Risk-Adjustment Methodologies and Applications in the VA

1. Risk-Adjustment Methodologies and Applications in the VA Amy K. Rosen, Ph.D. Director, Risk Assessment and Patient Safety Center for Health Quality, Outcomes and Economic Research A VA HSR&D Center of Excellence (Bedford, MA) & Professor, Health Policy and Management Boston University School of Public Health

2. Purpose of Talk • Introduce concept of risk adjustment • Describe two well-known diagnosis-based risk-adjustment tools: Diagnostic Cost Groups (DCGs) and Adjusted Clinical Groups (ACGs) • Discuss applications in the VA • Development of psychiatric risk-adjustment measure for the VA

3. Why is Risk Adjustment Necessary? • Health status of population can vary significantly • Goal is to provide equitable compensation and make appropriate comparisons • Allocations based on efficiency and quality, not selection

4. Risk Adjustment The process by which the health status of a population is taken into account when evaluating patterns or outcomes of care or setting capitation rates

5. Applications of Risk-Adjustment Measures • Payment management (Prospective) • Provider profiling (Concurrent) • Disease/Case management (Prospective) • Quality and outcomes (Concurrent) • Resource allocation (Prospective)

6. Types of Risk-Adjustment Measures

7. Evaluation Criteria • Predictive validity • Subgroup fit • Administrative feasibility • Incentives for efficiency • Resistance to gaming

8. Diagnosis-Based Risk Adjustment • Increasing use of risk adjustment based on diagnosis codes from administrative data • Persisting concerns with reliability and validity of diagnosis codes • Outpatient data - not much known about reliability • Variability in coding practices across providers and facilities • Upcoding and diagnostic creep • Tentative coding • Risk-adjustment measures minimize some of these (e.g., excluding ill-defined codes)

9. What are diagnosis-based risk-adjustment measures? • Diagnosis-based measures use demographics/diagnostic information from claims/encounters to: • Classify patients into clinically homogeneous groups based on expected need for resource utilization • Create clinical profile • Identify clinical needs • Evaluate clinical management programs • Predict relative resource use • Predict expenditures • Same year as diagnosis (Concurrent Models) • Subsequent year to diagnoses (Prospective Models)

10. Data Requirements • Defined population of patients • Claims/encounter data available for all members of the population (12 months) • Unique patient identifiers (i.e., social security numbers) • Age and gender • ICD-9-CM diagnosis codes from face-to-face clinical encounters Optional: payer, DOD, sociodemographics

11. Family of Diagnostic Cost Group (DCG) Models • Type of population • Commercial, Medicare, Medicaid • Clinical data available • All-encounter, inpatient only, age/sex and pharmacy • Year of prediction • Concurrent or prospective • Recognizes cumulative effect of multiple conditions in predicting costs

12. DCG Model Overview Relative Health Status ICD-9-CM Codes Relative Risk Scores Resource Use Predictor Clinical Groups DCG Categories Clinical Profiles

13. DCG Clinical Classifications ICD-9-CM codes (n = 15,000+) DxGroups (n =781) Hierarchies imposed for predictions Relative risk score PIP-DCGClinical Classification Condition Categories (CCs) (n= 184) PIP=Principal Inpatient Based on Inpatient Dx only Single-condition model Aggregated Condition Categories (ACCs) (n= 30)

14. Clinical Vignette:59 year old woman AMI, COPD, renal insufficiency (Release 5.0) ICD-9-CM DxGroup CC 410.91 AMI of unspecified site, initial episode of care 72.01 AMI, initial episode of care 50 AMI 491.2 obstructive chronic bronchitis 96.01 emphysema/ chronic bronchitis 64 COPD 518.1 Interstitial emphysema 106.04 renal failure, unspecified 586 renal failure nos 78 Renal Failure 106.03 chronic renal failure 585 chronic renal failure

15. Hierarchical Condition Categories (HCCs) • 31 Hierarchies are imposed on the CCs to produce HCCs. The clinical hierarchies: • Identify the most costly manifestation of each distinct disease • Decrease the model’s sensitivity to coding idiosyncrasies • Examples: Diabetes, Cancer, Heart, Mental Health

16. Cancer Hierarchy (Release 5.0) Metastatic Cancer High Cost Cancer Moderate Cost Cancer Low Cost Cancer Carcinoma in Situ Uncertain Neoplasm Skin Cancer Except Melanoma Benign Neoplasm

17. Current and Prospective Predictions

18. How Do All-Encounter DCG Models Predict? • Linear additive formulas (OLS regressions) combine predictions based on HCCs and age/sex cells subject to: • Hierarchical restrictions • Exclusions of CCs in prospective models • that are not useful for predicting costs (minor injuries) • vague and discretionary CCs based on concerns about gaming in payment models

19. DCG Predictions:Relative Risk Score (RRS) • Illustrate annual resource use as determined from DCG cost weights • RRS calculated by adding cost weights of an individual’s HCCs and dividing by benchmark (i.e., Medicare) mean dollar amount • RRS normalized so that population mean = 1.00

20. 0.45 54 year old male HCC 5.71 Diabetes with renal manifestation 0.95 Type 1 diabetes 1.84 Congestive heart failure 0.90 Acute myocardial infarction 0.89 Vascular disease with complication 0 Vascular disease 18.09 Dialysis status … ….. 0.46 Diabetes & congestive heart failure 43.30 Relative Risk Score Prospective Relative Risk Score Calculated Health Score for Year 2

21. Which Providers are “More Efficient”?

22. Adjusted Clinical Groups (ACGs) • Clustering of morbidity is a better predictor of health care resource use than presence of specific diseases • Level of resources necessary for delivering health care services is correlated with the morbidity of that population

23. Generating ACG Output(Version 4.5) 15,000 ICD-9-CM Diagnosis Codes Step. 1: Adjusted Diagnosis Groups (32 ADGs) Step 2: Collapsed ADGs (12 CADGs) Step 3: CADGs combined into Major Adjusted Categories (MACs) (26 MACs) AGE, GENDER Step 4: Adjusted Clinical Groups (106 ACGs)

24. Examples of ADGs and Their Common ICD-9-CM Codes A D G C o m m o n D i a g n o s i s ( I C D - 9 - C M C o d e ) 1 T i m e L i m i t e d : M i n o r N o n i n f e c t i o u s G a s t r o e n t e r i t i s ( 5 5 8 . 9 ) 3 T i m e L i m i t e d : M a j o r P h l e b i t i s o f L o w e r E x t r e m i t i e s ( 4 5 1 . 2 ) 9 L i k e l y t o R e c u r : P r o g r e s s i v e m p a c t i o n o f I n t e s t i n e ( 5 6 0 . 3 ) M a l i g n a n t H y p e r t e n s i v e R e n a l D i s e a s e W i t h R e n a l F a i l u r e ( 4 0 3 . 0 1 ) C e r e b r a l T h r o m b o s i s ( 4 3 4 . 0 ) A d u l t O n s e t T y p e I I D i a b e t e s w / K e t o a c i d o s i s 2 5 0 . 1 0 ) 1 0 C h r o n i c M e d i c a l : S t a b l e E s s e n t i a l H y p e r t e n s i o n ( 4 0 1 . 9 ) A d u l t - O n s e t T y p e I D i a b e t e s ( 2 5 0 . 0 0 ) 1 1 C h r o n i c M e d i c a l : U n s t a b l e M a l i g n a n t H y p e r t e n s i v e H e a r t D i s e a s e ( 4 0 2 . 0 ) S i c k l e - C e l l A n e m i a ( 2 8 2 . 6 ) D i a b e t e s M e l l i t u s W i t h o u t C o m p l i c a t i o n 2 5 0 . 0 3 ) 2 3 P s y c h o s o c i a l : T i m e L i m i t e d , C a n n a b i s A b u s e , U n s p e c i f i e d ( 3 0 5 . 2 0 ) M i n o r 2 4 P s y c h o s o c i a l : R e c u r r e n t o r P a n i c D i s o r d e r ( 3 0 0 . 0 1 ) P e r s i s t e n t , S t a b l e B u l i m i a ( 3 0 7 . 5 1 ) 2 5 P s y c h o s o c i a l : R e c u r r e n t o r C a t a t o n i c S c h i z o p h r e n i a ( 2 9 5 . 2 ) P e r s i s t e n t , U n s t a b l e A l c o h o l W i t h d r a w a l D e l i r i u m T r e m e n s ( 2 9 1 . 0 )

25. Clinical Vignette:40 year old woman: diabetes, hypertension (Release 4.5) ICD-9-CM ADG CADG MAC ACG V70.0, Adult Routine Exam 31: Preventative Administrative 250.00, Adult Onset Diabetes, without complications 4100: 2-3 other ADG combinations Age >34 6: Chronic Medical: Stable 10: Chronic Medical: Stable 24: Multiple ADG Categories 401.9, Essential Hypertension 9: Likely to recur: Progressive 5: Chronic Medical: Unstable 250.41, Diabetes with renal manifestations

26. Applying DCGs/ACGs in VA • Explore the feasibility of adapting diagnosis-based measures to the VA population • Examine how well each measure explains concurrent resource utilization and predicts future resource utilization in the VA • Evaluate their performance in clinically meaningful groups • Profile networks on their efficiency after adjustment for case-mix

27. ADG Categories in the VA and a Fee for Service Managed Care Population

28. ACC Categories in the VA and Medicare

29. Predictive Ratios for Patients with MH/SA Disorders DCG/HCC model DCG/HCC model + dummy markers

30. Predictive Ratios For Subgroups of Veterans: Concurrent Models

31. Actual and Predicted Ambulatory Provider Encounters: Concurrent Models

32. ACG, DCG, and Unadjusted Efficiency Indices By Network

33. Improved Special Population Data *Note: A value greater than 1 means that the actual cost exceeds the predicted cost (or price).

34. What Weaknesses Remained? • Did not predict mental health costs well • Did not explain long-term care costs • Did not predict special population costs

35. Patient Safety Indicators (PSIs) • Developed by Agency for Healthcare Research and Quality (AHRQ) • Screen for potential safety events in the inpatient setting • Risk adjustment based on age, sex, age/sex interactions, DRGs, 27 comorbidities (AHRQ comorbidity software) • Examine observed and risk-adjusted PSI rates in VA • 16 medical/surgical PSIs relevant to VA

36. AIDS: Acquired immune deficiency syndrome I Lymphoma Metastatic cancer Solid tumor without metastasis Rheumatoid arthritis/collagen vascular diseases Obesity Weight loss Blood loss anemia Deficiency anemias Alcohol abuse Drug abuse Depression AHRQ Comorbidities for “Decubitus Ulcer” Congestive heart failure Valvular disease Pulmonary circulation disorders Peripheral vascular disorders Hypertension (combine uncomplicated and complicated) Other neurological disorders Chronic pulmonary disease Diabetes, uncomplicated Diabetes, complicated Hypothyroidism Renal failure Peptic ulcer disease excluding bleeding Additional VA comorbidities Paralysis Liver disease Psychoses

37. Characteristics of VA and NIS Samples: Discharges and Patients

38. “Decubitus Ulcer” • VA does well in non-VA comparison • Within VA comparison changes direction

39. Conclusions • Despite different ways of evaluating model performance, model-based resource allocation for subgroups of veterans would not be adequate • Existing methods (ACGs/DCGs) generally underestimate health care costs of individuals with mental health/substance abuse (MH/SA) disorders • Non-VA based risk adjustment can be misleading in VA facility comparisons

40. Adequate Risk Adjustment: Important for Veterans with MH/ SA Disorders • The VA is the largest mental health service delivery system in the United States • Prevalence of mental disorders in VA: 30% • Goal: develop and validate a psychiatric diagnosis-based risk-adjustment measure (the “PsyCMS”) for veterans with MH/SA disorders

41. Guiding Principles • Incorporate all 526 adult MH/SA codes • Develop clinically homogeneous categories based on resource utilization • Demonstrate face validity • Include “manageable” # of categories • Minimize “gaming” • Predict concurrent/prospective utilization and costs

42. Methods • Sample • All veterans who received any health care in the VA during Fiscal Year 1999 (October 1, 1998 through September 1, 1999) and had a MH or SA diagnosis (ICD-9-CM codes 290-312.9 or 316) (n=914,225)

43. Methods • Data • Diagnostic and utilization data from VA inpatient and outpatient administrative data • Costs obtained from VA Health Economics and Resource Center (HERC) • FY99 data used for concurrent modeling; data split into 60% development sample (n=548,535) and 40% validation sample (n=365,690) • FY00 data used for prospective modeling

44. Variables • Dependent Variables • Total MH/SA costs: sum of costs associated with all outpatient and inpatient MH/SA utilization • Outpatient MH/SA encounters: sum of all visits associated with any MH/SA diagnosis code, plus all visits in MH/SA specialty clinics • Inpatient MH/SA utilization: number of days a patient resided in any inpatient setting for MH or SA treatment • Independent Variables • Age, gender, diagnostic information (all MH/SA primary and secondary diagnoses)

45. Methods • Data Analysis (Four major steps): • Classification and categorization of all MH/SA codes into diagnostic classification system • Examined distribution of MH/SA disorders using PsyCMS • Assessed predictive validity of the PsyCMS using concurrent and prospective modeling • Compared performance of PsyCMS with ACGs and DCGs

46. PsyCMS Mood/Psychosis Hierarchy

47. PsyCMS Anxiety Hierarchy

48. PsyCMS Alcohol Hierarchy

49. PsyCMS Drug Hierarchy

50. Diagnostic Cost Group (DCG) Mental Health Groupings HierarchicalCondition Categories (HCCs) DxGroups Condition Categories 53.01 alcoholic psychoses 53.02 drug psychoses 59.01 alcohol dependence 59.02 drug dependence Drug/Alcohol Dependence/ Psychoses Yes HCC 31 54.01 delirium/delusions/hallucinations 54.02 hallucinations, symptomatic 55.01 schizophrenic disorders 56.01 manic & depressive (bipolar) disorder 56.02 major depressive disorders 57.01 paranoid states 57.02 other nonorganic psychoses 60.01 personality disorders, including dissociative identity disorder 134.04 attempted suicide/self-inflicted injury No Psychosis & Other Higher Cost mental Disorders Yes HCC 32 No 60.06 nonpsychotic organic brain syndrome 60.07 depression, excluding depressive psychosis 60.11 autism, other childhood psychoses 60.12 anorexia/bulimia nervosa 60.19 prolonged posttraumatic stress disorder Depression & Other Moderate Cost Mental Disorders Yes HCC 33 58.01 panic disorders/attacks 58.02 generalized anxiety disorder 58.04 somatoform/dissociative disorders 58.05 phobic disorders 58.06 obsessive-compulsive disorders No Yes Anxiety Disorders HCC 34 58.03 other & unspecified anxiety states 58.07 other & unspecified neurotic disorders 59.03 non-dependent abuse of alcohol 59.04 tobacco use disorder 59.05 other nondependent drug abuse 60.02 sexual deviations & disorders 60.03 psychosomatic illness 60.04 acute reaction to stress 60.05 adjustment reaction, excluding prolonged depressive 60.08 behavior disorder 60.09 emotional disorders of childhood/adolescence 60.10 other mental disorders 60.13 attention deficit disorder, other hyperkinetic syndrome 60.14 learning/development learning disorder No Yes Lower Cost Mental Disorders HCC 35