| | August 20189UTLOOK Healthcare Tech clinical information models for diseases, and overall usability challenges with the current EHRs. For example, patients with CHF or COPD do not have consistent information documented in the EHR, despite the many clinical guidelines for care. EHR companies have not adopted a standardized template with common data elements, which would help address the documentation burden on physicians. Once the data is cleaned, aggregated, and linked to individual patients for longitudinal analyses, it must be organized or enriched to allow for development of risk-adjusted quality outcomes across facilities and allow for evaluation of cost analysis or payment models. Without such enrichment, we cannot perform an apples-to-apples data comparison to accurately attribute costs and outcomes. Measuring and incentivizing payment for performance or outcome requires that physicians who take care of the sickest patients are not penalized when compared to those who take care of healthier patients. In addition, patients must be grouped to allow segment management One commonly used regression model produces a clear picture of the individuals who are utilizing care at a higher rate, but it does not allow for patient groupings that clinical teams can use to manage segments of a population and track improvement over time.Big Data Needs a Framework for Transformation into Smart DataTransforming Big Data into actionable insights requires a philosophy and framework for value-based care. Without a clear understanding of what we want to learn and how best to measure our data, Big Data's potential will not be realized. To understand the interdependency between length of stay and complications or avoidable events, for example, the data must be linked and enriched to allow correlation analysis of root causes for potential action. Figure 1 proposes one such framework for consideration.What does all this mean for integrated healthcare systems? Cost reduction is crucial for all CEOs, and this means identifying variation or waste in the system, such as potentially preventable readmission or complications that could lower costs and increase quality and outcomes. It requires a consistent set of outcome measures within a larger analytic framework to drive the transformational changes needed to achieve these goals. This means first and foremost that HCOs need the Big Data along with data enrichment (risk stratification, benchmark, attribution, and more) down to the individual service line and physician level. Adequate risk adjustment ensures an accurate determination of the relative rate of negative outcomes and establishes fair rewards and penalties. HCOs must either acquire the skill sets to tackle the process of data normalization and enrichment, or outsource the work, without underestimating the level of complexity and the resources needed.In conclusion, healthcare organizations should consider the following goals in capitalizing on Big Data to meet the growing demands of value-based care:· Healthcare organizations must continue to focus, manage, and lower costs while optimizing revenue through:1) Consolidation/partnership with payers or other providers2) Focus on waste reduction and variation· Health IT companies should support HCOs with Smart Data, not just Big Data, and focus not only on aggregating, cleaning, and normalizing data, but also on categorization and enrichment of the data with risk stratification and benchmarking for improved performance management.· Payers and the Government should focus on measurable outcomes tied to significant cost savings with an appropriate and transparent incentive program to make healthcare more affordable and sustainable.· Apply Smart Data built on an analytic framework based on a philosophical foundation of quality outcomes. HTFocus on outcomes that are amenable to quality improvement efforts, i.e. outcomes that can be empirically derived from performance levels achieved by top-performers and for which a quality failure would result in a higher payment.Employ financial incentives (penalties and rewards) that are substantial enough to induce behavior change in proportion to the financial impact of the potentially avoidable event.Determine the relative outcome performance based on risk-adjusted comparisons that account for patient severity of illness.Establish a uniform and coordinated program that is transparent, clinically precise, and comprehensive.Essential Design Principles for a Successful Quality Payment ProgramThe proliferation of quality measures has made profiling and identifying potential quality problems increasingly complex
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