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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.

Most healthcare providers are reluctant to use health apps for healthcare because there is no rigorous way of choosing the best app for their patient or consumer. Accordingly, we developed a new method of app selection that fully considers target users' needs. This study verified whether health apps selected based on target users' needs can influence health-related factors.

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 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.

Adoption of health information technology (HIT) is often assessed in surveys of organizations. The validity of data from such surveys for ambulatory clinics has not been evaluated. We compared level of agreement between 1 ambulatory statewide survey and 2 other data sources: a second survey and interviews with survey respondents.

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.

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.