By Jason Burke, System VP, Enterprise Analytics & Data Sciences, UNC Health Care
Jason Burke, System VP, Enterprise Analytics & Data Sciences, UNC Health Care
Why can we predict hurricanes and snow storms days to weeks in advance, but healthcare struggles to know what will be happening with patients tomorrow? Future historians will likely consider our current era of human development as transformative in our ability to treat disease and prolong human life. And yet many patients and healthcare CIOs could likely agree that healthcare today is a somewhat chaotic system that too often does not exhibit the same mastery of performance found in other chaotic systems such as weather, combat operations, racing, and others. If high performance can be characterized as reliably exceeding expectations consistently and affordably despite variability in conditions, US healthcare—with its unsustainable escalations of cost without corresponding superior health outcomes—has ample room for innovation.
Part of that opportunity can be framed as an over-reliance on Business Intelligence (BI). Whereas retrospective reporting capabilities like BI are very useful, they provide few insights into what is likely to happen with patients right now, what will be happening tomorrow, what are the contributing factors, and where are the opportunities to influence outcomes.
Over the past eight years, the federal focus on electronic medical records adoption has opened huge doors to data-driven transformation. Historically, less than 20 percent of physicians used electronic health records; that number is now over 90 percent. Given these large and growing repositories of health-related data, if BI alone doesn’t deliver high performance, how can health organizations more effectively leverage this data to transform every patient’s outcomes and costs?
In considering this and other questions, capabilities for enterprise architecture begin to emerge.
Health care has started on this journey with the adoption of electronic records systems, though many of those systems were designed more as electronic filing cabinets. Incorporating other data—genomic data, business process measures, medical devices, consumer wearables, external data aggregators, and many others—provides opportunities to develop more sophisticated models of medical decision support. But it requires an enterprise architecture that can handle “big data” with greater agility than the systems of the past. And it requires organizations embrace disciplines such as data governance to improve the quality and semantic utility of their data assets.
The value of information is highly time sensitive. Whereas many health care organizations are well positioned to handle the batch loading of periodic data files, capabilities associated with real time interoperability across heterogeneous systems and environments are far less mature. But newer standards like FHIR are demonstrating the promise of greater interoperability both within and across IT architectures.
Recently, health care providers and insurers have been designing and implementing new models of care management and reimbursement that focus on empowering stronger health teams—physicians, specialists, care managers, nurses, and others—to better support patients and their care needs. In order to manage each patient’s health risks, team members need greater visibility into shared business processes.
Advanced analytics can change how we respond to medical problems, but it can also help to prevent those problems from ever occurring in the first place.
For technology-savvy leaders—CIOs, CTOs, and increasingly Chief Analytics Officers—there has never been a more exciting time to work in healthcare.