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 Health outcomes (mortality, morbidity, functioning) Costs Health care utilization

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

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

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

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

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

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

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.

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.

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)

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

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

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

Good Models Ability to predict costs for individuals Ability to predict costs for groups

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