My presentation at the International Health Economics Associations 7th World Congress

1. Risk Adjusted Utilization in Provider Contracting
Rong Yi, PhD
Vice President, Consulting
President
iHEA Beijing Conference, July 13, 2009

2. Overview
Challenges in cost containment in the US
g
healthcare industry
Risk adjustment in provider contracting
Development of risk adjusted utilization models
Application and industry feedback
pp y
©2009 Verisk Health, Inc. 2

3. Challenges in Cost Containment
Large health insurance company in New England
Current system rewards volume and intensity
Providers lack incentives to focus on quality and
efficiency
y
Payers face escalating costs and in turn pass
onto patients through higher premiums
Need to base payment on outcome to change the
incentives: Risk Adjusted
Quality – standard of care payments to
Efficiency – manage utilization change
behavior

4. Pay-for-performance contract
Global payment
• Adjusted by patients’ health status (
’ (all-encounter DCG
CG
risk score)
• Adjusted by general inflation instead of medical inflation
Performance bonus up if
• Quality g
y goals met (
(standard q
quality measures)
y )
• Efficiency goals met
– risk adjusted cost and utilization targets
Multiyear contract to ensure systematic change
and long-term goals

5. DCG/HCC Risk Adjustment System

6. DCG Risk Adjustment System
ICD-9 or ICD-10 Diagnosis Codes
DxGroups (DxGs)
(784 groups)
Impose Hierarchy to
Condition Categories (CCs)
reduce gaming and
(184 groups)
code creep
Aggregated Condition
Categories (ACC )
C t i (ACCs)
(30 groups)
Classify
Classif all diagnosis codes into clinicall meaningf l and
clinically meaningful
homogenous groups for econometric/statistical modeling.

7. Diagnosis Grouping Example
ICD-9 410.01: Initial Anterolateral Acute MI
DxGroup 81.01: acute myocardial infarction, initial
episode of care
CC 81: Acute Myocardial Infarction
ACC 16: Heart

8. Hierarchical Condition Category
(HCC) Example
HCC007 Metastatic Cancer and Acute Leukemia
M t t ti C dA t L k i
HCC008 Lung, Upper Digestive Tract, and Other Severe Cancers
HCC009 Lymphatic, Head and Neck, Brain, and Other Major Cancers
HCC010 Breast, Prostate, Colorectal and Other Cancers and Tumors
HCC011 Other Respiratory and Heart Neoplasm
HCC012 Other Digestive and Urinary Neoplasm
HCC013 Other Neoplasm
HCC014 Benign Neoplasm of Skin, Breast, Eye

9. Example: John Smith has Multiple
Conditions
Substance
Abuse Diabetes Heart
HCC015
HCC020
Diabetes with
Type I Diabetes
Renal
Mellitus
Manifestation
HCC016
Diabetes with
Neurologic or
Peripheral
+ +
Circulatory
Manifestation
HCC017
Diabetes with
Acute
Complications
HCC018
Diabetes with
Ophthalmologic
Manifestation
HCC019
Diabetes with No
or Unspecified
Complications

10. Risk Adjustment Software
j
Demog Diags, Proc Lab, Survey…
Clinical Detail
Database
Data Quality Checks & Tables
Clinical Mapping
& Predictions
Business Solutions
Report Set

11. Predict Future Total Healthcare
Expenditure Using Medical Diagnosis
John Smith
Age: 45
Sex: M
Hypertension
essential hypertension
Type II Diabetes Mellitus
type II diabetes w/ renal manifestation
Congestive Heart Failure
hypertension heart disease, w/ heart failure
disease
Drug/Alcohol Dependence
6.35x sicker than alcohol dependence
average
Relative Risk Score: 6 3
i i S 6.35

12. Understand Risk Distribution
for Decision Support
pp

13. Calibration of Pay for Performance Models
Pay-for-Performance

14. Risk Adjusted Utilization Metrics
Three concurrent risk adjustment models:
• Total counts of medical and surgical admissions (f
f (focus of
f
this presentation!)
– Excluding mental health and pregnancy related DRGs
• Use of advanced imaging test (CT MRI etc)
(CT, MRI,
– Weighted sum of tests
• Patient’s use of expensive drugs
– Specific NDC codes and $
Model development sample (N=860,565)
• Age 0-64
• Commercial health insurance with pharmacy benefit
insurance,
• At least 10 months eligibility
• U.S. Eastern seaboard
©2009 Verisk Health, Inc. 14

15. IP Count Model Development Sample
- Summary statistics -
Total IP Count Freq % of Total Total n = 860,565
0 ,
833,952 96.91
1 21,440 2.49
Average age = 34.7
2 3,509 0.41
3 933 0.11
4 394 0.05 Average relative risk
5 150 0.02 score = 1.21 (1.21
6 74 00
0.01 times sicker than an
7 45 0.01 average individual
8 29 0 with private
9 9 0 insurance in the US)
10+ 30 0
Long tail – standard distribution assumptions may not
fit well
©2009 Verisk Health, Inc. 15

16. Model Calibration
Different model specifications explored:
• Zero-Inflated Poisson
– Age/sex + HCCs + interactions
• Negative binomial
– Age/sex + HCCs + interactions
• Linear piece-wise splines
– Step 1 OLS regression to predict IP count (R Sq
(R-Sq
44.5%)
– Step 2 create knots by 8 age/sex groups and OLS-
p
predicted
– Step 3 regression using spline variables created in
Step 2, at 50, 75, 90 and 95th percentiles
©2009 Verisk Health, Inc. 16

17. Results - Male
©2009 Verisk Health, Inc. 17

18. Results - Female
©2009 Verisk Health, Inc. 18

19. Discussions
- estimation method -
Total counts of med/surg admissions have a
g
much longer tail than standard distributions
such as Poisson or negative binomial
Linear piece-wise splines fit better, although
further modifications need to be made, such as
negative prediction
ti di ti
©2009 Verisk Health, Inc. 19

20. Discussions
- interpretation and application -
Concurrent model framework establish a
diagnosis-based risk-adjusted experience of
total inpatient counts.
Disease burden is measured using health
insurance claims sensitive to coding quality
and specificity
d ifi it
• May cause unnecessary disparities across different
provider groups who have different levels of coding
quality
©2009 Verisk Health, Inc. 20

21. Industry Feedback
Shifting from the event -- the drug, the
g g,
operation, the hospital bed -- to the outcome for
the patient
New form of pay-for-performance, moving in
the right direction in trying to reduce escalating
growth i h lth
th in healthcare spending
di
©2009 Verisk Health, Inc. 21