Seven Benefits of Data Mining for Health Plan Design

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August 14th, 2012

Data mining for plan designImproving the health of plan members and reducing costs can often go hand in hand for many insurers and self-funded employers. One tactic that accomplishes both of these goals is data mining. Data mining transforms data patterns into valuable information through the use of analytical software. In addition to using traditional approaches to reduce health care plan costs, plan sponsors can use evidence-based data mining as an effective tool for cost management and improving care as indicated in Cost Management: Strategies for Self-Funded Health Plans by focusing on high-cost conditions and atypical utilization patterns.

The analyzed raw data is typically made up of plan enrollment, COBRA, medical claims, mental health claims, prescription drug claims, as well as performance and workers' compensation claims.  Medical data management companies collect this information from health insurers, third-party administrators, health maintenance organizations and pharmacy benefit managers and organize it into highly useful clinical utilization and financial data sets. Health care analysts use the data information to help decision-makers and plan sponsors take effective corrective actions, uncover problems and focus on areas for improvement.

Potential Benefits of Data Mining for Health Plan Design

  1. Determine what diseases and conditions are driving trends.
    By reviewing claims data to identify what health issues are most prevalent, the health plan sponsor can tailor their health care plan to promote better wellness and reduce costs.
  2. Target intervention to high-risk segments of the population and those who need the most care.
    Targeting which employees can benefit from advanced care management for more severe diseases or conditions and intervening to reduce the rate of hospital readmission can lead to lower costs.
  3. Identify gaps in medical treatment and direct employees to the proper care.
    Discover and reduce gaps in medical treatment by comparing employee data to Healthcare Effectiveness Data and Information Set (HEDIS) benchmarks.
  4. Identify the best, cost-effective network providers and guide employees to use them.
    Health Plan Sponsors can promote the use of providers that have been discovered as high-performance and high-quality through data mining.
  5. Improve health habits through wellness, health promotion, education and care-management programs that increase awareness and engage employees in their own care.
    Data mining can show if a health plan is effective in promoting wellness and prevention. An incentive-based plan will help manage costs by encouraging wellness.
  6. Measure the performance of vendors and administrators and hold them accountable for quality, cost-effective treatment by comparing their results to national  benchmarks.
    Plan sponsors can implement performance guarantees for financial, clinical, operational and utilization components to their health plans.
  7. Determine what level of cost-sharing improves employee health and cuts cost.
    Select the right level of cost-sharing that will encourage appropriate utilization to improve employee health and reduce long-term costs.

Data mining of past claims data can give insight on what changes to make in health plan designs to reduce costs and provide better utilization options to its members. The type of information that data mining provides can illustrate the way their plan members are managing their care, compare that against healthcare guidelines, and in turn position themselves to reduce plan costs through promoting better care of health.