Rachel Gold, PhD, MPH (Kaiser Permanente Center for Health Research)

1. Which Data Source Provides More Complete Information for Assessing Preventive Care Utilization in Vulnerable Populations?State Health Research & Policy Interest Group Meeting, June 11, 2011 Rachel Gold, PhD, MPH (Kaiser Permanente Center for Health Research) Jennifer DeVoe, MD, DPhil (Safety Net West PBRN, OCHIN, OHSU Dept. Family Medicine) Patti McIntire, BA:PPPM (OCHIN) Jon Puro, MPA:HA (OCHIN) Susan Chauvie, RN, MPA-HA (OCHIN) Charles A. Gallia, PhD (Oregon Medicaid Office)

2. Background and purpose States' public coverage policies rapidly changing with health care reform How to evaluate the impact of these changes on patient, system outcomes? Claims data often used to measure policy-relevant outcomes… Service utilization, care quality But claims data … … Incomplete for the uninsured / persons in periods without coverage Many Medicaid recipients have discontinuous coverage Hard to track utilization over time … Are based on billing, but not all diagnoses / services are billed … And their outpatient care reliability is debated(Wolinsky et al, 2007; Cooper et al, 2007)

3. Background and purpose How can we study the impact of health care reform on care utilization … and include the uninsured? Can CHC’s EHR utilization data “bridge the gap” in Medicaid claims data? … And thus help states plan for / evaluate policy interventions, notably those required by the ACA?

4. Can CHC’s EHR utilization data “bridge the gap” in Medicaid claims data? Community health centers (CHC) Serve many uninsured, publicly insured Historically, CHCs’ utilization data difficult / expensive to use But … more and more have electronic health records (EHR) Now mandated Some EHRs linked across multiple CHCs Could be an important source of utilization data among uninsured / newly insured Automated, real-time data

5. Analysis summary Goals: Link CHC EHR data with Medicaid claims data Measure congruence Identify populations more likely to have utilization data in either data source Hypothesis: CHCs’ EHR has more complete population-level utilization data than Medicaid claims Population: Adults with diabetes (DM), established patients in a network of CHCs in Oregon, 2005-2007

6. Congruence between the data sources

7. Methods – data sources 1. OCHIN EHR data A Health Center Controlled Network since 2001 Currently >140 CHC clinics, >860,000 unique patients, multiple states; >85% of FQHC visits in Oregon EHR is linked across all clinics, 1 patient ID Practice management data, Medical record data Study used utilization data from 50 Oregon CHCs, 2005-2007 … ‘Established’ patients with DM (n = 4,240) … who were ever enrolled in Medicaid (n = 2,103) Of visits not covered by Medicaid, >90% had no other insurance 2. Oregon Medicaid claims, 2005-2007

8. Methods – data linkage Individual-level linkages OCHIN’s EHR data + Oregon Medicaid claims data Used Medicaid ID numbers Service utilization 2005-2007 LDL cholesterol screening Influenza vaccination Nephropathy screening Hemoglobin A1c screening (HbA1c)

9. Methods – analyses % of patients with services documented in: Only EHR data Only Medicaid claims data Both datasets Among patients with >=1 DM service in 2005-2007, what characteristics associated with documentation?

10. Methods

11. Results – OCHIN adult diabetic patients with a Medicaid ID (n = 2,103) Age 19-35 9% R/E NH White 61% 36-50 29% Hispanic 17% 51-64 41% Black 10% >=65 22% API 8% Other / unk 5% Sex F 62% Insurance 100% 64% M 38% coverage <100% 36% 2005-2007 Lang English 67% Spanish 14% Other / unk 19% FPL 0-99% 82% 100-199% 14% >=200% 2% Unknown 2%

12. Results – receipt of diabetes services in 2005-2007 among 2,103 patients with Medicaid ID #, according to each data source

13. Results – % of diabetes services (2005-2007) in Medicaid claims alone, OCHIN EHR data alone, or in both datasets Services received by all pts w/DM; includes persons with no Medicaid ID n persons = 4,240

14. Results – % of diabetes services (2005-2007) in Medicaid claims alone, OCHIN EHR data alone, or in both datasets Services received by pts w/DM & a Medicaid ID; n persons = 2,103 (subset of the 4,240 with DM) Services received by all pts w/DM; includes persons with no Medicaid ID n persons = 4,240

15. Results – persons more likely to have data in EHR data only (all p<.05): >1 LDL >1 flu >1 micro >1 HbA1c > 64 years old vs younger     Males vs females    Spanish-speaking vs English    >=100% vs <100% FPL     No continuous Medicaid in 2005-7 vs fully covered    

16. Results overview: congruence between datasets • <50% of persons receiving services had documentation of the service in both EHR and claims data (in most cases) • Services in EHR only likely occurred while uninsured • OCHIN EHR has: • Higher % of services seen in just 1 dataset, compared to the % of services seen in Medicaid that are seen in just 1 dataset • Utilization rates closer to the combined total than those from claims alone • Optimal reporting = combined EHR and claims data

17. Limitations Missing private coverage data? Other payors <6% of visits Care outside the OCHIN network? Flu vaccinations Older patients with dual coverage Services in Medicaid claims but not EHR data: likely received outside of the OCHIN network Research needed DM patients defined based on clinic visit data Those never seen doing much better? Or much worse?

18. Discussion Medicaid claims data alone: Underreported care in CHCs CHC patients often have insurance gaps: even among those with a Medicaid ID, many had an insurance gap during the study Certain subgroups especially likely to be missing from claims Especially those that have a more difficult time maintaining Medicaid coverage Claims alone may underestimate care quality CHCs’ EHR data can be used to Measure utilization Inform policy discussions CHCs’ EHR data + Medicaid claims = more complete capture

19. Discussion Policy impact evaluations based on Medicaid claims alone do not accurately represent … CHC populations The uninsured / sporadically insured CHCs' EHR data should inform policies relevant to care delivery, outcomes Networked CHC EHRs = emergent new utilization data among uninsured Could become the gold standard of utilization data A new resource for policy makers to better understand … Health services utilization Population health CHC quality performance EHR data therefore key to evaluating the impact of health care reform

20. Discussion Linked CHC EHRs are data resources that should be further developed For evaluation of state health policy changes, and more When not available, then what? All-payer claims databases = no uninsured Such datasets not common (yet) EHR data may be getting better with time: 3 of 4 outcomes: EHR data  closer to the combined dataset, over 2005 – 2007 Data becoming more complete as systems mature?

21. Discussion: potential future research One of 1st studies to use linked safety net clinic EHR + Medicaid claims data Potential use of such data = a wide range of studies Policy impact assessment Practice change impact assessment Primary care delivery, quality Quality improvement Health services research Comparative effectiveness research CHCs can work with researchers to study their own care delivery

22. Questions? Rachel Gold rachel.gold@kpchr.org Jennifer DeVoe devoej@ohsu.edu