Big Data can drive improvements in care processes, delivery and management. This is particularly important in today’s growing environment of risk sharing, fixed reimbursement and penalties incurred for not achieving expected quality and outcome benchmarks. We are in a time of value-based business models and value-based care. . . organizations need analytics to manage the higher level of risk and reward. Data that is transformed into meaningful information is essential, but the challenge for organizations is how to harness this data for purposeful use. Data analytics is necessary from the individual level to population health, with the ability to include not only acute care, but settings across the care continuum. The post-acute care settings are of particular focus as a way to manage care in a less costly but quality way, the latter becoming an important consideration in risk sharing and capitated models. The capability to blend and align clinical and financial data into analytics is particularly crucial with the risk-reward models that are in play. What may appear on the surface as reasons for increased costs and utilization of services may not be the true root cause.
As an example, Geisinger Health Plan had long-used clinical and financial data for population health management to identify opportunities for cost and quality improvements. Geisinger has learned that underlying factors such as “behavioral health” issues may be significant drivers in utilization of health care services. This is a proof point that data must be analyzed with an open and creative lens, as never before, to better manage care.
So what types of analytics are necessary? While retrospective reporting and comparative analytics are important, explorative analytics to investigate for root cause analysis can give insight to improve processes and care management. Of great value and of growing importance are analytics that are more near-real time, providing guided analytics translating insight into action. Guided analytics identifies a problem or risk situation such as a predictive risk score that alerts clinicians for awareness and decision making to take action Even more advanced are predictive analytics that allows for manipulation of variables and assumptions based on algorithms and “if and then” situations to forecast a future event and impact on outcomes. Last, prescriptive analytics based upon evidence can guide specific actions in managing individuals and population health. Evidence-based protocols continue to demonstrate the value to achieve quality and cost management goals, and with the right analytics, standards-based protocols can be customized to individuals to effectively achieve desired quality and performance goals.
It’s an exciting time in the world of Big Data. The possibilities for data analytics in healthcare to drive insights and take action are burgeoning. Unlike other industries that have been using analytics in a variety of ways with proven value, healthcare is relatively young in optimizing analytics for clinical and business intelligence to drive performance and forecast new business models. As healthcare systems take on the challenge of managing higher risk, Caradigm solutions are well positioned to support the emerging frontier of Big Data and analytics for today and the future.