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To compare 3 commercial knowledge bases (KBs) used for detection and avoidance of potential drug-drug interactions (DDIs) in clinical practice.

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 examine, using a smartphone application, whether mood is related to daily movement patterns in pregnant women at risk for perinatal depression.

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.

Nationwide initiatives have promoted greater adoption of health information technology as a means to reduce adverse drug events (ADEs). Hospital adoption of electronic health records with Meaningful Use (MU) capabilities expected to improve medication safety has grown rapidly. However, evidence that MU capabilities are associated with declines in in-hospital ADEs is lacking.

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.

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.

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.