Integrating Clinical Data Warehouses: How Can Multi-

System Care for Older Veterans Be Measured Consistently?

AcademyHealth Annual Research MeetingTuesday, June 10, 2008

Presenter: James F. Burgess, Jr., Ph.D.VA Center for Organization, Leadership and

Management Research and Boston University School of Public Health

Co-Authors/Collaborators

• Matt Maciejewski (VA Center for Health Services Research in Primary Care and U. of North Carolina School of Pharmacy)

• Mark Perkins (VA Center for Outcomes Research in Older Adults)

• Nancy Sharp (VA Center for Outcomes Research in Older Adults and U. of Washington)

• Chuan-Fen Liu (VA Center for Outcomes Research in Older Adults and U. of Washington)– Supported by VA HSR&D IIR 04-292

Outline

• Problem of merging data from Clinical Data Warehouses with data from different health care systems

• Possible approaches to matching VA and Medicare services by type of care

• Introduce the study motivating this issue • Methodology of our chosen approach• Context of identifying “primary care” in this

particular VA case to research generally

Merging Data from Different Health Care Systems

• Data generating processes vary, especially nature of encounter data– By location, by provider, by diagnosis or grouped

diagnoses, by procedure, others

• Two kinds of payment incentives (data collected to be paid, pay for reporting or other payments or incentives for particular data to be collected)

• Origins of data (primarily paper/electronic)• Auditing or other scrutiny helps accuracy

Why Do We Want to Match VA and Medicare Services by Care Type?• To identify continuity of primary care,

we need to:– Identify primary care in Medicare in the

absence of a variable that specifically identifies primary care

– Classify VA and Medicare encounters as either primary care or something else

• Processes to generate measures that are an essential part of the actual patient care workflow are most accurate

Dual Use, Continuity of Care and Duplication of Care Study

• Purpose– Examine how continuity of primary care is

impacted by use of VA and Medicare services– Evaluate duplication of preventive and high

cost services

• Sample– Veterans obtaining primary care at CBOCs

and/or VAMC primary care clinics in 2000

• Follow Up Years: 2000-2004

Matching VA and Medicare Data

• Two basic approaches: matching on cost or workload counts (we do counts)

• Aligning incentives and organizational structures in the two systems

• VA a provider focused on treatment, Medicare a payor focused on billing

• Most physicians in VA employed by VA, most Medicare billing MDs are not employed by the billing hospitals

Philosophies of Matching

• Try to make VA look like Medicare– Use CPTs and match as though VA data is billing

data (severely undercounts VA work)

• Try to make Medicare look like VA– Classify Medicare work into VA-type “Clinic Stop”

categories (these are often used for VA research)

• Create a hybrid and transform both– Pick and choose from advantages and disadvantages

of data in each sector and select a comparison point that directly reflects neither system

General Approach

• Classify VA and Medicare encounter into “Care Type” based on hierarchical algorithm

• Roll up encounters:– by subject – by care type – by fiscal year

• For each subject, join VA care type counts and Medicare care type counts

• Use combination of provider specialty and Procedure (CPT-4) codes to classify

Validation of Algorithm

• VA definition of “Primary care” vs. encounters that algorithm would call “primary care”

• VA definition of primary care (VA’s DSS system)– Encounter at clinic stop 323, 301, 318, 350, or 319

• Algorithm’s definition of primary care (PC)– Primary care provider (Family Practice(FP)/PC

Physician, FP/PC Nurse Practitioner, or FP Physician Assistant)

– E&M CPT4 code associated with PC office visit– Other CPT4 code not Medicine or not E&M code

associated with specialty care visit

Positive/Negative Predictive Probability for PC E/M Code

Given PC Stop Code EncounterPrimary Care E/M Code

PC Stop Code

EncounterYES NO Type of

Prob.Percent

YES 190,986 8,452

Positive Pred. Prob.

95.7%

NO 147,961 376,554

Negative Pred. Prob.

71.8%

Positive/Negative Predictive Probability for PC E/M Code

Given PC Stop Code Encounter By Year

Measure YEAR 2000 2001 2002 2003 2004

VA PC Stop Code Encounter vs.

Primary Care E/M code

PV+ 94.6% 95.7% 96.0% 96.3% 96.6%

PV- 69.5% 71.0% 71.7% 73.4% 74.2%

Positive/Negative Predictive Probability for PC Provider Type Given PC Stop Code Encounter

Primary Care Provider Type

PC Stop Code

EncounterYES NO Type of

Prob.Percent

YES 147,740 51,698

Positive Pred. Prob.

74.1%

NO 178,584 345,931

Negative Pred. Prob.

66.0%

Positive/Negative Predictive Probability for PC Provider Type Given PC Stop Code Encounter

By Year

Measure YEAR 2000 2001 2002 2003 2004

VA PC Stop Code Encounter vs.

Primary Care Provider Type

PV+ 74.6% 76.8% 75.2% 71.3% 71.0%

PV- 70.0% 64.6% 64.2% 65.0% 65.4%

Positive/Negative Predictive Probability for PC Care Type

Given PC Stop Code EncounterPrimary Care Care Type

PC Stop Code

EncounterYES NO Type of

Prob.Percent

YES 103,870 95,568

Positive Pred. Prob.

52.0%

NO 19,634 504,881

Negative Pred. Prob.

96.3%

Positive/Negative Predictive Probability for PC Care Type

Given PC Stop Code Encounter By Year

Measure YEAR 2000 2001 2002 2003 2004

VA PC Stop Code Encounter vs.

Primary Care Care Type

PV+ 51.8% 53.3% 52.5% 50.7% 51.8%

PV- 96.7% 96.0% 96.0% 96.2% 96.3%

Primary Care Type Classification between Medicare and VA

Classification Algorithm Medicare(N = 739 K)

VA(N = 724 K)

Number of records

% Number of records

%

VA Specific Stop code N/A N/A 199,438 27.5

Primary care E/M codes 249,280 33.7 338,947 46.8

Primary care provider type

197,274 20.8 326,324 45.1

Primary care type (E/M and provider type)

103,032 13.9 123,504 17.1

Conclusions and Implications

• Extreme caution in interpretation of terms like “primary care” that we think we understand is important when comparing across systems

• Generalizing from studies using VA clinical data warehouse systems to identify types of patient care services to non-VA services is difficult

• Comprehensive care for Medicare eligible veterans using VA and Medicare systems would benefit from a joint clinical data warehouse