Healthcare is shifting to an ecosystem approach, one that values cognitive support at the point of care for providers, care givers, care coordinators, patients, members, and consumers.
For payers, this represents a real opportunity, and I believe that those payers who choose a holistic strategy—and use technology to support the patterns that will help them deliver a multi-channel engagement model to their ecosystem—will be key players in the fight to drive collaboration, improve quality and outcomes, and lower costs.
I’ve talked a lot about the concept of population health, how it represents a shift to a model where we’re more proactive about care.
But the concept’s foundation is still firmly rooted in evidence-based medicine which, despite its capabilities, will have limited effectiveness in the future we’re moving into.
But I promise you: precise medicine will be effective. And I believe payers will play a significant role in its adoption as they recognise the value of micro-stratified population care, which will call for the entire industry to be much more proactive in its care models and adopt real-time technologies that don’t require truck-loading data to different workflow engines, patient tools, or provider tools.
An explosion of data types
From clinical data to genomic data to pharmacy data to claims data to device data created by wearables, we’re already seeing data sets appearing everywhere. In the coming years, we’re going to see device manufacturers create all kinds of highly specialised gadgetry that generates heaps of data, from sensors that note changes in glucose levels and dispense insulin like an actual pancreas, to facial masks that help users voluntarily move weakened cheek muscles.
This gadgetry will generate a lot of data—up to two terabytes per person—that will, if utilised properly, drive a much more precise care plan for its users.
But what sort of systems can handle all that new data? Certainly not the same systems that handle the traditional clinical, claims, and pharmacy data today. Some of this new data is serialised, streamed, or unstructured. Some, like variation data, is exceedingly large, and I expect that that’s the sort of data that will find its way into the solutions that payers will be offering to their patients and providers.
That’s why I think key payers will be key players—their very way of doing business will require the healthcare industry to change.
Still, a clinician can try to navigate the payer’s new world as much as she wants, but with up to two terabytes of variables plopped in front of her, how is she ever supposed to base a meaningful decision on that data in the fifteen minutes she’s allotted for a patient? It’s just not possible.
What is possible is the use of a real-time system that will crunch that data and enable that clinician to have the cognitive support to make an informed decision right then and there.
Yet despite the feasibility of such a system, today’s systems aren’t built that way. Instead, they’re built to accommodate a sequence like this:
- A transactional system dumps data into an operational store
- That operational store dumps that data into a data warehouse
- That data warehouse does some analytics
- The results are dumped into a workflow or engagement engine
That sequence won’t fit in the data-rich future coming up. That future will require payers to tie all of these systems together for their providers by using tools that can stream that data in real time.
About a decade ago, my oldest son, Carter, was diagnosed with cystic fibrosis (CF).
Like most kids with CF, Carter had a host of physical problems, like lung infections due to mucous build-up and thrive issues due to pancreas blockage. In his eighth grade year alone, his lungs needed a thorough cleaning, so he was hospitalised and homebound for three consecutive weeks with a PICC line.
Four years ago, Vertex Pharmaceuticals released a drug designed to address Carter’s specific genetic variation of CF, one that only four percent of patients have.
But when I told Carter’s doctor about it, he said it wouldn’t help Carter because he didn’t have that genetic variation.
But after I pressed the doctor to review sixty pages of Carter’s data, the doctor soon reversed his position.
“This is a game changer,” he said.
Now let’s be clear: Carter’s doctor is a great doctor. But he didn’t have the tools to help him analyse that sixty pages of data and connect my son to a promising new drug therapy that went on to stabilise his lung function, end his annual sinus surgeries, eliminate his regular bronchial scopes, made his ED visits a thing of the past, and allowed him to thrive into a six-foot-two-inch, 225-pound captain of his high-school football team. Today, Carter’s off to college on his own, our payers don’t have to pay for all the procedures mentioned above anymore, and his mom and I don’t worry about him one bit.
That is the promise of precision medicine exemplified. But in the future, rather than rely on a highly interested advocate—like a parent who’s passionate about precision medicine—to provide that cognitive support, payers and providers will be able to rely on technology that synthesises and analyses the data (e.g., those sixty pages Carter’s doctor couldn’t effortlessly process) and utilise it in the right context at the right time.
What needs to be done?
Payers need to learn how to be much more proactive about care, embrace a system of engagement via the real-time sending of data to providers and patients, and utilise systems that support their current and future needs, which will require payers to make sense of that two terabytes of variation data, wearables data, genomic data, professionally monitored data, and social behavioral data that will drive healthcare.
This will depend on a different kind of patient record altogether, one that’s more actionable, more focused on engagement, utilises machine learning, and features a far more comprehensive set of dimensions that payers have a unique perspective on (e.g., clinical claims, care plans, and pharmacy data), and then treats those dimensions with the proper alignment, analysis, integrated models, calculations, and aggregations they deserve.
This might sound ambitious, but is supporting different clinical, social, behavioral, and genome data sets through open APIs any more ambitious than what Apple does with open-source, real-time-enabling, scalable software like Cassandra to support data sets for music and video files, notifications, messaging, backups, and more? Is it any more ambitious than what Netflix, Twitter, and Instagram—services that trade directly on their real-time reputations—do with their data sets?
It’s not. Biometrics yielded from wearables that track glucose, blood pressure, weight, activity, and more adhere to the exact same read/write process that tweets yielded from iPhones adhere to, the only difference being that that the “followers” in the biometrics case would be a trusted network authorised by the patient to view that data (e.g., providers, payers, care coordinators, care givers, and specialists).
In fact, with the exposure of so many APIs—including standard APIs using FHIR, non-standard APIs, and aggregated APIs—in a precision-medicine platform like the one I’m describing comes a real opportunity for innovation that’s limited only by our imaginations.
- Clinical-services data to providers who traditionally were never exposed to such information (e.g., identities within a circle of care, relationships with specialists, and a range of social-networking activities)
- A patient panel to a provider partner with a particularly useful care management or care coordinator use case
- Different types of APIs to a payer’s partners, vendors, or even their vendors’ development teams in a way that eliminates the possibility of it being taken down by denial-of-service techniques
When viewed this way, precision medicine represents much more than the shift described in the beginning of the post—a shift to a model that population health currently represents, where we’re merely more proactive about care.
If you’ll indulge the analogy, it represents a sort of positive “hydra” (the serpent in Greek mythology that grew back two more heads whenever one was cut off). In this case, a new service emerges with every piece of data collected and API exposed, and when our imaginations are applied to that service, more services then emerge.
With conditions like that, who can say what ingenious innovations, methods, and techniques are on their way?
It’s impossible to know.
But I can tell you what I do know: with the right participation from payers, precision medicine will work, and our journey as a society is about to get healthier and happier because of it.
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