April 20198THE NEW FRONTIER FOR HEALTHCARE ANALYTICSHealthcare analytics is in its infancy. Most organizations lack a cohesive data strategy and even fewer have effectively integrated analytics into the care delivery process, with most using analytics in one-off projects. For the majority of providers, the goal of analytics is a predictive algorithm based on fast moving clinical data that improves care delivery at the bedside. However, there is a new frontier past applying predictive algorithms on data captured within the four walls of healthcare.To even start on this journey, it is vital to have established the foundation for effective data and analytics. Specifically, two core capabilities must be first addressed: Data Warehouse and Data Governance.The silos of data must be broken down. Healthcare data often reside in multiple distinct silos, whether functional or historical. There are the functional silos where operational data such as supply chain information is distinct from patient data within the electronic medical records that is distinct from financial data and separated from patient satisfaction and quality data. There are historic silos as a result of mergers or acquisitions where similar data, such as clinical data within an electronic medical record, sit within different databases. A well-defined linking of the data is vital to the consistency and reproducibility of analytics. Without a cohesive data warehouse strategy, whether through systems such as Hadoop, HANA, or virtualization, there will never be a single source of truth on which to base analytics.If the single source of truth from a cohesive data warehouse strategy is the most important building block of analytics, a common language for analytics is a close second. Without a common language and strict data governance, analytics becomes a jumbled set of terms and calculations, rarely consistent or actionable. If the average length of stay within a hospital is calculated based on claims data (one day is one day regardless of discharge in the morning or afternoon), an optimization project on improving hospital capacity by targeting early in the day discharge may have no effect on the measured length of stay.Moreover, language or governance extends into benchmarking. Healthcare is still too complex and fragmented to have a common measure. Payers and provider often differ in the approach to measure healthcare value with some relying on operational efficiency, some on cost metrics, and all overlaid on clinical quality. However, quality is at times, even more complex. Is diabetes well treated if there are no inpatient hospitalization or when the Hemoglobin A1C less than 7? Does random blood glucose matter or does BMI matter more? Ultimately, if it is a combination, how are each variable weighed? Organizations need to define the metrics or goalposts for success otherwise analytics can never be used to focus the limited resources of the organization on improvement.Only once the foundation of a standard data warehouse is formed and a common language, including data governance and benchmarking, is established, can the hard work of analytics come into play.At this point, most organizations start down the road of advanced analytics, generally in the world of predictive algorithms. For instance, Benson HsuBenson Hsu, VP, Data and Analytics, Sanford HealthByIn My Opinion
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