The Whole is Greater Than the Sum of its Parts

By April Gill, Vice President of Analytic Solutions, Welltok

When it comes to risk adjustment and quality, we have long known there is valuable information sitting in silos across the healthcare ecosystem – within EMRs, in various point solution databases such as care management or disease management service providers and even inside the often disconnected departments of America’s health plans. The ever-present challenge is to bring it all together in a meaningful way. And now, with the continual shifts in the political and regulatory environment including the move from fee-for-service to value-based care and incentivized or bundled payment models, it is more important than ever to solve for this and invest in a comprehensive and integrated approach.

Let’s explore how 5 key disciplines – when strategically merged together – can help your organization learn and improve over time and, ultimately, drive enhanced long-term success. 

Make Your Data Actionable

As approaches and technology evolve, the thinking around data must as well. Specifically, healthcare organizations need to embrace data as a way to create a 360-degree, actionable view of members. This can be accomplished by gathering and normalizing disparate data and storing it in a longitudinal database – a single source that aggregates transactional databases like claims, clinical workflow systems such as EMRs and member interaction data from CRM systems as well as external data such as consumer behavior and preferences. The resultant view exceeds current transactional views and takes into consideration clinical notes, call center notes, social determinants of health and other factors to enables healthcare organizations to provide optimal care and support members when they need it most.

Derive Insights Through Machine Learning

Data alone does not provide insight. In order to identify areas of opportunity to improve quality of care, coding and documentation accuracy, and to prevent RADV Risk, organizations should consider deploying machine learning technologies. Machine learning supports the analysis of all available information to determine the causative and correlative variables associated with clinical risk. The process can be fast, systematic and provides an opportunity to gather new insights from all structured and unstructured data sources in an unbiased fashion.

Focus on Desired Outcomes

Without thoughtful execution, no initiative can survive. Start by focusing on the desired outcomes and identify output requirements and the intended method of execution. Once these are set, you can begin feeding data into the insights engine. This process can be complex. It involves cross-functional collaboration in the areas of provider network strategy, vendor execution management, change management and education, but it is worth the effort to create alignment. Once your plan is in place and necessary training is complete, moving the insights into production can occur and realizing clinical and business value is almost immediate.

Leverage Resources to Enhance Value

There are a limited number of interactions a member will have with his or her provider, health plan and other service providers over the course of a year. By intentionally streamlining these interactions, health care organizations create greater efficiency, improve timeliness and quality of care and deliver a better, more connected member experience. While the consolidation of services into a single home or provider office visit will require a heavier lift initially, in the long run, it creates far greater value to both the member and the health plan by improving gap in care closure rates and enhancing coordination of care management and other services.

Institute a Continuous Improvement Process

Healthcare organizations need to have the insights to identify course corrections in real-time and the agility to make the necessary shifts. An effective continuous improvement process enables them to achieve this by constantly evaluating outcomes and feedback, optimizing how the system functions and adjusting for changes in membership, market forces, regulation and other factors. The ongoing integration of new data and outcomes supported by evolving technology will also make your system smarter over time and ensure it continues to improve.

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About the Author

April Gill

April is an accomplished leader in growth strategy with 20 years of experience in consultative sales and business development, account management and product strategy. She leverages advanced analytics to solve critical business problems and drive financial performance for healthcare stakeholders.

 


Tags: Risk Adjustment, data management, HHS, strategy, machine learning

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