From the drug companies and the insurance companies to the hospitals and clinics -- it's an enormously complex and fast-moving value chain that has a single goal: to help each and every one of us live better and longer lives.
If one were to ask the question "in what industry do you see the fastest adoption of cloud and big data", the answer -- at least for me -- would be healthcare.
A Unique American Perspective?
Unlike most parts of the world, healthcare in the United States is clearly a business. Sure, the federal and state governments subsidize certain aspects, but every participant is competitively motivated to deliver ever-better services to more people at ever-lower costs.
As I travel, I haven't seen the degree of competitiveness driving innovation and rapid technology adoption across healthcare as I do in the United States.
Not even close, if I'm being honest.
Yes, our US healthcare system has its various and sundry challenges, but one redeeming quality appears to be the rapid pace of evolution and improvement. There's a heckuva lot of money on the table (not to mention lives at stake), and there's a deep roster of motivated players with plenty of resources to invest.
So, let's start on our armchair tour of how cloud and big data are combining to revolutionize healthcare, especially in the United States ...
The Supply Side -- The Drug Companies
Clouds and big data are really nothing new to the companies that research and manufacture drugs, therapies and devices.
Some of the earliest and most productive clouds can be found at drug companies doing all forms of research.
The underlying science is demanding ever-more elastic compute, served up in a convenient, self-service fashion.
Drug effectiveness in the field is essentially a big data application, especially when correlated with other health and treatment information, demographics and more. The more data from more sources, the better the insight.
And, as a result, the underlying data repositories now appear to be growing at hyper-speed.
Perhaps the most extreme example of this are the newer genomics companies. Yes, an enormous amount of data is generated by sequencing genomes, but that specific data is of limited use unless it's correlated with actual life histories, therapeutic results and more.
The underlying formula here appears to be (big data) * (big data) * (big data) -- or more.
Indeed, within these organizations, you'll sometimes find two distinct IT functions -- one pointed at the more traditional back office, desktop, collaboration sorts of things, and a second one that's directly aligned with the researchers.
More and more of these research functions are already pursuing a hybrid cloud strategy: invest in on-premises infrastructure for the more predictable infrastructure needs, and use external cloud providers when the big stuff comes along. (insert link here)
The prognosis? More cloud, more big data -- a lot more. Why? It's core to their business model -- finding new drugs, therapies and treatments that do more and cost less.
The Payers -- Insurance Companies
By comparison, the healthcare insurance companies appear to be on the precipice of being completely transformed by both cloud and big data. There's a lot going on in their world. Old IT thinking is quickly giving way to new IT thinking, and it's rather cool to watch -- from a safe distance, that is.
For starters, many of then are moving from a B2B model (selling healthcare coverage through employers) to a B2C model where they have more of a direct relationship with their ultimate customers. Web portals, mobile apps, self-service tools -- it's a new world for IT.
Insurance companies want to know as much as possible about their customers -- not only their health histories, but their demographics, lifestyle choices and more. Not only does this help them coach their clients to make healthy choices, it helps them price coverage accurately.
They also care a lot about the costs and outcomes about the delivery sides -- what treatments work best in which circumstances, which hospitals and doctors are most efficient (or not) and more. It's no mistake they should be highly motivated to collect as many sources of information as possible, and invest heavily to glean continual nuggets of insight and understanding.
Although there are a few promising efforts, I think insurance companies will need to either continually invest in improving their big data analytics capabilities, or perhaps be acquired by those that do. Better understanding of massive and diverse data will be a key lever in all of their business models.
Not only are many healthcare insurance companies actively building internal private cloud models to gain efficiency and agility (I know, I meet with them regularly), but there's a specialized service provider market emerging as well -- our good partner CareCore comes to mind. It's not hard to imagine a complex ecosystem of service providers forming to support different aspects of healthcare insurer's needs.
The prediction? A very different industry and very different IT landscape within a few short years. Why? Both cloud and big data appear absolutely core to healthcare insurers' business model, and those that make the investment sooner should gain a not-inconsiderable competitive advantage.
The Delivery Side -- Doctors, Nurses, Hospitals and Clinics
The IT you find in these environments appears to be moving quickly to a hybrid cloud model. Skilled healthcare workers are inevitably in short supply, and -- not surprisingly -- they're also very demanding of their IT environments.
As a result, IT organizations in these settings tend to be incredibly agile and responsive to their users' needs. You don't have to lecture them on the whole aligned-with-the-business thing. They get it.
Fortunately, many hospitals and clinics were early adopters of VMware, so it shouldn't be a surprise that they're now moving to fully-virtualized, pooled-resource models ahead of other industries.
It's easy to understand why -- these organizations are under enormous cost pressures, and they want to spend every available dollar investing in delivering better healthcare vs. investing in IT resources.
A few larger and more progressive hospitals are now moving to becoming specialized IT service providers for the smaller players in their region, using their IT capabilities to allow healthcare delivery organizations of all sizes access to world-class IT.
The big data side is where there's the most potential. Not only do healthcare delivery organizations generate an enormous amount of potential raw input to analytical engines, they're also at the point where predictive analytics can do the most good -- when the patient is being treated.
It's unlikely that most healthcare delivery organizations will be able to invest in large-scale analytics capabilities and their associated information bases -- even though they often do their own research -- but they can easily consume those provided by others, such as the insurance companies and drug research companies.
Indeed, there's the glimmering potential of someday having large-scale healthcare analytics capabilities that ingest data from the point of capture and in turn provide real-time predictive capabilities on specific courses of treatment.
Not to mention, perhaps generating some interesting revenue streams from the data they can sell to both drug companies and insurance companies :)
A Few Final Points
Yes, there are good arguments as to why healthcare shouldn't be driven by the profit motive, but - in doing so -- amazing and transformative forms of healthcare are now being created that will ultimately touch all of our lives. Capitalism does have its positives ...
The more compute that's easily and dynamically accessible, the better. The more data that can be assembled, harnessed and correlated, the better. If it's linked and aggregated across organizational, industry and geographical boundaries, so much the better.
More data, more insight, more value -- it's the fundamental equation of big data analytics.
Luke Lonegan of Greenplum recently coined an interesting phrase to describe the key technology thinking here -- "computable storage" -- where analytics and data can be freely combined, integrated and scaled at maximum performance and minimum cost.
No, technology itself doesn't appear to be a key inhibitor, nor in reality the costs associated -- especially in light of the rewards. And, yes, key talent is scarce, but that will come in time.
No, it appears that the fundamental barrier to the bright new world is staring right at us in the mirror.
It's ourselves -- and our natural need for privacy and confidentiality, as expressed in various regulations, customs and practices.
Our innate desire for data privacy must ultimately be balanced against that exact same rich data being a key raw ingredient that -- when aggregated -- may lead to a greater good for all.
I believe that the era of big data and cloud transforming healthcare -- in all its aspects -- will ultimately be gated by this fundamental change in our collective perspectives.
Would I be comfortable sharing some of my most personal and sensitive information if it someday leads to helping many other people?
Years ago, people donated their bodies to science. Tomorrow, we may be asked to donate our data to science.
That's something we're all going to have to think long and hard about ...
By: Chuck Hollis
