{"id":2049,"date":"2016-06-24T23:12:52","date_gmt":"2016-06-24T23:12:52","guid":{"rendered":"http:\/\/orionhealth-blog.local\/?p=2049"},"modified":"2024-08-13T01:19:16","modified_gmt":"2024-08-13T01:19:16","slug":"a-different-kind-of-patient-record","status":"publish","type":"post","link":"https:\/\/orionhealth.com\/global\/blog\/a-different-kind-of-patient-record\/","title":{"rendered":"A Different Kind of Patient Record"},"content":{"rendered":"\n

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.<\/strong><\/p>\n\n\n\n

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For payers, this represents a real opportunity, and I believe that those payers who choose a holistic strategy\u2014and use technology to support the patterns that will help them deliver a multi-channel engagement model to their ecosystem\u2014will be key players in the fight to drive collaboration, improve quality and outcomes, and lower costs. <\/p>\n\n\n\n

I\u2019ve talked a lot about the concept of population health<\/a>, how it represents a shift to a model where we\u2019re more proactive about care.<\/p>\n\n\n\n

But the concept\u2019s foundation is still firmly rooted in evidence-based medicine which, despite its capabilities, will have limited effectiveness in the future we\u2019re moving into.<\/p>\n\n\n\n

But I promise you: precise medicine will<\/em> 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\u2019t require truck-loading data to different workflow engines, patient tools, or provider tools. <\/p>\n\n\n\n

An explosion of data types<\/strong><\/h5>\n\n\n\n

From clinical data to genomic data to pharmacy data to claims data to device data created by wearables<\/a>, we\u2019re already seeing data sets appearing everywhere<\/em>. In the coming years, we\u2019re going to see device manufacturers create all kinds of highly specialised gadgetry<\/a> 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.<\/p>\n\n\n\n

This gadgetry will generate a lot<\/em> of data\u2014up to two terabytes per person<\/a>\u2014that will, if utilised properly, drive a much more precise care plan for its users. <\/p>\n\n\n\n

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\u2019s the sort of data that will find its way into the solutions that payers will be offering to their patients and providers. <\/p>\n\n\n\n

That\u2019s why I think key payers will be key players<\/em>\u2014their very way of doing business will require the healthcare industry to change. <\/p>\n\n\n\n

Still, a clinician can try to navigate the payer\u2019s 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\u2019s allotted for a patient? It\u2019s just not possible. <\/p>\n\n\n\n

What is<\/em> 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. <\/p>\n\n\n\n

Yet despite the feasibility of such a system, today\u2019s systems aren\u2019t built that way. Instead, they\u2019re built to accommodate a sequence like this: <\/p>\n\n\n\n

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  1. A transactional system dumps data into an operational store<\/li>\n\n\n\n
  2. That operational store dumps that data into a data warehouse<\/li>\n\n\n\n
  3. That data warehouse does some analytics<\/li>\n\n\n\n
  4. The results are dumped into a workflow or engagement engine <\/li>\n<\/ol>\n\n\n\n

    That sequence won\u2019t 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.<\/p>\n\n\n\n

    Carter\u2019s story<\/strong><\/h5>\n\n\n\n

    About a decade ago, my oldest son, Carter, was diagnosed with cystic fibrosis (CF).<\/p>\n\n\n\n

    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.<\/p>\n\n\n\n

    Four years ago, Vertex Pharmaceuticals<\/a> released a drug designed to address Carter\u2019s specific genetic variation of CF, one that only four percent of patients have.<\/p>\n\n\n\n

    But when I told Carter\u2019s doctor about it, he said it wouldn\u2019t help Carter because he didn\u2019t have that genetic variation.

    But after I pressed the doctor to review sixty pages of Carter\u2019s data, the doctor soon reversed his position.<\/p>\n\n\n\n

    \u201cThis is a game changer,\u201d he said.<\/p>\n\n\n\n

    Now let\u2019s be clear: Carter\u2019s doctor is a great<\/em> doctor. But he didn\u2019t 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\u2019s off to college on his own, our payers don\u2019t have to pay for all the procedures mentioned above anymore, and his mom and I don\u2019t worry about him one bit.<\/p>\n\n\n\n

    That<\/em> is the promise of precision medicine exemplified. But in the future, rather than rely on a highly interested advocate\u2014like a parent who\u2019s passionate about precision medicine\u2014to 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\u2019s doctor couldn\u2019t effortlessly process) and utilise it in the right context at the right time.<\/p>\n\n\n\n

    What needs to be done?<\/strong><\/h5>\n\n\n\n

    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. <\/p>\n\n\n\n

    This will depend on a different kind of patient record altogether, one that\u2019s 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.<\/p>\n\n\n\n

    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\u00a0any more ambitious than what Netflix, Twitter, and Instagram\u2014services that trade directly on their real-time reputations\u2014do with\u00a0their<\/em>\u00a0data sets?<\/p>\n\n\n\n

    It\u2019s 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 \u201cfollowers\u201d 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).<\/p>\n\n\n\n

    In fact, with the exposure of so many APIs\u2014including standard APIs using FHIR<\/a>, non-standard APIs, and aggregated APIs\u2014in a precision-medicine platform like the one I\u2019m describing comes a real opportunity for innovation that\u2019s limited only by our imaginations.<\/p>\n\n\n\n

    Imagine exposing: <\/p>\n\n\n\n