Science is a data game – there seems to be endless amounts of information, which means some of it is bound to get lost in the shuffle. Unfortunately, as we move in the direction of precision medicine where treatment is targeted to the individual, we need all the data that is available to be included in a patient’s treatment decision. The cure for what ails one person may be buried in the success or failure of another’s treatment. First, we have to uncover it – then, there needs to be a way to manage it.
Can better information management through advanced big data tools help us reach a point of affordable precision medicine? Here’s what we know about the current state of big data in healthcare.
R&D As A Data Trap
One of the primary reasons that precision medicine is so expensive is because of the sheer amount of prior effort that goes into developing treatments. And when the results of R&D, whether positive or negative, aren’t communicated clearly or data is lost in the process, that drives cost up even further and delays needed treatments to patients. Overall, R&D has the technology needed to develop new treatments, but not the data mining tools needed to assess past research.
More Read
Competitive research organization should focus on interdisciplinary thinking and information processing so that a greater number of team members have the ability to identify valuable data. Assessment needs to be a shared skill, but it needs to be bolstered by the ability to meaningfully filter through past clinical trials, case studies, research and articles, meeting abstracts, papers and proceedings, etc., from decades past.
Differentiating Data
At its core, precision medicine is about identifying actionable data and acting on evidence. After all, one of the reasons that medical care costs so much is that the system is rife with waste – in both time and resources. Patients are given the wrong care at the wrong time, leading to suboptimal outcomes and lost windows for treatment. They get sicker while being offered care without evidence it will work for them, for the simple reason that it’s the standard treatment.
If we design systems that collect more actionable data within an enhanced UX framework, we’ll be better positioned to put that information to work. Often, what we need to know in medicine is trapped within a study with entirely different aims. When it comes to precision medicine, we need to be able to differentiate each data stream, each part of a data set, because sometimes the right treatment is the accident from someone else’s work.
Finding A Market
It’s hard to prove there’s a market for the data systems we need to make precision medicine work because right now very few people can afford such care. In fact, drug costs are so high that many think something unethical is afoot in R&D when what’s going on might just be bad data management. For now, we’re stuck trying to prove people will benefit from treatment they can’t afford in order to develop systems that will make it affordable; it’s a data systems catch-22.
To overcome this paradox, we’ll need different business models, potentially models that bring together big data innovation with healthcare R&D; a joint tech-healthcare undertaking may be profitable when nothing else is. Patented data processing systems would undoubtedly recoup their own costs if licensed to other healthcare companies. It’s a matter of pursuing the innovations that will make the whole industry profitable. We need to shift R&D funding to the tech side to advance the medical side.
Precision medicine is the future – and it’s going to remain the future for now. Big data will change the industry. Until then, patients are waiting.
