Stung into action

One of the most visible manifestations of economic ascent and the concomitant growth of the middle class is car ownership, which is Sáo Paulo in a curse as well as a blessing. On Friday evenings, when rush-hour mayhem reaches its peak, tailbacks out of the city can reach up to 183 miles in total. Of course, the misery is not just confined to commuters: in a city of 11 million and one of the most overcrowded road networks in the world, what hope is there for an ambulance service and the people it is supposed to serve?

In 2007, the average response time for ambulances from Sáo Paulo’s Serviço de Atendimento Móvel de Urgência (SAMU) was almost 30 minutes, compared to London’s 14 and Montreal’s 10 (the international benchmark).

The desired trade-off between extra cost and superior efficiency does not automatically ensue, hence investing in more ambulances was not viable. Neither was opening new ambulance stations; finance, building space and bureaucracy put paid to that. Mobile stations, however, are cheaper and can be relocated to ensure good coverage.

The first task was to assemble the relevant data. This included patterns of disease and other health issues, information from SAMU and a facts and figures on the city’s traffic. There were also numerous variables to process, including costs, demand, location, numbers and capacity. Basically: how could SAMU deploy its 140 vehicles to service 9000 call per day. The idea was that this could be addressed by scenario simulation and combinatorial optimisation. The answer lay in nature, where the finest exponent of exploration and exploitation, is the honey bee.

Just as a colony’s foraging for food is random initially, so demand for medical care arises in an arbitrary fashion. Finding food sources then returning to the hive is comparable to ambulances dispatched to emergencies then later returning to base.

A restructure to maximise coverage while minimising disruption was recommended following research. Flexibility to reallocate and reposition was a cornerstone of the new system: additional mobile bases were employed – in squares, parks and other public spaces – allowing ambulances to be temporarily stationed in “hotspots”. Modelling of various scenarios had pieced together a way of reducing SAMU’s response times – which were duly more than halved.

2007-2012 saw SAMU’s response times cut down to 10 minutes, becoming the first Latin American ambulance service to be recognised as an Accredited Centre of Excellence. Objectives were established and pursued. Novel protocols and policies were introduced. Almost every aspect of operations was subtly refined or radically overhauled. Yet there is no doubt operational research made a pivotal contribution.

The concepts weren’t easy to sell, and had to clearly demonstrate the benefits that mobiles stations, if properly located, could bring compared to traditional facilities – all done in the context of a limited budget.

The algorithm designed could do precisely that. The bottom line is that it was shown how, just like a honey bee, SAMU could constantly adapt to its surroundings.