Interactive visualization tool to understand and monitor health disparities in diabetes care and outcomes.

Objective: Type 2 diabetes (T2DM) poses a significant public health challenge, with pronounced disparities in control and outcomes. Social determinants of health (SDoH) significantly contribute to these disparities, affecting healthcare access, neighborhood environments, and social context. We discuss the design, development, and use of an innovative web-based application integrating real-world data (electronic health record and geospatial files), to enhance comprehension of the impact of SDoH on T2 DM health disparities.

Leveraging Student Volunteers to Connect Patients with Social Risk to Resources On a Coordinated Care Platform: A Case Study with Two Endocrinology Clinics.

Introduction: Although unmet social needs can impact health outcomes, health systems often lack the capacity to fully address these needs. Our study describes a model that organized student volunteers as a community-based organisation (CBO) to serve as a social referral hub on a coordinated social care platform, NCCARE360.

Description: Patients at two endocrinology clinics were systematically screened for social needs.

Increased Clinician Time Using Electronic Health Records During COVID-19 Pandemic.

The COVID-19 pandemic challenged how healthcare systems provided care in socially distanced formats. We hypothesized that the COVID-19 era changes in clinical care delivery models contributed to increased Electronic Health Record (EHR) related work. To evaluate the changes in time and volume metrics of EHR usage, we segregated EHR audit log metric data into PreCOVID2019 March/April/May, initial COVID2020 March/April/May, and late COVID2021 March/April/May for 1262 physician providers.

Implementation of Changes to Medical Student Documentation at Duke University Health System: Balancing Education With Service.

Purpose: When the Centers for Medicare and Medicaid Services (CMS) changed policies about medical student documentation, students with proper supervision may now document their history, physical exam, and medical decision making in the electronic health record (EHR) for billable encounters. Since documentation is a core entrustable professional activity for medical students, the authors sought to evaluate student opportunities for documentation and feedback across and between clerkships.

Method: In February 2018, a multidisciplinary workgroup was formed to implement student documentation at Duke University Health System, including educating trainees and supervisors, tracking EHR usage, and enforcing CMS compliance.

Assessing electronic health record phenotypes against gold-standard diagnostic criteria for diabetes mellitus.

Objective: We assessed the sensitivity and specificity of 8 electronic health record (EHR)-based phenotypes for diabetes mellitus against gold-standard American Diabetes Association (ADA) diagnostic criteria via chart review by clinical experts.

Materials And Methods: We identified EHR-based diabetes phenotype definitions that were developed for various purposes by a variety of users, including academic medical centers, Medicare, the New York City Health Department, and pharmacy benefit managers. We applied these definitions to a sample of 173 503 patients with records in the Duke Health System Enterprise Data Warehouse and at least 1 visit over a 5-year period (2007-2011).

A natural language processing algorithm to define a venous thromboembolism phenotype.

Deep venous thrombosis and pulmonary embolism are diseases associated with significant morbidity and mortality. Known risk factors are attributed for only slight majority of venous thromboembolic disease (VTE) with the remainder of risk presumably related to unidentified genetic factors. We designed a general purpose Natural Language (NLP) algorithm to retrospectively capture both acute and historical cases of thromboembolic disease in a de-identified electronic health record.

Applying active learning to high-throughput phenotyping algorithms for electronic health records data.

Objectives: Generalizable, high-throughput phenotyping methods based on supervised machine learning (ML) algorithms could significantly accelerate the use of electronic health records data for clinical and translational research. However, they often require large numbers of annotated samples, which are costly and time-consuming to review. We investigated the use of active learning (AL) in ML-based phenotyping algorithms.

Comparative analysis of pharmacovigilance methods in the detection of adverse drug reactions using electronic medical records.

Objective: Medication  safety requires that each drug be monitored throughout its market life as early detection of adverse drug reactions (ADRs) can lead to alerts that prevent patient harm. Recently, electronic medical records (EMRs) have emerged as a valuable resource for pharmacovigilance. This study examines the use of retrospective medication orders and inpatient laboratory results documented in the EMR to identify ADRs.

Return of individual research results from genome-wide association studies: experience of the Electronic Medical Records and Genomics (eMERGE) Network.

Purpose: Return of individual genetic results to research participants, including participants in archives and biorepositories, is receiving increased attention. However, few groups have deliberated on specific results or weighed deliberations against relevant local contextual factors.

Methods: The Electronic Medical Records and Genomics (eMERGE) Network, which includes five biorepositories conducting genome-wide association studies, convened a return of results oversight committee to identify potentially returnable results.