Considered to be one of the UK’s most precious institutions, it's no surprise that the National Health Service (NHS) has been ranked as one of the most important issues in this year’s general election. Phrases like ‘building an NHS with the time to care,’ will no doubt be ringing in the ears of the electorate and public alike over the next month.
Electoral pledges have been focused on investing more funding and human resources, joined up services from home to hospital, and guaranteeing appointments. Whilst investment is imperative, I’m keen to point out how the NHS can be more efficient and how big data could dramatically cut our country’s care bill.
It goes without saying that the NHS is a relentless producer of data – be it patient data, performance data, clinical data or textual unstructured data. However, despite the breadth of a nationalised health care system, this data is immensely siloed. Hidden within this data are valuable insights that can identify trends, potential outbreaks in diseases or peaks in medical conditions. This data, if used correctly, could be the pill the NHS has been looking for.
One example of data being put to good use is at Royal Brompton & Harefield NHS Foundation Trust, where they have in excess of 400 data systems and 20 clinical data sets. It uses data to make evidence-based decisions based on its research into heart and lung conditions. It has been able to bring numerous disparate data sources together into a single system. A single source of the truth makes it possible for a consultant to eventually have all the information about a patient immediately in front of them, giving a much clearer picture for earlier decision-making – e.g. whether to provide emergency treatment.
The Trust also uses advanced analytics to uncover unexpected or less obvious connections between data. This could be a previously unknown link between a medication and a certain condition, or links between lifestyle and recovery from operations. Crucially, the analytical capability means these insights can be rapidly identified. For example, in certain situations patients may no longer have to go into hospital and sit in a waiting room for hours. Through better understanding of the patient from their data, it may be possible to intervene early and deal with their situation in a different way. It should also lead to more rapid diagnosis and treatment. All these outcomes deliver obvious benefits in reduced costs and lower demands on services.
Using big data analytics techniques more widely could anticipate pandemics or peaks in demand on A&E (accident and emergency) services. This is where significant efficiency savings can be found by putting additional resources into A&E to manage expected peaks, and vice versa when demand subsides. The data can also be analysed to delve deeper into what causes peaks and troughs in demand, further improving predictions and resource allocation.
Our greatest medical discoveries are borne from repeated testing and repeated failure. Much effort is being spent across the health care industry to understand the correlating genome sequence for many patients covering different conditions and outcomes. This creates masses of data, but the good news is that the technology can now process all that data much quicker than before. Already medical researchers have begun tailoring treatments for different patients. For example, clinicians can prescribe drugs with a varying level of toxicity according to how susceptible the patient might be to treatment, which is based on the evidence from data.
Big data analytics provides a quicker route to efficiency savings and new breakthroughs, which can help revolutionise the NHS – unlike blindly throwing more money at it.