Everyone’s talking about the importance of big data in healthcare. Yet, as the data piles up – most of it is isolated in different silos, and health systems are struggling to turn big data from a concept into a reality. Here’s how I see it having a substantial impact on the health of populations, today and in the future.
Most healthcare organisations today are using two sets of data: retrospective data, basic event-based information collected from medical records, and real-time clinical data, the information captured and presented at the point of care (imaging, blood pressure, oxygen saturation, heart rate, etc). For example, if a diabetic patient enters the hospital complaining about numbness in their toes, instead of immediately assuming the cause is their diabetes, the clinician could monitor their blood flow and oxygen saturation, and potentially determine if there’s something more threatening around the corner, like an aneurism or a stroke.
Pioneering technologies have succeeded in putting these two data pieces together in a way that allows clinicians to grasp the relevant information and use it to identify trends that will impact the future of healthcare – otherwise known as predictive analytics. So for example, if more diabetic patients start to present a similar trend of numbness in their toes, the coupling of real-time and retrospective data can potentially help doctors analyse how treatments will work on a particular population. This gives hospitals a much stronger capability to develop preventative and longer-term services customised for their patients.
But what if we take data a step further and introduce gene sequencing into the picture? Today, gene sequencing is used primarily to determine the course of treatment for cancer patients. As gene sequencing becomes more common, the cost may fall, making it more likely that we’ll see gene sequencing become a routine part of a patient’s health record. Imagine the kind of impact this data will have on treating infectious diseases, where hours and even minutes matter. The next time there’s a disease outbreak, we could potentially know the genome of the infectious organism, the susceptibility of the organism to various antibiotic therapies, and therefore determine the correct course of action without wasting precious resources in trial and error.
Undoubtedly, we have yet to determine the most practical, cost-effective way to manage this kind of data. To put it into perspective, the human body contains nearly 150tr gigabytes of information. That’s the equivalent of 75bn fully-loaded 16GB Apple iPads, which would fill the entire area of Wembley Stadium to the brim 41 times. Imagine collecting that kind of data for an entire population.
There’s no doubt that this is a mammoth task, and while we might not be there yet, we are certainly getting closer. There are still challenges ahead: organisations are learning lessons from the early adopters and trying to determine the best ways to cooperate and share data. Undoubtedly the amount of investment required to make big data technologies work is more than what a single segment of the market can afford. That means all stakeholders, including pharmaceuticals, will have to work towards a common vision. But with public-private partnerships paving the way for payers and providers to work more closely together, we are heading towards success, and more importantly, better patient care.