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Risk Adjustment Models

Risk Adjustment Models . Jung Ki Kim, Ph.D. Judy Y. Yip, Ph.D. Center for Long Term Care Integration. Risk Adjustment Models. Examines pre-existing patient factors that result in differences in the outcomes of analyses being conducted Assessment and accountability Risk of What

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Risk Adjustment Models

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  1. Risk Adjustment Models Jung Ki Kim, Ph.D. Judy Y. Yip, Ph.D. Center for Long Term Care Integration

  2. Risk Adjustment Models Examines pre-existing patient factors that result in differences in the outcomes of analyses being conducted • Assessment and accountability • Risk of What • Health outcomes (mortality, morbidity, functioning) • Costs • Health care utilization

  3. Dimensions of Risk • Age, sex, race/ethnicity • Acute clinical stability • Principal diagnosis • Severity of principal diagnosis • Extent and severity of co-morbidity • Physical functional status • Psychological, cognitive, and psychosocial function • Cultural and socioeconomic attributes and behaviors • Health status and quality of life • Patient attitudes and preferences for outcomes

  4. Characteristics of a `GOOD` model • A suitable measures or model has to possess: • Predictive accuracy • Reliability • Resistance to gaming • Administration feasibility

  5. Types of Risk Adjusters • Demographic • Claims-Diagnosis • Functional Status • OR any combination of the above

  6. Risk Adjustment Models Demographic Model • AAPCC Diagnosis-based Model DCG • PIPDCG • ADDCG • HCC ACG • ADG-HosDom • ADG-MDC DPS • CDPS

  7. Medicare`s Adjusted Average per Capita Cost (AAPCC) • Age, sex, welfare status (Medicaid), and institutional status • Payment is set at 95% of Medicare expenditures predicted by the AAPCC • Adjusts the national average per capita cost of all Medicare beneficiaries to reflect the average cost in the county where the enrollees reside

  8. Why Need to Improve the Risk Adjustment Methodology in Capitated Payment Systems? Limitations of AAPCC • Inaccuracy • Unfairness Accurate risk adjustment would • benefit plans by more fairly paying them if they were high risk persons • benefit HCFA by reducing the possibility of overpayment to plans for low-risk beneficiaries who on average use fewer services

  9. Diagnostic Cost Group (DCG) Models • Developed by Boston University • Comprehensive clinical classification system; Groups individuals into clinically homogeneous groups • Used by HCFA to calculate health-based (risk-adjusted) payments for Medicare+Choice plans.

  10. DCG Models (Cont`d) • PIPDCG(Principal inpatient DCG): Classifies people based on their single highest cost principal inpatient diagnosis • ADDCG(All-diagnoses DCG): Adds secondary inpatient, hospital outpatient, and physician diagnoses to the principal inpatient diagnosis, and classifies people based on their single highest predicted cost diagnosis • HCC (Hierarchical Coexisting Conditions): Accounts for multiple medical conditions; Sums the incremental predicted cost for each condition to arrive at the total predicted cost.

  11. ACG (Adjusted Clinical Groups) and ADG (Ambulatory Diagnostic Groups) Models Developed by Johns Hopkins University • ACG (Ambulatory Care Group) • PACS (Payment Amount for Capitated System) • ADG-MCD (Ambulatory Diagnostic Groups-Major Diagnostic Category) • ADG-HosDom (Ambulatory Diagnostic Groups-Hospital Dominant)

  12. Disability Payment System (DPS) • Developed by Kronick and colleages • For Medicaid recipients with disability • The major chronic diagnostic categories are divided into 43 subcategories - severity subcategories (e.g. high-, medium- and low-cost cardiovascular). • CDPS (Chronic Illness and Disability Payment System) models

  13. Current Use of Diagnostic-based Adjusters in Medicaid ( in 2000) State Classification System Maryland ACGs Colorado DPS Oregon DPS Minnesota ACGs Delaware CDPS Michigan CDPS

  14. Functional Status Risk Adjustment • Survey-based adjusters • Include health status risk adjusters such as individuals’ health perception, functional status/disability (ADLs and IADLs), self-reported clinical diagnoses, chronic disease risk

  15. Good Models • Ability to predict costs for individuals • Ability to predict costs for groups

  16. Applications and Future Tasks • Prospective vs. concurrent models • Scope of diagnoses included in model • How to categorize diagnoses? • What kinds of diagnoses are included? • How many diagnoses are included? • Test different models with our Medicare and Medi-Cal population • Test different sub-groups of our data population • Develop new models with a new diagnostic system

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