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

© 2011, KAISER PERMANENTE CENTER FOR HEALTH RESEARCH

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)


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)


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?


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


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


Congruence between the data sources


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


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)


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?


Methods


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%


Results – receipt of diabetes services in 2005-2007

among 2,103 patients with Medicaid ID #, according to

each data source


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


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


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


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


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?


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


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


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?


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


Questions?Rachel Gold [email protected] DeVoe [email protected]

Documents

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