Welcome to the data world. Many secrets are hidden in big data, and now, with the computing power to unearth them, analytics promises to deliver transformative power wherever it is put to work. Still, the technology is a relative newcomer in the healthcare world. Brett Furst, CEO of Arbormetrix, says there is nothing to fear – and that analysis of clinical data has much to offer the medical world. Here, he shares his top five requirements to succeed with, or at least get excited about, the power of clinical analytics.

1. Know the difference between solutions. Analytics solutions vary widely in size and shape. Furst says it is important to know what the different kinds are, and how to apply each one to specific problems, whether they have to do with population health and disease management, episodic delivery or post-acute care. Population health and disease management focuses on "improving the general health of a population and keeping them out of a hospital," according to Furst. Think screening a database to find people who might be at risk for a certain condition and reaching out to them. Episodic analytics "focuses on identifying variation in the delivery and associated outcomes of specialty and acute care." This kind of analytics is about looking back and finding ways to improve care in the future based on how it was provided previously, says Furst. Post-acute analytics centers around "utilization management so patients receive the appropriate level of care after hospitalization, with a focus on cutting down on wasted resources." Essentially, the three flavors Furst outlines could be seen as the analytical equivalents of before, during and after. 

2. What's in the data? Knowing which analytics can be applied to which problems opens the door to immense functionality. With the rise of ACOs and the paradigm shift of reimbursement for quality of care, healthcare providers are scrambling to approach the health of their populations proactively. Clinical analytics has a role in this shift, and Furst says harnessing its power means that organizations will be able to more intelligently identify, solve, and manage the challenges that they are beginning to face. Furst says being able to ask questions such as, "Where the spending is, how many readmits do we have every year, and how do outcomes stack up against variances of treatment?" have a massive "effect on clinical performance [that] goes to the actual outcome of the patient." By taking the data generated in a hospital and making sense of it with clinical analytics, Furst says there is a real ability to find and tackle performance issues. "When you combine good clinical data with good accounting data, you can pinpoint what types of conditions might make for readmits," he says.