This recent Deloitte healthcare analytic report came out with focus on big data. The report aims to identify current state of data analytics as well as emerging trends in healthcare.
The 30-Second Recap?
Most organizations believe data analytics is important, but less than half have a clear strategy to approach it. Few (5 of 50 surveyed organizations) anticipate an increase in budget. Those who did invest describe the most important drivers as improve clinical outcomes, deliver value-based care, and reduce operating costs.
And then there’s my favorite figure, reproduced from the Deloitte report:
The most influential perceived barriers to acquiring healthcare data analytic capabilities include culture, fragmentation of ownership, and access to skilled resources.
What Does All This Mean?
If you believe in a future where data analytics dominate healthcare decision making both on the business side and clinical side, then it follows that this curve best explains the trajectory of big data adoptation based on the famous Geoffrey Moore book.
While innovators continue to exist in healthcare data analytics, in my opinion the dominant force of the market has moved on. With emerging commercial solutions entering healthcare space, and most organizations contemplating – though not yet ready to invest in – data analytics, we now sit in an early adopters stage of the curve.
Early adopters are described as visionaries, those not willing to adapt a heavily flawed product but willing to invest significant effort to gain a competitive advantage. They don’t just want improvement. They adopt for a breakthrough.
It also means that the industry has not yet garnered sufficient momentum to sweep ahead full speed. The exponential growth portion of the curve has not yet happened.
We come up to the chasm. The chasm is where a technology either become a runaway mainstream success (smartphones) or remain niche (Boogie boards). If you believe healthcare data analytics is here to stay, then the chasm is your greatest opportunity. This is the moment when early adopters have ironed out the wrinkles and are ready for mainstream implementation, the moment before the bulk of organizations begin to jump all-in, the moment when you take off running while others hold their breaths.
This means if you lead a large organization. This is the time to seriously consider an organized strategy for data science and tackle culture; this is the time to centralize data access. Complacency with what works today is the recipe for tomorrow’s obsolescence in the world of data science.
And if you are a mere mortal like me, this is the time to acquire the relevant skills in medical informatics that will make you indispensable. It means to join a group like Society for Imaging Informatics in Medicine (SIIM) or American Medical Informatics Association (AMIA). It means picking up one of many approachable online videos/tutorials and write your first line of code.
It means that the next time you see something Google or Facebook can do but your electronic medical record can’t, think: the technology is already there, and I should be part of it.