We live in an age where information, or more importantly our information, is everywhere. The digital age has revolutionised our concept of data and now, with smartphones and social media, the list of who knows what about us is ever growing. There are masses of data out there that can be collected and used to predict all sorts of things about us from where we’ll buy our next outfit to where we’d like to go on holiday – and companies have been using our data to do just this for years. So, with all of that information at our fingertips, shouldn’t we be using it to answer much more vital questions?? Can it serve us better in clinical testing scenarios versus more traditional methods?
Clinical testing or clinical trials are designed experiments, commissioned to prove or disprove a hypothesis (for the purpose of this article we will talk drug trials). The issue can be that in an experiment being ‘designed’, it can exclude or overlook certain variables which may actually be of vital importance to the outcome.
Big data presents a solution to the above problem, by capturing data in large volumes from many places and providing a more complete view. So what is big data, how can it help to improve clinical trials, and what are the barriers to its use today?
What Is Big Data?
First of all, let’s explain what big data is. Big data is exactly what it says on the tin; It’s a large amount of data collected through various means such as social media, machine to machine transactions, audio, video, email, text documents, etc. Basically, any information that can be stored in any format, structured or unstructured, falls under the umbrella term ‘big data’. In essence, the more data the better!
But how can we use it in meaningful ways?
How Do Clinical Drug Trials Work Now?
Currently, clinical drug trials, testing a drug for safety and fitness for purpose, are divided into phases. The earlier phases of a trial will test the safety of a drug and what the side effects of that drug are, whilst the later trials will measure how that drug compares to similar drugs on the market. The problem with this process is that patients are chosen deliberately with set characteristics, usually with no illnesses at all, or any other presenting complaints other than that which the drug is being tested against, and put through specific conditions which are engineered to achieve the desired results. There is also a strong sexual bias in clinical testing, too.
With too little variables in the equation, this can lead to problems when the drug is then released to the wider market and is prescribed to individuals on a vast scale.
As well as the risk factor of inadequate variable testing, clinical trials are massively expensive to design and implement. In 2006, medical trials in the USA cost over $25 billion. Pharma companies can spend anything up to 800 million dollars per drug candidate; quite a large sum when you consider that 15-20% of trials never make it to human testing.
Cancer Research UK reported that it can take between 10-15 years for a drug to make it through clinical trials, meaning potentially life-changing drugs are being kept off the market for longer because there just isn’t enough information available from traditional clinical trials to assure a drug’s safety and get them to those who could greatly benefit.
Glen de Vries of Medidata talks about how big data can help – “It provides a real-world picture of how the patient is doing and that allows us to better record how the drug affects their quality of life, which is ultimately what has the biggest impact on patient and prescriber preference.”
What The Industry Thinks
The medical community has been that there is something missing from clinical trials and testing, and that big data could very well be it. A 2012 study stated that 2.5 quintillion terabytes of data were generated daily! Whilst many other industries have been using big data to their advantage for a number of years, health has only just begun to venture away from traditional methods. Odd, as big data can play such a pivotal role in gathering evidence for the safety, effectiveness and outcomes of treatment options we make available.
Big data has also proved to be useful in many other areas of the medical industry. For example, using the NHS database, J.P. Errico (Chairman and CEO of electroCore) and his team were able to prove a theory; people who suffer from headaches are more likely to suffer from other conditions, seek GP help more often and be prescribed more medication than those who do not. Using Big Data, they were able to pinpoint 7-8 more conditions that a patient may suffer from than if they had used traditional research. This allowed them to have a greater understanding of the cause of these types of presenting symptoms, and to treat them more effectively with more complete information.
Many industry professionals believe Big Data is the future of clinical trials and that the current trial system has its flaws, producing results that in cases can be seriously harmful to the patient. In a world where technology is continuously advancing and astronomical amounts of data can be collected in simple and cost-effective ways, it is irresponsible that healthcare is not utilising big data l in order to provide the very best healthcare possible and deliver better outcomes for patients