In an attempt to improve the healthcare system there’s currently a movement from pattern-based treatment to personalised medicine. The focus now is that the right drug is given to the right patient at the right time, which means putting the individual at the centre. The agenda is to look for molecular markings and root causes which can help with diagnosis and treatment, which is where Big Data becomes significant.
The NHS is currently experiencing a second crisis: its inability to analyse data. There’s a huge amount of data to be dissected which goes beyond just medical data; genomic and socioeconomic data are incredibly important too, and provide the key to identifying the causes and solutions for numerous diseases. The problem lies with the major shortage of Data Scientists who can effectively analyse data, which means that all of these discoveries are being missed.
In order to demonstrate this, Austin Tanney at Analytics Engines conducted a live analysis on global Diabetes data, which yielded some invaluable and fascinating insights into the causes and solutions for this worrying global epidemic. It’s important to note firstly the scale at which Diabetes is affecting the UK population. Diabetes currently costs the NHS a whopping £14 billion a year, which means that since 2012, the cost has risen by over £4 billion a year. It’s clear that this disease is becoming an increasingly prevalent issue that requires additional support.
To conduct this analysis, Analytics Engines used 10 sources of Data including the Office for National Statistics, NHS Digital, Health and Social Care, and the Business Services Organisation. These resulted in 30 sets of data, which allowed Austin to make some palpable correlations. The first comparison he investigated was the rate of employment in conjunction with the rate of Diabetes. It revealed that Foyle as well and north and west Belfast have the largest rates of unemployment and the largest rates of Diabetes, demonstrating that people without steady incomes are more at risk of developing the disease.
It’s not just about employment rates however; Glasgow has a lower rate of unemployment than all three of these areas, but a higher rate of social deprivation and consequently a higher rate of Diabetes. In the US, they’ve been drawing correlations between salary levels and the rate of Diabetes. The state with the highest rate of Diabetes has an average yearly income of just over $44,000, which is lower than the national average of around $60,000, whereas the state with the lowest rate of Diabetes has an average yearly income of over $70,000. This signifies that the amount people earn annually directly affects their chances of acquiring the disease. The US has also been measuring obesity and inactivity levels from open data to draw links. Colorado has the lowest rates of obesity and inactivity levels in the whole of the US as well as the lowest rate of Diabetes, indicating that these two factors play a pivotal role in whether or not you may develop the disease.
What all of these findings show is that healthcare is complicated. The system can’t simply solve matters with a new drug; it requires complex solutions which is where Big Data is instrumental. Given the huge socioeconomic factors at play, we have to start thinking about policy change and the way money is invested by the state. By making changes to public policy we could prevent certain diseases like Diabetes, and presenting data in a relevant way would help to justify these changes.
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