Living the Promise of Big Data Part II

In the first of this series, we discussed the need for payers to more effectively understand the needs of specific groups of members and ultimately improve standards of care. 

A comprehensive analytics approach presents a framework for helping health plans to clinically evaluate members, collaborate with providers more strategically, and apply models to help gauge the impact of efforts to improve clinical effectiveness.

In a comprehensive framework, there are three steps in developing an analytics’ approach: (1) accessing data sources; (2) aggregating and stratifying the data; and (3) modeling the data.  We covered data sources in Part 1 of this series.  Once the data sources are identified, health plans must focus on two additional phases of analysis and decision making in order to create actionable, member-specific management plans. 

Step 2: Aggregate and Stratify the Data

Clinical data and market insights are aggregated and stratified to create actionable care management plans for each member. These plans encompass prospective and retrospective services that help ensure that members are receiving the appropriate care; providers have appropriate member information at the point of care; and health plans receive the appropriate information to document member conditions. These management plans are characterized by the following:

  • Statistically supported – Using the latest techniques in applied statistics and probability assists in the  identification of the program(s) most likely to close care gaps and confirm suspected conditions
  • Responsive – The flexibility built into the model allows members to progress through the spectrum of care as their needs evolve. The member transitions between programs as indicated in the data
  • Comprehensive reporting – Detailed analytics drives the ongoing training and assessment of the model. As results come in, that data is used in suspect identification and targeting to strengthen future results

Step 3: Model the Data

Front-end, proactive identification of Medicare Advantage members at-risk of developing chronic diseases requires rules-driven logic based on predictive models built from the comprehensive clinical and administrative data sets described above. Stratifying members according to clinical risk profiles and disease types leverages predictive signs and risk factors that indicate likely conditions not yet appearing on claims.  

Critical advances in risk management analytics highlight the value in a significantly more robust statistical logic to plan and manage best-possible care. Aggregating and stratifying clinical data and market insights creates actionable, member-specific management plans. The resulting care planning outcomes reflect prospective and retrospective risk adjustment programs that help ensure:

  • Members receive appropriate care.
  • Providers have appropriate member information at the point of care.
  • Health plans receive appropriate information to document member conditions.

The potential in risk adjustment, traditionally one of the least understood components of health care, has earned the heightened visibility attached to ACA-driven reform to control costs by reducing adverse member selection. Ideally, the new awareness will also renew and intensify payer engagement in applying optimized risk adjustment analytics to close care quality and utilization gaps. This is particularly evident for the chronic-care, high-cost Medicare reimbursement environment, where decreased payment rates make it essential to accurately represent health plan risk scores.

Don James, Director of Product Management, Risk Adjustment Solutions at Optum

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