Big data solutions- Our Researcher of the Month for July is Ian Smith, of Cambridge’s Royal Papworth Hospital.
Reducing traffic to and from medical facilities – and examining driving behaviour among those with sleep problems – are the subject of ongoing high-level complex research across Europe.
Ian Smith heads up the sleep service at Cambridge’s Royal Papworth Hospital in the UK, which is contributing health care data to a project called Track and Know, aimed at bringing big data solutions to real-life geospatial problems.
Royal Papworth accepts referrals from across the East of England; an area of some 19,000 square kilometres, with around 20,000 patients being seen each year. The population of the region is thinly spread, with an average density of 310 people per square kilometre – compared, for example, to more than 1,500 in London.
Smith told Research Information: ‘We often need to arrange sleep studies for patients at home that would require several trips to and from the hospital, which would add up to journeys of as much as 400km for some people. The solution has been to develop a network of outreach facilities over the last 10 years to improve accessibility, but these have been situated as best guesses to serve our referred population. At the peak we had 23 facilities and the average journey was 35 km.
Through the project the team – a mix of 14 university and commercial partners from 10 countries ranged across the EU – has analysed 46,211 historical patient journeys and modelled the most efficient network.
Smith continued: ‘The model shows that we can provide the service most efficiently with just 10 clinics and reduce the average journey to 17km, saving up to 30 tonnes of CO2 emissions a year.
‘We have looked further into our referral base by the socio-economic status of the areas from which patients are referred; we would anticipate more sleep illness in areas with social deprivation related to poor employment opportunities, increasing rates of obesity, and increased numbers of older residents.
‘However we have had fewer referrals from these areas, when patients do attend they are more unwell and a higher proportion of patients fail to attend their appointments. We also now know that more people miss their appointments when it is sunny than when it is raining! We are re-running these high-level, complex models to see if we can make journeys particularly easy for people referred from areas that are socio-economically challenged.’