AcademyHealth Annual Research Meeting Tuesday, June 10, 2008

1. Integrating Clinical Data Warehouses: How Can Multi-System Care for Older Veterans Be Measured Consistently? AcademyHealth Annual Research Meeting Tuesday, 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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. Positive/Negative Predictive Probability for PC E/M Code Given PC Stop Code Encounter

12. Positive/Negative Predictive Probability for PC E/M Code Given PC Stop Code Encounter By Year

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

14. Positive/Negative Predictive Probability for PC Provider Type Given PC Stop Code Encounter By Year

15. Positive/Negative Predictive Probability for PC Care Type Given PC Stop Code Encounter

16. Positive/Negative Predictive Probability for PC Care Type Given PC Stop Code Encounter By Year

17. Primary Care Type Classification between Medicare and VA

18. 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