| | February 201719UTLOOK Healthcare Tech 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.Real Time DataA seismograph that reports earthquake readings two weeks after the quake occurs does not lend itself to decision making and emergency responsiveness. The value of information is highly time sensitive. And whereas many healthcare 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.Over the past eight years, the federal focus on electronic medical records adoption has opened huge doors to data-driven transformationClosed LoopRecently, healthcare providers and insurers have been designing and implementing new models of care management and reimbursement that focus on empowering stronger health teams­physicians, specialists, care managers, nurses, and others­to 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. For example, if a physician refers a patient to a specialist for a consult, did that appointment get scheduled? Did the patient get there? Were any next steps identified? Were they followed? Just as racing teams need to know what car tunings produced high performing results, healthcare teams need to know what activities help patients along their journey to better health. Advanced AnalyticsPredictive modeling and simulation--techniques routinely used in weather forecasting, for example--will become foundational for improving the quality and costs of care. Part of the opportunity is in gaining a better understanding of the factors influencing health outcomes; for example, why do some patients tend to come back to the emergency department within 30 days of discharge from the hospital? But part of the opportunity is also in developing more personalized views of improvements: what would best help this particular patient avoid complications that would result in a new hospital visit? 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. Many of these capabilities represent daunting challenges, but they are far from "pie in the sky". Health industry conferences and publications are regularly featuring early examples of these changes in motion--detecting diseases by analyzing free text data, preventing hospital readmissions by modeling patient risks, uncovering unnecessary variations in cost-sensitive care processes, identifying patient subgroups that respond differently to treatments, developing "precision medicine" approaches to diseases, and many more. For technology-savvy leaders­CIOs, CTOs, and increasingly Chief Analytics Officers­there has never been a more exciting time to work in healthcare. HTJason Burke
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