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De-identification is a fundamental task in electronic health records to remove protected health information entities. Deep learning models have proven to be promising tools to automate de-identification processes. However, when the target domain (where the model is applied) is different from the source domain (where the model is trained), the model often suffers a significant performance drop, commonly referred to as domain adaptation issue. In de-identification, domain adaptation issues can [...]

To explore Veterans Health Administration clinicians' perspectives on the idea of redesigning electronic consultation (e-consult) delivery in line with a hub-and-spoke (centralized) model.

Substance use screening in adolescence is unstandardized and often documented in clinical notes, rather than in structured electronic health records (EHRs). The objective of this study was to integrate logic rules with state-of-the-art natural language processing (NLP) and machine learning technologies to detect substance use information from both structured and unstructured EHR data.

Outcomes mentioned on online health communities (OHCs) by patients can serve as a source of evidence for off-label drug usage evaluation, but identifying these outcomes manually is tedious work. We have built a natural language processing model to identify off-label usage of drugs mentioned in these patient posts.

The study sought to conduct a systematic review to explore the functions utilized by electronic cancer survivorship care planning interventions and assess their effects on patient and provider outcomes.

The study sought to investigate whether consistent use of the Veterans Health Administration's My HealtheVet (MHV) online patient portal is associated with improvement in diabetes-related physiological measures among new portal users.

We conduct a first large-scale analysis of mobile health (mHealth) apps available on Google Play with the goal of providing a comprehensive view of mHealth apps' security features and gauging the associated risks for mHealth users and their data.

Clinical notes contain an abundance of important, but not-readily accessible, information about patients. Systems that automatically extract this information rely on large amounts of training data of which there exists limited resources to create. Furthermore, they are developed disjointly, meaning that no information can be shared among task-specific systems. This bottleneck unnecessarily complicates practical application, reduces the performance capabilities of each individual solution, and associates the engineering debt of managing [...]

We investigated the progression of healthcare cybersecurity over 2014-2019 as measured by external risk ratings. We further examined the relationship between hospital data breaches and cybersecurity ratings.