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The study sought to develop a comprehensive and current description of what Clinical Informatics Subspecialty (CIS) physician diplomates do and what they need to know.

The objective of this study is to demonstrate the feasibility of applying word embeddings to expand the terminology of dietary supplements (DS) using over 26 million clinical notes.

Chronic obstructive pulmonary disease (COPD) phenotypes cover a range of lung abnormalities. To allow text mining methods to identify pertinent and potentially complex information about these phenotypes from textual data, we have developed a novel annotated corpus, which we use to train a neural network-based named entity recognizer to detect fine-grained COPD phenotypic information.

We present a new system to track, manage, and report on all risks and issues encountered during a clinical trial.

"Meaningful Use" (MU) of electronic health records (EHRs) is a measure used by Medicare to determine whether hospitals are comprehensively using electronic tools. Whether hospitals' engagement in value-based initiatives such as MU is associated with value-defined as high quality and low costs-is unknown. Our objectives were to describe hospital participation in MU, and determine whether duration of participation is associated with value.

The collection and use of a family health history are important for assessing the patient's risk of disease, but history taking is often impeded by practical barriers in the office. Provision for patient-computer dialogue, linked with the electronic health record, may enable patients to contribute their history while bypassing these barriers. We sought to assess the patient experience using such a tool.

We aim to evaluate the effectiveness of advanced deep learning models (eg, capsule network [CapNet], adversarial training [ADV]) for single-domain and multidomain relation extraction from electronic health record (EHR) notes.

In the context of patient broad consent for future research uses of their identifiable health record data, we compare the effectiveness of interactive trust-enhanced e-consent, interactive-only e-consent, and standard e-consent (no interactivity, no trust enhancement).