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Maternity patients interact with the healthcare system over an approximately ten-month interval, requiring multiple visits, acquiring pregnancy-specific education, and sharing health information among providers. Many features of a web-based patient portal could help pregnant women manage their interactions with the healthcare system; however, it is unclear whether pregnant women in safety-net settings have the resources, skills or interest required for portal adoption.

Medication order voiding allows clinicians to indicate that an existing order was placed in error. We explored whether the order voiding function could be used to record and study medication ordering errors.

To develop automated classification methods for eligibility criteria in ClinicalTrials.gov to facilitate patient-trial matching for specific populations such as persons living with HIV or pregnant women.

The International Classification of Health Interventions, currently being developed, seeks to span all sectors of the health system. Our objective was to test the draft classification's coverage of interventions commonly delivered by nurses, and propose changes to improve the utility and reliability of the classification for aggregating and analyzing data on nursing interventions.

Social media is an important pharmacovigilance data source for adverse drug reaction (ADR) identification. Human review of social media data is infeasible due to data quantity, thus natural language processing techniques are necessary. Social media includes informal vocabulary and irregular grammar, which challenge natural language processing methods. Our objective is to develop a scalable, deep-learning approach that exceeds state-of-the-art ADR detection performance in social media.

To evaluate the impact of clinical decision support (CDS) tools on rates of vitamin D testing. Screening for vitamin D deficiency has increased in recent years, spurred by studies suggesting vitamin D's clinical benefits. Such screening, however, is often unsupported by evidence and can incur unnecessary costs.

To better understand clinician information needs and learning opportunities by exploring the use of best-practice algorithms across different training levels and specialties.

Comorbidity adjustment is often performed during outcomes and health care resource utilization research. Our goal was to develop an efficient algorithm in structured query language (SQL) to determine the Elixhauser comorbidity index.

To investigate time differences in recording observations and an early warning score using traditional paper charts and a novel e-Obs system in clinical practice.