LABORATORY INFORMATICS
with big data solutions Big data is helping research into patients with sleep problems, reducing trips to medical facilities, and assessing their safety behind the wheel
Real-life problems
likely that shift workers are also at risk. However, there are currently no good measures of who is at risk and who is safe.’ Accordingly, the team is developing a mobile phone app to measure driving behaviour, and is currently monitoring the driving of staff at the hospital who work a mix of day and night shifts. It will soon start monitoring patients with sleep illnesses, before and after treatment, as they drive around the East of England region. Smith said: ‘The plan is to use the
Ian Smith heads up the sleep service at Cambridge’s Royal Papworth Hospital in the UK, which is contributing healthcare data for the Track and Know project, aimed at finding big data solutions to real-life geospatial problems. Royal Papworth accepts referrals from
across the East of England; an area of 19,000km2, with around 20,000 patients seen each year. The population is thinly spread, with an average density of 310 people per square kilometre – compared to more than 1,500 in London. Smith said: ‘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 35km. Through the project the team – a mix of 14 university and commercial partners from 10 EU countries – 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
16 Scientific Computing World Autumn 2020
app to alert people when their driving performance is drifting. In future, it should be able to help adjudicate on whether or not people can return to driving when they have had a sleep illness – the aim is to contribute to an improvement in road safety, and also getting people back to work as soon as it is appropriate. ‘Other use cases for the Track and Know
platform include looking at insurance models based on driving behaviour, and fleet management – predicting journeys and efficient distribution of resources. ‘The data analysis teams have built
the Track and Know platform to accept a broad range of data types and allow for rapid processing of big data, integrating driver behaviour with local road conditions, weather patterns, distribution of retail outlets, petrol stations, electrical charging points, weight of traffic, known accident black spots, and so on.’ The study is funded from an EU 2020
“The plan is to use the app to alert people when their driving performance is drifting”
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.’
Driving knowledge The team is also working on a sub-project that examines driving behaviour among those with sleep disorders. Smith said: ‘We know that people with sleep disorders are more likely to crash their cars than people with good sleep, maybe four times more likely. It also seems
grant of €4.8m, and all publications relating to the research are required to be open – they can be found at https://
trackandknowproject.eu/publications/. Furthermore, many of the models and algorithms are based on the EU Track and Know project, where some of the data and models are public: https://
trackandknowproject.eu/file-repository/ Smith concluded: ‘National Health
Service journeys account for around 5 per cent of traffic on our roads. If we can reduce this, we can have a major impact on road congestion and CO2 emissions. ‘In the era of Covid there is an increasing
premium attached to reducing travel and visits to healthcare facilities unless absolutely necessary. Sleepiness is now a bigger problem for road safety than alcohol, so identifying sleepy drivers is a key priority and a simple phone app could give drivers their own safety profile. ‘Replanning services throughout the NHS may help clear backlogs caused by the lockdown, and better match supply and demand in the coming years. On the back of the current work, a sub-group from Track and Know has been awarded a grant from Innovate UK to address exactly this challenge. Watch this space!’
@scwmagazine |
www.scientific-computing.com
vchal/
Shutterstock.com
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38
