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To assess information sources that may elucidate errors related to radiologic diagnostic imaging, quantify the incidence of potential safety events from each source, and quantify the number of steps involved from diagnostic imaging chain and socio-technical factors.

To illustrate the need for wider implementation of the CancelRx message by quantifying and characterizing the inappropriate usage of new electronic prescription (NewRx) messages for communicating discontinuation instructions to pharmacies.

Clinical vocabularies allow for standard representation of clinical concepts, and can also contain knowledge structures, such as hierarchy, that facilitate the creation of maintainable and accurate clinical decision support (CDS). A key architectural feature of clinical hierarchies is how they handle parent-child relationships - specifically whether hierarchies are strict hierarchies (allowing a single parent per concept) or polyhierarchies (allowing multiple parents per concept). These structures handle subsumption relationships (ie, ancestor [...]

Development and maintenance of order sets is a knowledge-intensive task for off-the-shelf machine-learning algorithms alone. We hypothesize that integrating clinical knowledge with machine learning can facilitate effective development and maintenance of order sets while promoting best practices in ordering. To this end, we simulated the revision of an "AM Lab Order Set" under 6 revision approaches. Revisions included changes in the order set content or default settings through 1) population [...]

Online platforms have created a variety of opportunities for breast patients to discuss their hormonal therapy, a long-term adjuvant treatment to reduce the chance of breast cancer occurrence and mortality. The goal of this investigation is to ascertain the extent to which the messages breast cancer patients communicated through an online portal can indicate their potential for discontinuing hormonal therapy.

Globally, 36% of deaths among children can be attributed to environmental factors. However, no comprehensive list of environmental exposures exists. We seek to address this gap by developing a literature-mining algorithm to catalog prenatal environmental exposures.

Develop an approach, One-class-at-a-time, for triaging psychiatric patients using machine learning on textual patient records. Our approach aims to automate the triaging process and reduce expert effort while providing high classification reliability.

Insulin therapy, medical nutrition therapy, and physical activity are required for the treatment of type 1 diabetes (T1D). There is a lack of studies in real-life environments that characterize patient-reported data from logs, activity trackers, and medical devices (e.g., glucose sensors) in the context of exercise.

Appropriate management of attention-deficit/hyperactivity disorder (ADHD) involves parents, clinicians, and teachers. Fragmentation of interventions between different settings can lead to suboptimal care and outcomes. Electronic systems can bridge gaps across settings. Our institution developed an email-based software to collect ADHD information from parents and teachers, which delivered data directly to the clinician within the electronic health record (EHR).