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Author-centric analyses of fast-growing biomedical reference databases are challenging due to author ambiguity. This problem has been mainly addressed through author disambiguation using supervised machine-learning algorithms. Such algorithms, however, require adequately designed gold standards that reflect the reference database properly. In this study we used MEDLINE to build the first unbiased gold standard in a reference database and improve over the existing state of the art in author disambiguation.

Patient portal functionalities, such as patient-physician e-communication, can benefit patients by improving clinical outcomes. Utilization has historically been low but may have increased in recent years due to the implementation of Stage 2 Meaningful Use for electronic health records. This study has 2 objectives: 1) to compare patient portal utilization rates before Stage 2 (2011-2013) and after Stage 2 (2014-2017), and 2) to examine whether disparities in patient portal utilization [...]

Despite increased use of electronic health records (EHRs), the clinical impact of system downtime is unknown.

The study sought to examine whether provider encouragement is associated with improvements in engaging patients with their healthcare processes using online portals.

The study sought to determine availability and use of structured override reasons for drug-drug interaction (DDI) alerts in electronic health records.

The testing of informatics tools designed for use during mass casualty incidents presents a unique problem as there is no readily available population of victims or identical exposure setting. The purpose of this article is to describe the process of designing, planning, and executing a functional exercise to accomplish the research objective of validating an informatics tool specifically designed to identify and triage victims of irritant gas syndrome agents.

We sought to assess the quality of race and ethnicity information in observational health databases, including electronic health records (EHRs), and to propose patient self-recording as an improvement strategy.

The study sought to explore online health communities (OHCs) for sexual minority women (SMW) with cancer by conducting computational text analysis on posts.

We aimed to address deficiencies in structured electronic health record (EHR) data for race and ethnicity by identifying black and Hispanic patients from unstructured clinical notes and assessing differences between patients with or without structured race/ethnicity data.