The adoption of precision health—the process, according to Stanford Medicine, of “tapping health data to provide targeted, predictive and personalized care”—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 sets to different workflow engines, patient tools, or provider tools.

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 specialized 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, per day—that will, if utilized properly, drive a much more precise care plan for its users. In the value-based era, where outcomes matter for reimbursement, care that is developed precisely for the unique needs of a patient seems like a trend that would evolve organically. After all, what better best practice for care than one that caters to the precise biology of an individual?

New data formats needed

Yet, one obstacle remains: The sheer amount of data. What sort of systems can handle all this new precision health data? Certainly not the same systems that handle the traditional clinical, claims, and pharmacy data today. Some of this new data is serialized, streamed, or unstructured. Some, like variation data, is exceedingly large. A clinician can try to navigate through this data 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 15 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.
This will require a different kind of patient record altogether—one that’s more actionable, more focused on engagement, utilizes machine learning, and features a far more comprehensive set of dimensions (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 (application programming interfaces) 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 the “followers” in the biometrics case would be a trusted network authorized by the patient to view that data (e.g., providers, payers, care coordinators, caregivers, and specialists).

APIs to the rescue

In fact, with the exposure of so many APIs—including standard APIs using FHIR, non-standard APIs, and aggregated APIs—in a precision health platform like the one I’m describing comes a real opportunity for innovation that’s limited only by our imaginations.

When viewed this way, precision health 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”—that is, 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 the industry, precision health will work, and our journey as a society is about to get healthier and happier because of it.

The original article can be found at the Health Management Technology site here.