulance performance is defined as the percentage of serious emergency calls with a response time of less than eight minutes. Under this scenario, to be able to meet the typical 75% performance target, the model predicts a requirement to quadruple the vehicle and staff resources available. Scenario 2 – Snow Falls Simultaneously, falls in the snow are increasing the demand for fracture surgery. Dr John Heyworth, Head of the Accident and Emergency consultants, told the BBC: The great majority of people we have seen have not behaved in a reckless manner they are simply falling over. As hospitals get busier, ambulance staff take longer to handover patients at A&E. This translates to increased turnaround times and leads to increased utilisation and the associated impact on performance. Assuming 5 minutes increase on the average ambulance turnaround time the model estimates deterioration in performance from 77% to 73%. It never rains but it pours Unfortunately for the emergency services, these two scenarios snow-ball with other operational implications give service managers a real headache. otable.jpg outgraph.jpg Interestingly, not all emergency services are reporting an absolute increase in base demand. Some hospitals are cancelling routine appointments whilst contingency plans such as the provision of 4X4 vehicles and the welcome assistance from other agencies such as mountain rescue, the Red Cross and the Ministry of Defence are increasing the number of resources available. There is also a call to the public to make use of services wisely and only request ambulances in life-threatening situations. However, running the model under extreme weather conditions with varying demand shows that services consistently fail to meet performance. g1.png We conclude that job cycle time (especially travelling time to scene) is the main driver of performance and that varying activity demand has less of an effect. In fact, to be able to meet performance targets, demand levels need to reduce by 80%. Our model is able to assess the impact on performance and the capacity required to meet performance targets. Under extreme weather conditions we can only expect emergency services to struggle - our only advice is to be careful!

 
Capgemini has been using its OR capability in the Ambulance sector to support a number of capacity modelling and performance management projects. If you’d like to hear more, including seeing the model we used here, please get in touch.

 

About the author

Jonathan Chadwick
Jonathan Chadwick
Jon has worked for 18 years as an analytical consultant in the UK, USA and Europe for a diverse range of sectors, most recently Financial, Oil & Gas and Government. Jon has extensive experience in benefits realisation, modelling, business analytics, portfolio management and change management. Jon devised and created Figure It Out.

Connect with me

Leave a comment

Your email address will not be published. Required fields are marked *.