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World Health Congress 2009 Europe Market Insight
- 1. Predictive Modeling andRisk Adjustment Applications in the U.S. and I t A li ti i th U S d International M k t ti l Markets Dr. Rong Yi, Vice President, Consulting Dr. Thomas Zahn, General Manager, DxCG Gesundheitsanalytik GmbH World Health Congress, European Conference Brussels, May, 2009
- 2. Overview Risk Adjustment and predictive models in the j p big picture How are models developed Applications in healthcare financing, management, delivery and reforms ©2009 Verisk Health, Inc. 2
- 3. Setting up theStage - Healthcare Dichotomy Healthcare Financing Delivery Feasibility Cost, Quality, andd and Efficacy sustainability
- 4. Healthcare Financing Challenges For all decision-makers from governments down to individual consumers: How much? How many years? Whom to cover? What services to offer? … Need to understand and predict acc ratel nderstand accurately: • Risk selection • Aging and disease burden g g • Benefit design and outcomes
- 5. Healthcare Delivery Challenges Resource planning Health d H lth and wellness management ll t • Population • g Disease and case management • Wellness and productivity • Patient education and outreach • … Need to: • Stratify the population and prioritize management efforts • Measure outcomes (cost, quality and efficacy) • …
- 6. Risk adjustment andpredictive modeling as decision support tools pp
- 7. How are ModelsBuilt? Claims Data + Clinical Classification + Econometric/Statistical Modeling
- 8. Data used asinput Enrollment information: • age, sex, eligible months, basis of eligibility (e.g., disabled) Claims information • Diagnosis • Procedures P d • Pharmacy • Long term care • Spending – timing, categories, patterns p g g, g ,p • Utilization – hospital, ED, specialty Not everything is used for prediction! • D Depends on client needs, available d t model’s i t d d d li t d il bl data, d l’ intended use, and the tradeoff between easy of use and added predictive accuracy
- 9. Risk Prediction usingMedical Diagnosis only John Smith Age: 45 Sex: M Hypertension essential hypertension Type II Diabetes Mellitus type II diabetes w/ renal manifestation Congestive Heart Failure g hypertension heart disease, w/ heart failure 6.35x sicker than Drug/Alcohol Dependence average alcohol dependence Relative Risk Score: 6.35
- 10. Clinical Classification Systems Example– DCG/HCC Diag Grouping Why Grouping? Distill massive amount of data to create useful analytic units y Most Common Classification Systems: DCG/HCC - Grouping of Diagnosis Codes RxGroups - Grouping of Drug Codes BETOS - Grouping of Procedure Codes Example: The DCG/HCC Classification System ICD-9 or ICD-10 Diagnosis Codes DxGroups (DxGs) (784 groups) Hierarchical C diti Hi hi l Condition Categories (HCCs) (184 groups)
- 11. What Can bePredicted? … almost anything as long as data supports y g g pp Individual level prediction. Can be g p up by p grouped p y age/gender, medical conditions, geography, benefit design, etc. Predicts within a year, across years and for multiple y , y p years, Examples of predicted outcomes: • Total healthcare cost or a subset, e.g., drug cost g g • Distribution of risk, e.g., probability of cost more than $100,000 • Healthcare utilization, e.g., future hospitalization, avoidable ER visits, total length of stay use of advanced imaging tests visits stay, • Disease progression and spending persistence • Duration of injury
- 12. Risk adjustment andpredictive modeling as decision support tools pp
- 13. Bring All Together Operations Marketing Health Plans Revenue Work Comp Government Optimization Insurers Agencies Underwriting Employer Employers Provider Medical Reporting Groups Management Financial Pharmacy Audit Pharmacy Consultants Analysis 13 © Verisk HealthCare 2008 10/16/2008
- 14. Applications of RiskAdjustment and Predictive Modeling P di ti M d li
- 15. Healthcare Reform Example MassachusettsUniversal Healthcare 2006 Massachusetts landmark legislation g Procurement of healthcare for the previously uninsured • Capitation based on age/sex/geography/benefit design p g g g p y g • Structured bids to incentivize lower bidders Challenge: risk selection. Healthier members cluster in lower-priced p p plans Need to establish a fairer and more sustainable procurement process Solution: • Fairer – account for disease burden • Sustainable – prospective risk adjustment to ensure financial stability
- 16. Risk-based Capitation Illustration Systemwide Monthly Payment/Capita $420 Health Plan A Health Plan B Health Plan C Relative Risk Score 1.16 Relative Risk Score 0.61 Relative Risk Score 1.52 Budget: $420 x 1.16 Budget $420 x 0.61 Budget: $420 x 1.52 = $488 = $256 = $640 Implementation: • Budget Neutral • Further Adjustments on benefit design, geographic factor, new/partial enrollees / ti l ll • Quarterly adjustments to smooth cash flow
- 17. For Health Plans Care and disease management identification g and stratification Understand changes in risk over time – actuarial and underwriting • Aging and increased medical comorbidities • Disease progression Reinsurance and stop loss arrangements Compare providers fairly, adjusting for differences in health status diff i h lth t t ©2005 by DxCG®, Inc.
- 18. For Self-Insured Employers Pay health plans based on resource needs y p rather than risk selection Compare health plans fairly (accounting for health status) Negotiate rate reductions from inefficient plans ©2005 by DxCG®, Inc.
- 19. For Providers Integratewith medical informatics: • Identify at-risk patients for the right care (high likelihood of hospitalization, avoidable ER use, etc.) • Identify care gaps and improve quality • Evidence-Based Medicine Compare quality and efficiency to their peers • Referral and prescription patterns • Use of advanced imaging tests g g Pay-for-performance Patient-centered Medical Home ©2005 by DxCG®, Inc.