Development of an algorithm to link electronic health record prescriptions with pharmacy dispense claims.

Objective: Medication adherence is an important aspect of chronic disease management. Electronic health record (EHR) data are often not linked to dispensing data, limiting clinicians' understanding of which of their patients fill their medications, and how to tailor care appropriately. We aimed to develop an algorithm to link EHR prescribing to claims-based dispensing data and use the results to quantify how often patients with diabetes filled prescribed chronic disease medications. Materials and Methods: We developed an algorithm linking EHR prescribing data (RxNorm terminology) to claims-based dispensing data (NDC terminology), within sample of adult (19-64) community health center (CHC) patients with diabetes from a network of CHCs across 12 states. We demonstrate an application of the method by calculating dispense rates for a set of commonly prescribed diabetes and cardio-protective medications. To further inform clinical care, we computed adjusted odds ratios of dispense by patient-, encounter-, and clinic-level characteristics. Results: Seventy-six percent of cardio-protective medication prescriptions and 74% of diabetes medications were linked to a dispensing record. Age, income, ethnicity, insurance, assigned primary care provider, comorbidity, time on EHR, and clinic size were significantly associated with odds of dispensing. Discussion: EHR prescriptions and pharmacy dispense data can be linked at the record level across different terminologies. Dispensing rates in this low-income population with diabetes were similar to other populations. Conclusion: Record linkage resulted in the finding that CHC patients with diabetes largely had their chronic disease medications dispensed. Understanding factors associated with dispensing rates highlight barriers and opportunities for optimal disease management.

Interviews with Patients and Providers on Transgender and Gender Nonconforming Health Data Collection in the Electronic Health Record.

Purpose: Meaningful use (MU) and Uniform Data Systems (UDSs) are calling for the collection of gender identity (GI) in electronic health record (EHR) systems; however, many transgender and nonconforming (TGNC) patients may not feel safe disclosing their GI and the data collection is not designed to guide care provision. This study explores the complexities surrounding the inclusion of GI in EHR data collection and how it can best serve patients and providers. Methods: Using a semistructured interview format, TGNC patients (n=7) and providers (n=5) who care for TGNC patients were asked about data collection procedures and the use of these data within community health centers in Oregon. Using a constant comparative data analysis methodology, interview transcripts were coded for emergent concepts until overlapping themes were identified. Results: Both patients and providers expressed a need for the EHR to expand upon MU and UDS-recommended fields to include current pronouns and name and gender identifiers in a forward-facing display to prevent misgendering by clinic staff and providers. Furthermore, they both cited the need for a broader range of birth-assigned sex and gender options. TGNC patients and providers disagreed on the scope of health information to be collected as well as who should be tasked with the data collection. Conclusion: These interviews offer us a glimpse into the structural difficulties of creating an EHR system that serves the needs of clinicians while providing safe and culturally competent care to TGNC patients.

Protocol for the analysis of a natural experiment on the impact of the Affordable Care Act on diabetes care in community health centers.

BACKGROUND: It is hypothesized that Affordable Care Act (ACA) Medicaid expansions could substantially improve access to health insurance and healthcare services for patients at risk for diabetes mellitus (DM), with pre-DM, or already diagnosed with DM. The ACA called for every state to expand Medicaid coverage by 2014. In a 2012 legal challenge, the US Supreme Court ruled that states were not required to implement Medicaid expansions. This 'natural experiment' presents a unique opportunity to learn whether and to what extent Medicaid expansion can affect healthcare access and services for patients with DM risk, pre-DM, or DM. METHODS/DESIGN: Data from electronic health records (EHRs) from the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) clinical data research network, which has data from >700 community health centers (CHCs), was included in the study. EHR data will be linked to Oregon Medicaid claims data. Data collection will include information on changes in health insurance, service receipt, and health outcomes, spanning 9 years (pre- and post-expansion), comparing states that expanded Medicaid, and those that did not. Patients included in this study will be diagnosed with DM, be at risk for DM, or have pre-DM, between the ages of 19 and 64, with ≥1 ambulatory visit. Sample size is estimated to be roughly 275,000 patients. Biostatistical analyses will include the difference-in-differences (DID) methodology and a generalized linear mixed model. Econometric analyses will include a DID two-part method to calculate the difference in Medicaid expenditures in Oregon among newly insured CHC patients. DISCUSSION: Findings will have national relevance on DM health services and outcomes and will be shared through national conferences and publications. The findings will provide information needed to impact the policy as it is related to access to health insurance and receipt of healthcare among a vulnerable population. TRIAL REGISTRATION: This project is registered with ClinicalTrials.gov ( NCT02685384 ). Registered 18 May 2016.

Associations of retrospective and concurrent lipid levels with subclinical atherosclerosis prediction after 20 years of follow-up: the Coronary Artery Risk Development in Young Adults (CARDIA) study.

PURPOSE: Using data from the Coronary Artery Risk Development in Young Adults (CARDIA) study, we sought to determine how well lipids measured at baseline and at 20 years predict the presence of subclinical atherosclerosis. METHODS: Complete risk factor, coronary artery calcification (CAC), and carotid intima media thickness (CIMT) data were available for 2435 participants. Lipids were categorized into quartiles, CAC at Y20 was dichotomized as present/absent, and CIMT was dichotomized as ≥84 or <84th overall percentile. Multivariable logistic regression was used to model the association between lipids and CAC/CIMT. C statistics were used to assess the discriminative value of each lipid measure in predicting the presence of CAC or CIMT at Y20. RESULTS: Lipid levels measured in young adulthood as well as middle age were both associated with subclinical disease in middle age. The discriminatory value of lipids was virtually identical at baseline, when participants were 18-30 years of age, and 20 years later. Neither baseline nor Y20 lipid data were strong predictors of Y20 subclinical disease despite statistically significant associations. CONCLUSIONS: These results are consistent with a growing body of evidence that early-life exposure to nonoptimal lipids matters and lifestyle modifications administered earlier in the lifespan could slow the progress of the atherosclerotic plaques.