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)
© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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)
© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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?
© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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
© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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 populationlevel utilization data than Medicaid claims
 Population: Adults with diabetes (DM), established
patients in a network of CHCs in Oregon, 2005-2007
© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
Congruence between the data sources
© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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
© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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)
© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
Methods – analyses
 % of patients with services documented in:
 Only EHR data
 Only Medicaid claims data
 Both datasets
 Among patients with >=1 DM service in 20052007, what characteristics associated with
documentation?
© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
Methods
© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
Results – OCHIN adult diabetic patients
with a Medicaid ID (n = 2,103)
Age
19-35
36-50
51-64
>=65
9%
29%
41%
22%
R/E
Sex
F
M
62%
38%
Insurance 100%
coverage <100%
2005-2007
Lang
English
67%
Spanish 14%
Other / unk 19%
FPL
0-99%
100-199%
>=200%
Unknown
82%
14%
2%
2%
© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
NH White
Hispanic
Black
API
Other / unk
61%
17%
10%
8%
5%
64%
36%
Results – receipt of diabetes services in 2005-2007
among 2,103 patients with Medicaid ID #, according to
each data source
© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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
© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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)
© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
Services received by all pts w/DM;
includes persons with no Medicaid ID
n persons = 4,240
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




© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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
© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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?
© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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
© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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
© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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?
© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
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
© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
Questions?
Rachel Gold
Jennifer DeVoe
© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH
rachel.gold@kpchr.org
devoej@ohsu.edu