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Data Mining: Results-Driven Decision Making

Data Mining: Results-Driven Decision Making. Presented by: Michael Faughnan and Eric Gary Aon Consulting July 2007. Agenda. Introduction: Why Data Mining Matters Key Components of Data Mining: Claim Spend Health Risk Assessment Big Spenders Establish Your Scorecard.

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Data Mining: Results-Driven Decision Making

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  1. Data Mining: Results-Driven Decision Making Presented by: Michael Faughnan and Eric Gary Aon Consulting July 2007

  2. Agenda • Introduction: Why Data Mining Matters • Key Components of Data Mining: • Claim Spend • Health Risk Assessment • Big Spenders • Establish Your Scorecard

  3. Human Resources Budget • Administration • Recruiting • Training • Payroll • Employee Benefits • Healthcare spending is the most unpredictable • Healthcare inflation is higher • Healthcare is outsourced

  4. “You can't manage what you don't measure.” • If you're only looking at the score, you may be missing the game • Are participants good consumers? • What are the prevalent risk factors? • How healthy is the populations? • How are my vendors doing?

  5. Measuring = Data Mining • Claims • Reality • Health Risk Assessment • Statement • Predictive Modeling • Foreshadowing

  6. Benchmarks • Benchmarks • Internal • Carrier/Vendor • General population (US, private plan, under age 65) • Benchmarks serve a purpose, but . . . • Demographics

  7. Demographics • Know your group! • What risk classifications drive health cost? • Age • Gender • Family content • Geography • Industry and type of employment • Current health status / risk profile

  8. Claim Data • Are we good consumers? • Rx • Frequency • Generic substitution rate • Retail vs mail utilization • Lifestyle drug utilization • Drug compliance • Specialist vs PCP utilization • Cat Scan/MRI utilization • ER vs urgent care utilization • In-network vs out-of-network utilization • Hospital admission / average length of stay

  9. It’s all in the details • Are internists the most appropriate PCP? • Why is specialist utilization so high? • Appropriate data analytics is the roadmap to effective strategy and tactics

  10. Plan Design Changes / Benchmarking • Do you know what services are being utilized? • Has utilization changed year over year? Why? Was it impacted by plan changes? Or something else? Who makes up Other? Did that change in ER co-pay really make a difference? • Can you compare utilization year over year? • Do you know what it is telling you?

  11. Utilization Scorecard

  12. Claim Data • Where are we spending? • Top facilities • Top drugs • Top providers • Top participants

  13. Top Facilities Deserves Investigation

  14. Large Claim Distribution

  15. Health Risk Assessment (HRA) • Collects : • Demographics • Lifestyle • Personal medical history • Family medical history • Physiological data • Employee attitude

  16. Health Risk Assessment (HRA) • Identify health risk factors that can be modified with changes in lifestyle • Reports to individuals their health risk • Aggregate report to management

  17. Health Risk Assessment (HRA) • Health behaviors account for 40-50% morbidity and mortality • Smokers • Overweight • Aging • Physically inactive • Family history

  18. Costs Increase With Risk & Age $12,000 $10,000 $10,095 5+ Risks $9,221 $8,000 $6,664 3-4 Risks $6,000 $7,268 $5,445 $4,130 $4,000 $3,432 $3,601 0-2 Risks $2,741 $4,319 $2,025 $2,000 $3,366 $1,920 $1,515 $1,247 $0 <35 35-44 45-54 55-64 65+ Source: StayWell data analyzed by U of Michigan (N = 43,687) – HERO Study Health Risk Assessment (HRA)

  19. HRA: Key Summary Data

  20. HRA: Key Summary Data

  21. HRA: Interpreting the Results • Analyzing HRA results will: • Prioritize investments to a Wellness Program • Guide outreach efforts

  22. Burden of Healthcare Costs Health Promotion Care Management Case/Disease Management Getting Better Living w/Illness Staying Healthy

  23. Find / Manage Big Spenders • People with chronic conditions account for approximately*: • 75% of all health care costs • 88% of all prescriptions filled • 72% of all physician visits • 76% of all inpatient stays *Partnership for Solutions, a research group at Johns Hopkins University

  24. Evaluate Data • Utilization / Prior Use • Dollar thresholds • Type, amount, timing of service • ER, hospitalization procedure code • Rx • Diagnosis • HRA • Advanced Data Augmentation • Predictive modeling software

  25. Monitor DM Effectiveness Example: Are people being missed? Should we intensify our focus?

  26. Case Management Effectiveness

  27. Considerations • Who will do the work? • In-house • Health Plan • Broker/Consultant • Third Party/Other Vendors • Info/Tools You Will Need • Data • Software • Format • Frequency of Reports • Depth

  28. The bottom line… You need to develop a scorecard!

  29. Act on the Information • Prioritize Management • Identify Risk • Target Wellness Program • Monitor Large Case / DM Program

  30. Questions

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