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

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

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

The need for risk adjustment reflects the desire to capture the health status of the population in order to provide equitable compensation and make appropriate comparisons.  Risk adjustment encourages plans to compete on the basis of efficiency and quality, not selection.  

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

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

Types of Risk-Adjustment Measures

Different kinds of RA measures, but as you can see, they are used for payment, provider profiling, quality assessment, disease management, and to describe health status of the population. 

Evaluation Criteria
        - Predictive validity
        - Subgroup fit
        - Administrative feasibility
        - Incentives for efficiency
        - Resistance to gaming

Since there are many risk adjustment measures out there, how can we decide which one to use? We base this on evaluation criteria which can be used for other types of measures also. Specific to RA, we are concerned about gaming. I will show you some ways we have evaluated RA measures in the VA

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)

I am going to focus in this presentation on diagnosis-based RA.  CMS now use inpatient diagnosis codes to adjust payments to Medicare+Choice contractors.  States use risk-adjusted payments to MCOs for Medicaid enrollees. 

Upcoding: increase in the coding of related conditions affecting treatment—prevent gaming or manipulation of diagnoses

Creep: # and severity of diagnoses where diagnosis is uncertain.

Concerns about these issues are less so in the VA because we are less concerned about financial reimbursement. For example, DCG software has built-in safeguards. It excludes ill-defined codes and bundles related diagnoses. Diagnostic data are not immune from gaming but criteria exist for defining most major conditions and this helps to provide some validation.

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)

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

VA inpt and outpt files from Medical SAS datasets at Austin.  

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

DCGs developed in mid 1980’s at BU as an inpatient data model using Medicare FFS files. Models have now been developed for Medicaid and commercial populations using these respective datasets.  There are a number of model variations depending on type of population being analyzed (Medicare, etc), source of diagnostic data, purpose (explanation vs. payment).  Inpatient models are called PIP-DCG models; all-encounter models are called HCC models and these are now used by CMS to risk-adjust Medicare+Choice payment. . Basic premise of DCGS is that it recognizes cumulative effect of multiple conditions in predicting costs. We use the Medicare population model as the benchmark population when we calculated DCG scores in the VA data- VA population more similar to Medicare than Medicaid or commercial populations. Based on medical condition (or conditions) and age/gender categories, the model predicts the total medical costs for each individual. 

DCG Model Overview

Relative 

Risk Scores

DCG

Categories

ICD-9-CM

Codes

Clinical

Groups

Resource

Use

Predictor

Relative

Health

Status

Clinical

Profiles

DCG Clinical Classifications

Hierarchies imposed for predictions

Condition Categories (CCs)

(n= 184)

Aggregated Condition Categories (ACCs)

(n= 30)

ICD-9-CM codes

(n = 15,000+)

DxGroups

(n =781)

PIP-DCGClinical Classification

PIP=Principal Inpatient

Based on Inpatient Dx only

Single-condition model

Relative risk

 score

DCG models use all available diagnoses from physicians and hospitals as recorded in International

Classification Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes, and classify each

diagnosis into 781 clinically-based DxGroups. These DxGroups are further mapped into 184 Condition

Categories (CCs) with each CC consisting of DxGroups that are clinically related and similar

with respect to levels of resource use. Most of the DxGroups are mapped into only one CC. However

a few are mapped into 2 CCs. Because CCs are not mutually exclusive, a single person may have multiple CCs. The CCs

are further collapsed into 30 broadly defined Aggregated Condition Categories (ACCs), which are

useful for profiling or presenting summarized analyses. Hierarchies are imposed on the CCs to created HCCs, discussed later and also RRS (later). Will show you an example of RRS later on. 

Clinical Vignette:59 year old woman AMI, COPD, renal insufficiency (Release 5.0)

410.91 AMI of unspecified site, initial episode of care

491.2 obstructive chronic bronchitis

586 renal failure nos

585 chronic renal failure

72.01 AMI, initial episode of care

96.01 emphysema/

chronic bronchitis

106.04 renal failure,

unspecified

106.03 chronic renal failure

50 AMI

64 COPD

78 Renal 

Failure

ICD-9-CM

DxGroup

CC

518.1 Interstitial emphysema

This is from Release 5.0, CCs and Dxgroups have changed slightly since then, but not very much. Now they have a product called DxCG Risk Smart. Version 2...also have pharmacy models. 

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

Hierarchical Condition Categories (HCCs) are generated from

CCs by imposing hierarchies which identify only the most costly manifestation of each distinct disease.

Hierarchies are diseases of similar type, primarily the same body system. Within hierarchies, only the weight of the highest cost category is used to assess risk.

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

THE DCG model classifies each person within the cancer hierarchy based on the highest cost category. So a patient with metastatic cancer may also have benign neoplasm.  To pay a provider for the lower cost, non-specific diagnoses create an incentive for gaming and rewards upcoding. Therefore, DCG models base predictions of cost only on the highest cost cancer cc.

Current and Prospective Predictions

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

Use linear additive formulas. Each clinical group and demographic factors contribute additively to the predicted payment and RRS.  There are separate cost weights for concurrent and prospective models.

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

Relative risk score based on age, gender and each CC identified. There is a risk weight for each medical cc. Risk weights reflect an estimate of the marginal cost for a given medical condition relative to the base cost for individuals with no medical conditions.  Offered risk weights are those developed on the benchmark (Medicare) population.  Need to recalibrate weights for a specific population. We have recalibrated cost weights based on the health status of the VA population. 

Prospective Relative Risk Score Calculated

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

Health Score for Year 2

Dialysis status clearly determines his relative risk score

Which Providers are “More Efficient”?

Example of using RA measure to compare providers...use observed over expected (from the DCG model) you can see that Group E is less efficient than Group A (O/E is greater than 1)

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

Generating ACG Output(Version 4.5)

 15,000 ICD-9-CM Diagnosis Codes

Step. 1: Adjusted Diagnosis Groups

Step 2: Collapsed ADGs

Step 3: CADGs combined into Major Adjusted Categories (MACs)

Step 4: Adjusted Clinical Groups

(32 ADGs)

(12 CADGs)

(26 MACs)

(106 ACGs)

AGE, GENDER

We’ved used ACGs 6.0, now have 8.0, also have pharmacy models but basic classification has not changed.

Examples of ADGs and Their Common ICD-9-CM Codes

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Clinical Vignette:40 year old woman: diabetes, hypertension (Release 4.5)

250.41, Diabetes with renal manifestations

401.9, Essential Hypertension

250.00, Adult Onset Diabetes, without complications

V70.0, Adult Routine Exam

ICD-9-CM

ADG

CADG

MAC

ACG

31:  Preventative Administrative

10: Chronic Medical: Stable

9: Likely to recur: Progressive

5: Chronic Medical: Unstable

6: Chronic Medical: Stable

24: Multiple ADG Categories

4100: 2-3 other ADG combinations Age >34

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

We received a grant to explore use of these RA measures in the VA- Can we take an off the shelf system and apply it in a different population than it was designed for? given that they were developed on other populations, we wanted to examine how well they captured VA disease burden and predicted utilization/costs in the VA. Without adequate RA, inequitable resource allocations to subgroups who may need them the most may be quite likely. Therefore evaluation is critical to adoption of off the shelf systems. 

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

ACC Categories in the VA and Medicare

Predictive Ratios for Patients with MH/SA Disorders

DCG/HCC model

DCG/HCC model + dummy markers

For MH population, DCG models doesn’t do that well. Need to add dummy markers (patients with different severity of SA in specific HCCs) to make the model more predictive. Models did not separate pts with SA from those with MH. Now they do...Models only improved modestly

Predictive Ratios For Subgroups of Veterans: Concurrent Models

This slide tells the same story as the next one....

Actual and Predicted Ambulatory Provider Encounters: Concurrent Models

Finally, I’ve included a few slides to show you that some of the work we’re doing corroborates these findings. DCGS and ACGS underpredict at high end, overpredict at low end.

ACG, DCG, and Unadjusted Efficiency Indices By Network

We looked earlier at provider efficiency with DCGs. This shows you that which case-mix measure you use will affect judgment of networks’ efficiency. Differences between unadjusted and adjusted and between ACG/DCG...look at VISN Q- more efficiet by ACGs than DCGs. 

Improved Special Population Data

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

The VERA Pt Classification Working Group reviewed RA systems in the VA. They use complex and basic needs pts. They modified the DCGs because of differences in VA/Medicare popualtion (called it VA DCG vs. Medicare DCG). And applied these models to VA special pops. Slides from MSG. They realigned HCCs based on VA costs, and combined 17 low cost HCCs based on the data. Recalibrated models using cost weights from VA cost data. Each HCC had different cost in VA from cost in Medicare. 

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

What did they conclude? Because DCG is regression model and the vast majority of VA patients are in low cost groups, similar to us, they found that the model tends to overpay for low cost groups and underpay for high cost groups. 

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

This is another example from recent work of ours which shows the need to fix the RA from an off the shelf system.....here we used the PSIs...PSI RA developed using AHRQ databases- state hospital discharge databases, includes women, children, etc.

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

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 

Additional VA comorbidities 

Paralysis 

Liver disease 

Psychoses

These are the 27 comorbidities in the v2.1 RA,

24 of the 27 are in the PSI 3 RA, 

Note that I am showing only the comorbidites , from which we chose 15 to add back in.  15 which we thought would be more important in the VA

 The xbeta also includes DRGs, age, sex and their interactions

Characteristics of VA and NIS Samples: Discharges and Patients

Why was it impt to add comorbidities? See differences bet VA/NIS pops

“Decubitus Ulcer”
        - VA does well in non-VA comparison 
        - Within VA comparison changes direction
| PSI 3    |
|----------|
| Facility | VA Observed | AHRQ Expected | VA Expected | VA Obs / AHRQ Expt | VA Obs / VA Expt |
| A        | 19.45 | 22.92 | 17.60 | 0.85 | 1.11 |
| B        | 18.16 | 24.10 | 17.99 | 0.75 | 1.01 |
| C        | 19.42 | 24.21 | 20.50 | 0.80 | 0.95 |

Say va based / ahrq based. When we use the PSI software, facility A looks good, as does B, but when we add in RA variables, this changes. 

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

And lead to inequitable resource allocations, unfair provider profiling

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

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

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) 

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

VA inpt and outpt data files from medical SAS datasets at Austin

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)

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

PsyCMS Mood/Psychosis Hierarchy

PsyCMS Anxiety Hierarchy

PsyCMS Alcohol Hierarchy

PsyCMS Drug Hierarchy

Diagnostic Cost Group (DCG) Mental Health Groupings

DxGroups

Condition Categories

Hierarchical Condition Categories (HCCs)

HCC 31

HCC 32

HCC 33

HCC 34

HCC 35

No

No

No

No

Yes

Yes

Yes

Yes

Yes

53.01 alcoholic psychoses

53.02 drug psychoses

59.01 alcohol dependence

59.02 drug dependence

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

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

58.01 panic disorders/attacks

58.02 generalized anxiety disorder

58.04 somatoform/dissociative disorders

58.05 phobic disorders

58.06 obsessive-compulsive disorders

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

Drug/Alcohol Dependence/ Psychoses

Psychosis & Other Higher Cost mental Disorders

Depression & Other Moderate Cost Mental Disorders

Lower Cost Mental Disorders

Anxiety Disorders

Adjusted Clinical Group (ACG) Psycho-social Groupings

ICD-9-CM Psychiatric Codes

ADG 23

Psycho-social: Time Limited, Minor

ADG 24

Psycho-social: Recurrent or Persistent, Stable

ADG 25

Psycho-social: Recurrent or Persistent, Unstable

CADG 10

Psycho-Social

MAC 10

Psychosocial

MAC 17

Acute: Minor and Psychosocial

MAC 24

All Other Combinations Not Listed Above
        - CADG 10 & CADG 1 & CADG 3
        - CADG 10 & CADG 1
        - CADG 10 & CADG 1  & CADG 2  &   CADG 3
        - CADG 10 & All Other Remaining CADG Combinations

ACG 1500

ACG 1400

ACG 2500

ACG 2700

Refer to MAC 24 Decision Tree

No

Yes

Yes

No

No

Yes

No
        - CADG 10 Only
        - CADG 10 & CADG 12 & Anything Else

Yes

ACG 3700

MAC 21

Acute: Minor & Likely to Recur & Psychosocial

ADG 25?

ADG 24?

ACG 1300

ADG 25?

ADG 24?

MAC 12

Pregnancy 

Refer to MAC 12 Decision Tree

ACG 3500

ACG 2600

MAC 23

Acute: Minor & Acute:  Major & Likely to Recur & Psychosocial

Results: Prevalence of Selected PsyCMS Categories

Table 1: Total MH/SA Costs for Selected Categories

Table 2: Model Goodness of Fit for Concurrent (FY99) Validation Samples

Table 3: Model Goodness of Fit for Prospective (FY00) Validation Samples

Conclusions
        - PsyCMS appears to be valid and reliable measure for MH/SA risk adjustment 
        - PsyCMS performs better than other systems in predicting concurrent and prospective MH/SA costs/utilization 
        - It can serve as risk-adjustment system for describing MH/SA populations, profiling MH/SA services, and budgeting future MH/SA resources

Any Questions?

Amy Rosen, Ph.D.E-mail akrosen@bu.edu

CHQOER Bedford VAMC200 Springs Road (152)Bedford, MA 01730  U.S.APhone (781) 687-2960Fax (781) 687-3106

http://www.va.gov/chqoer/RAPS/RAPS.htm

References
        - Rosen AK, Loveland S, Anderson J, Rothendler J, Hankin C, Moskowitz M, Berlowitz DR.  Evaluating diagnosis-based case-mix measures: how well do they apply to the VA population?  Medical Care 2001; 39(7): 692-704.
        - Rosen AK, Loveland S, Anderson J.  Applying DCGs to Examine the Disease Burden of VA Facilities: Comparing the Six “Evaluating VA Costs” Study Sites to Other VA Sites and Medicare.  Medical Care, June 2003: 41(6 suppl): II-91-II-102. 
        - Rosen AK, Loveland S, Anderson J, Hankin C, Breckenridge J, Berlowitz DR.  Diagnostic Cost Groups (DCGs) and concurrent utilization among patients with substance abuse disorders. Health Services Research, 2002: 37(4): 1079-1102.
        - Rakovski C, Rosen AK, Loveland S, Anderson JJ, Berlowitz DR, Ash A. Evaluation of diagnosis-based risk adjustment measures among specific subgroups: can existing measures be improved by simple modifications?" Health Services and Outcomes Research Methodology, 2002: 3(1): 57-74.
        - Rosen AK, Rakovski C, Loveland S, Anderson JJ, Berlowitz DR.  Profiling resource use across providers: do different outcomes affect assessments of provider efficiency after case-mix adjustment?  American Journal of Managed Care, 2002: 8(12): 1105-1115.
        - Rosen AK, Loveland S, Rakovski C, Christiansen C, Berlowitz DR.  Do different case-mix measures affect assessments of provider efficiency?  Lessons from the VA.  The Journal of Ambulatory Care Management 2003: 26(3): 229-242.
        - Rosen AK, Reid R, Broemeling AM, Rakovski C.  Applying a risk adjustment framework to primary care: can we improve on existing measures?  Annals of Family Medicine, 2003: 1(1): 44-51.

References (cont’d)
        - Rosen AK, Trivedi P, Amuan M, & Montez M.  The John Hopkins Adjusted Clinical Groups (ACGs) case-mix system: A risk-adjustment methodology currently available at the VA Austin Automation Center. VIReC Insights Vol. 4, No. 1. Hines, IL: VA Information Resource Center, 2003.  Available at  http://virec.research.med.va.gov .  
        - Liu CF, Sales AE, Sharp ND, Fishman P, Sloan KL, Todd-Stenberg J, Nichol WP, Rosen AK, Loveland S. Case-mix adjusting performance measures in a VA population: pharmacy- and diagnosis- based approaches.  Health Services Research, 2003: 38 (5): 1319-1338.
        - Warner G, Hoenig, H, Montez M, Wang F, Rosen AK. Evaluating diagnosis-based risk-adjustment methods in the spinal cord dysfunction population.  Archives of Physical Medicine and Rehabilitation, 2004: 85(2): 218-226.
        - Rosen AK, Christiansen CL, Montez ME, Loveland S, Shokeen P, Sloan KL, and Ettner SL.  Evaluating risk-adjustment methodologies for patients with mental health and substance abuse disorders in the Veterans Health Administration. International Journal of Healthcare Technology and Management, 2006: 7 (1/2): 43-81.
        - Sloan KL, Montez ME, Spiro A III, Christiansen CL, Loveland S, Shokeen P, Herz L, Eisen S, Breckenridge, JN, Rosen AK. Development and validation of a psychiatric case-mix system. Medical Care 2006: 44:568-580.
        - Montez ME, Christiansen CL, Ettner SL, Loveland S, Shokeen P, and Rosen AK.  Performance of statistical models to predict mental health and substance abuse cost. BMC Medical Research Methodology, October 2006: 6:53. 
        - Rosen AK, Zhaos S, Rivard P, Loveland S, Montez M, Elixhauser A, and Romano P.  Tracking Rates of Patient Safety Indicators over Time: Lessons from the VA.  Medical Care 2006: 44(9): 850-861. 
        - www.dxcg.com
        - www.acg.jhsph.edu
        - Risk Adjustment for Measuring Health Care Outcomes, edited by Lisa Iezzoni, Health Administration Press, 3rd edition, 2003.