Format

Send to

Choose Destination
Am J Prev Med. 2016 Nov;51(5):752-761. doi: 10.1016/j.amepre.2016.07.004. Epub 2016 Aug 10.

Measuring Preventive Care Delivery: Comparing Rates Across Three Data Sources.

Author information

1
Department of Family Medicine, Oregon Health & Science University, Portland, Oregon. Electronic address: bailstef@ohsu.edu.
2
Department of Family Medicine, Oregon Health & Science University, Portland, Oregon.
3
Department of Family Medicine, Oregon Health & Science University, Portland, Oregon; Department of Public Health and Preventive Medicine, Division of Biostatistics, Oregon Health & Science University, Portland, Oregon.
4
OCHIN, Inc., Portland, Oregon.
5
Department of Family Medicine, Oregon Health & Science University, Portland, Oregon; OCHIN, Inc., Portland, Oregon.
6
OCHIN, Inc., Portland, Oregon; Kaiser Permanente Northwest Center for Health Research, Portland, Oregon.

Abstract

INTRODUCTION:

Preventive care delivery is an important quality outcome, and electronic data reports are being used increasingly to track these services. It is highly informative when electronic data sources are compared to information manually extracted from medical charts to assess validity and completeness.

METHODS:

This cross-sectional study used a random sample of Medicaid-insured patients seen at 43 community health centers in 2011 to calculate standard measures of correspondence between manual chart review and two automated sources (electronic health records [EHRs] and Medicaid claims), comparing documentation of orders for and receipt of ten preventive services (n=150 patients/service). Data were analyzed in 2015.

RESULTS:

Using manual chart review as the gold standard, automated EHR extraction showed near-perfect to perfect agreement (κ=0.96-1.0) for services received within the primary care setting (e.g., BMI, blood pressure). Receipt of breast and colorectal cancer screenings, services commonly referred out, showed moderate (κ=0.42) to substantial (κ=0.62) agreement, respectively. Automated EHR extraction showed near-perfect agreement (κ=0.83-0.97) for documentation of ordered services. Medicaid claims showed near-perfect agreement (κ=0.87) for hyperlipidemia and diabetes screening, and substantial agreement (κ=0.67-0.80) for receipt of breast, cervical, and colorectal cancer screenings, and influenza vaccination. Claims showed moderate agreement (κ=0.59) for chlamydia screening receipt. Medicaid claims did not capture ordered or unbilled services.

CONCLUSIONS:

Findings suggest that automated EHR and claims data provide valid sources for measuring receipt of most preventive services; however, ordered and unbilled services were primarily captured via EHR data and completed referrals were more often documented in claims data.

TRIAL REGISTRATION:

ClinicalTrials.gov NCT02355132.

PMID:
27522472
PMCID:
PMC5067199
DOI:
10.1016/j.amepre.2016.07.004
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center