Many people I meet who work at the intersection of healthcare and technology can get frustrated.
On one hand, the current healthcare delivery system in the US, with many obvious and visible opportunities for improvement.
On the other hand, the tantalizing power of newer technologies: cloud, big data, mobility, etc.
Historically, many people like me have looked at the situation, and tried to figure out why supply and demand weren't getting properly connected.
The technology is there -- why isn't being consumed?
In a nutshell, my opinion is that the industry wasn't organized for success. There were inherent structural barriers that inhibited the widespread adoption of the newer technologies and approaches.
But, with the recent rise of ACOs (accountable care organizations) and their brethren, a few of us are extremely optimistic that the next few years will be very different than the past.
A Simplistic Background
In the United States, healthcare is largely a for-profit business: fee for service.
It doesn't take an MBA to realize that this can lead to some interesting outcomes: a strong financial incentive to offer more services (and more expensive services) to drive revenue, a strong incentive to lower the cost of providing those services as well as a strong competitive ethos in many markets, as -- in essence -- you're competing for patients.
Not all of these outcomes are in everyone's interests -- as individuals, or as a society.
From my perspective, the first wave of change was driven by the large payers -- the health care insurance companies. Their business model was simple: contract with employers to provide fixed cost healthcare coverage against a variable (and sometimes unpredictable) cost base.
They, in turn first focused on reducing costs through pricing power, later followed by a more progressive view that healthier employees meant less demand for health care services.
But, in the United States, there's one insurance payer above all others -- the US Government via Medicare and -- to a certain extent -- Medicaid. Their size means that they have a unique ability to force structural change in the industry.
Two years ago, the Patient Care and Affordable Health Care Act of 2010 was passed by the Obama administration, and -- while certain components are being debated in the US Supreme Court -- a often overlooked part of the legislation is the new bargain with health care delivery organizations.
The incentive is simple: if your healthcare delivery organization focuses on health care outcomes (vs. simple service delivery) and saves the government money in the process, the government shares some of the rewards with you.
Do better than your peers, and you can make a lot of money indeed.
All of the sudden, big piles of money are on the table that now create powerful incentives for health care organizations to change their models -- and quickly.
And, in my humble experience, there's no incentive for rapid change quite like the potential of big piles of money :)
While there's definitely speculation as to whether or not this specific legislation will proceed unimpeded, the future is clear: we're heading to a model where healthcare providers will be strongly incentivized for results (efficiency of wellness results) vs. effort (how many services were delivered and their costs).
Structural Change In Health Care Delivery
A significant amount of healthcare services in the US flow through smaller providers: individual practices, modest clinics, regional hospitals and the like. Now, if you're going to play in this new Medicare game (or any game like it), size matters -- a lot.
- You have a strong interest in providing cost-effective, shared back-end services, with IT being a hot topic right now.
- You have a strong interest in aggregating and integrating different components of the overall healthcare delivery model -- from wellness counseling to pharmacy to even providing your own health insurance products.
- You aspire to be a "wellness delivery system" as opposed to a traditional hospital, for example.
- And, above all, you quickly gain an appreciation for the power of predictive analytics to "change the game" as far as modeling outcomes.
Cloud Is Big
I've met with about two dozen healthcare IT organizations, and the conversation is getting far more predictable.
There is structural change afoot in our industry, driven by new legislation that creates powerful incentives for change. As a result, we're going from a world of isolated and small IT functions to aggregated and integrated ones, usually through acquisition and/or affiliation.
We've brought in a new IT leadership crew to set strategy and accelerate the transition. We need to look more like a competitive IT service provider, and less like a traditional, project-oriented siloed IT shop.
And we need to do it sooner than later.
Needless to say, we've got a lot to say to the IT teams who are in this situation. And we expect many more to raise their hands before too long.
Mobility Is Big
The first round of "mobility in healthcare" was mostly around the healthcare providers -- giving them increased access to information and applications as they moved through their workspaces.
Nothing wrong with that -- and it's still an important topic.
But if your new mission is accountable health care, all of the sudden there's this incredibly strong incentive to extend your "mobility platform" to include your patients as well. More patient interactions -- at lower cost -- always means better healthcare outcomes.
In addition to the predictable mobile apps, think about video chat, the usefulness of text message reminders as well as eventually purpose-built sensors that easily attach to mobile devices -- like blood sugar monitors.
Big Data Analytics Is Big
No one -- but no one -- in this industry denies the amazing power of predictive analytical models powered by big data to produce a continual stream of meaningfully superior patient outcomes. They may use different terms across different specialties to describe the effect, but the basic thought is always the same. More data and more resources always means better outcomes.
But prior to the rise of next-gen ACOs and their brethren, there were serious structural problems in assembling the data and the resources.
One, clearly, was access to relevant data about the patient. Not just isolated dribbles of clinical data -- we're talking *all* the data from *any* potential source. Thanks to privacy regulations, drug and therapy companies can't get to it easily. Nor can health care insurance providers. Nor can various government entities.
But the new breed of ACOs (or any other managed care delivery organization) have privileged access and a trusted relationship with the patient.
I don't know about you, but I tell my doctor *everything* (or should!) because if he or she has more information, they can make better recommendations.
I, as a recipient of healthcare services, have a powerful incentive to make sure my doctor has free and easy access to anything at all that might be relevant about me and my healthcare.
In essence, the new ACO is an attractive and privileged "assembly point" for diverse data around a patient.
The second was scale. Doing meaningful work in big data analytics requires a significant investment in both IT resources and some very, very smart people. Being able to amortize that investment over dozens (or hundreds!) of healthcare delivery points makes their use far more cost-effective.
The third was horizontal integration. If all you do is blood lab work, all you've got access to is mostly those results.
However, if your "wellness delivery organization" incorporates the entire value-chain of healthcare services, you've got ready access to a wonderfully rich and easily correlatable world of data sets -- and that's *before* you even start to go outside the organization.
And, finally, strong financial incentives need to be in place to build, produce and act on the insights in a cross-functional way.
Thanks to the new incentive scheme courtesy of the US Government, that appears to be clearly in place for a sizable population of Medicare recipients.
And if this one doesn't do the trick, I'm sure there will be others.
Do Healthcare IT Organizations Get It?
Yes, but they're all coming at it from wildly different perspectives -- at least, based on my interactions so far.
There are plenty of "we need to consolidate and streamline our IT function to reduce costs" discussions. A few of these have progressed to value generation vs. simply doing what you did last year for less money. But not enough.
There are a handful of healthcare delivery organizations who take a strong interest in mobility, but it's almost always pointed at doctors and RNs -- vs. the patients themselves. I think that should start to change in the next year or so.
And -- wonderfully -- I've now met about a half-dozen very progressive groups who are starting to get the big data analytics bug. Their passion is contagious -- they're on a mission to change the world using better predictive models. It's hard not to share their enthusiasm.
To this day, though, I have yet to encounter anyone who can articulate that end-to-end vision around technology enablement in the new world of accountable health care.
But, hey, it's early days :)
Chuck,
Thanks for your insightful "state of healthcare" review. I find that there is one more facet to healthcare that makes the data/information analytic landscape even more compelling, and that's an interesting book by David Weinberger, Too Big to Know.
Dr. Weinberger brings forward the notion that books and traditional learning are discontinuous. And that there is emerging, due to the hyperlinked information universe, a massive ecology of interconnected fragments that continually act with the power of positive and negative reinforcement. The net is that traditional views, or in David's vocabulary "Facts" are largely based upon constrained reasoning, and that as the basis of this reasoning changes with the arrival of new facts, so should their interpretations.
There are a number of entities sitting on top of 30years of historic clinical data, the VA, certain ACO's and certainly academic medical centers that can chart many of the manifest changes in treatment planning and outcome, but may themselves, because of their constrains present significant correlation. But just the same, many not be able to discern accurate causality due to a lack of completeness of the information base - maybe genomics, proteomics, or the very complex nature of the human biological system.
The interconnectedness of the clinical landscape is of paramount value in the establishment of correlation, and derived causality, and that with the increase of new information, traditional best practices can be [and needs to be] constantly re-evaluated.
I believe that, this lack of causal understanding, due to highly constrained reasoning, has lead to many of the derogatory statements about todays outcome based care model. As todays outcome measures are based more on correlation than on causality, determining causal factors, and then understanding the right control structures should improve the operationalization of care. I further believe that, as the availability of substantially more complete clinical information, across higher percentages of populations, will lead to improvements in these outcome measures and the controls that can affect them. The net result of getting this right is improved outcomes at decreased costs, and effectively turning the practice of medicine into a big data science with a more common methodology and more predictable outcome. In effect, full circle, operationalized predictive analytics.
Posted by: DanHushon | March 30, 2012 at 01:04 PM
Wow -- thanks for sharing your thoughts!
-- Chuck
Posted by: Chuck Hollis | March 30, 2012 at 01:47 PM