Medicaid coverage accuracy in electronic health records

Abstract

Health insurance coverage facilitates access to preventive screenings and other essential health care services, and is linked to improved health outcomes; therefore, it is critical to understand how well coverage information is documented in the electronic health record (EHR) and which characteristics are associated with accurate documentation. Our objective was to evaluate the validity of EHR data for monitoring longitudinal Medicaid coverage and assess variation by patient demographics, visit types, and clinic characteristics. We conducted a retrospective, observational study comparing Medicaid status agreement between Oregon community health center EHR data linked at the patient-level to Medicaid enrollment data (gold standard). We included adult patients with a Medicaid identification number and ≥1 clinic visit between 1/1/2013-12/31/2014 [>1 million visits (n = 135,514 patients)]. We estimated statistical correspondence between EHR and Medicaid data at each visit (visit-level) and for different insurance cohorts over time (patient-level). Data were collected in 2016 and analyzed 2017-2018. We observed excellent agreement between EHR and Medicaid data for health insurance information: kappa (>0.80), sensitivity (>0.80), and specificity (>0.85). Several characteristics were associated with agreement; at the visit-level, agreement was lower for patients who preferred a non-English language and for visits missing income information. At the patient-level, agreement was lower for black patients and higher for older patients seen in primary care community health centers. Community health center EHR data are a valid source of Medicaid coverage information. Agreement varied with several characteristics, something researchers and clinic staff should consider when using health insurance information from EHR data.

Keywords: Electronic health records; Health insurance; Health policy; Medicaid.

Figures

Fig. 1

Measures of Medicaid coverage agreement between EHR and Medicaid data, stratified by pre- and post-Affordable Care Act (ACA) periods. Note: OCHIN health information network Epic© electronic health record (EHR) data are referred to as EHR data and Oregon Medicaid enrollment data are referred to as Medicaid data. Agreement is defined as total proportion of encounters in which EHR data denoted the same coverage status as the ‘gold standard’ (i.e., Medicaid data). PABAK adjusts kappa for differences in prevalence of the conditions and for bias between data sources. Sensitivity is the probability that EHR denoted coverage when the assumed gold standard also denoted coverage. Specificity is the probability that EHR correctly classified ‘no Medicaid coverage’ when the assumed gold standard also denotes no Medicaid coverage. PPV is the likelihood of a visit being covered by Medicaid when the gold standard denoted Medicaid coverage. NPV is the likelihood of an encounter not being covered by Medicaid when the gold standard denoted no Medicaid coverage. Data were collected in 2016 and analyzed 2017–2018.

Fig. 2

Odds ratios for patient and clinic factors associated with agreement on Medicaid coverage at the patient-level, between EHR and Medicaid data. Note: OCHIN health information network Epic© EHR data are referred to as EHR data and Oregon Medicaid enrollment data are referred to as Medicaid data. The number of chronic conditions ranged from 0 to 5 based on the following conditions: hypertension, diabetes, coronary artery disease, lipid disorder, and asthma/chronic obstructive pulmonary disorder. Data were collected in 2016 and analyzed 2017–2018.