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Electronic health data may improve the timeliness and accuracy of resource-intense contact investigations (CIs) in healthcare settings.

Clinical decision support (CDS) is essential for delivery of high-quality, cost-effective, and safe healthcare. The authors sought to evaluate the CDS capabilities across electronic health record (EHR) systems.

Semantic role labeling (SRL), which extracts a shallow semantic relation representation from different surface textual forms of free text sentences, is important for understanding natural language. Few studies in SRL have been conducted in the medical domain, primarily due to lack of annotated clinical SRL corpora, which are time-consuming and costly to build. The goal of this study is to investigate domain adaptation techniques for clinical SRL leveraging resources built [...]

Evidence supports the potential for e-prescribing to reduce the incidence of adverse drug events (ADEs) in hospital-based studies, but studies in the ambulatory setting have not used occurrence of ADE as their outcome. Using the "prescription origin code" in 2011 Medicare Part D prescription drug events files, the authors investigate whether physicians who meet the meaningful use stage 2 threshold for e-prescribing (≥50% of prescriptions e-prescribed) have lower rates of [...]

The Health Insurance Portability and Accountability Act Privacy Rule enables healthcare organizations to share de-identified data via two routes. They can either 1) show re-identification risk is small (e.g., via a formal model, such as k-anonymity) with respect to an anticipated recipient or 2) apply a rule-based policy (i.e., Safe Harbor) that enumerates attributes to be altered (e.g., dates to years). The latter is often invoked because it is interpretable [...]

To improve the normalization of relative and incomplete temporal expressions (RI-TIMEXes) in clinical narratives.

Extracting medical knowledge from electronic medical records requires automated approaches to combat scalability limitations and selection biases. However, existing machine learning approaches are often regarded by clinicians as black boxes. Moreover, training data for these automated approaches at often sparsely annotated at best. The authors target unsupervised learning for modeling clinical narrative text, aiming at improving both accuracy and interpretability.

Hospital-acquired acute kidney injury (HA-AKI) is a potentially preventable cause of morbidity and mortality. Identifying high-risk patients prior to the onset of kidney injury is a key step towards AKI prevention.