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To develop and prospectively evaluate a web-based tool that forecasts the daily bed need for admissions from the cardiac catheterization laboratory using routinely available clinical data within electronic medical records (EMRs).

Understand barriers to the use of personal health data (PHD) in research from the perspective of three stakeholder groups: early adopter individuals who track data about their health, researchers who may use PHD as part of their research, and companies that market self-tracking devices, apps or services, and aggregate and manage the data that are generated.

Hospitals are challenged to provide timely patient care while maintaining high resource utilization. This has prompted hospital initiatives to increase patient flow and minimize nonvalue added care time. Real-time demand capacity management (RTDC) is one such initiative whereby clinicians convene each morning to predict patients able to leave the same day and prioritize their remaining tasks for early discharge. Our objective is to automate and improve these discharge predictions by [...]

To identify patients in a human immunodeficiency virus (HIV) study cohort who have fallen by applying supervised machine learning methods to radiology reports of the cohort.

Stage 2 and proposed Stage 3 meaningful use criteria ask providers to support patient care coordination by electronically generating, exchanging, and reconciling key information during patient care transitions.

Antimicrobial resistance is a growing, global public health crisis, due in large part to the overuse and misuse of antibiotics. Understanding medication allergy data and allergy reactions that are documented in electronic health records (EHRs) can help to identify opportunities to improve the quality of documentation of beta-lactam allergies, thus potentially reducing the prescribing of alternative antibiotics.

Variation in the use of tests and treatments has been demonstrated to be substantial between providers and geographic regions. This study assessed variation between outpatient providers in overriding electronic prescribing warnings.

Accurate food adverse sensitivity documentation in electronic health records (EHRs) is crucial to patient safety. This study examined, encoded, and grouped foods that caused any adverse sensitivity in a large allergy repository using natural language processing and standard terminologies.

Electronic medical record (EMR) databases offer significant potential for developing clinical hypotheses and identifying disease risk associations by fitting statistical models that capture the relationship between a binary response variable and a set of predictor variables that represent clinical, phenotypical, and demographic data for the patient. However, EMR response data may be error prone for a variety of reasons. Performing a manual chart review to validate data accuracy is time [...]

Antibiotic computerized decision support systems (CDSSs) were developed to guide antibiotic decisions, yet prescriptions of CDSS-recommended antibiotics have remained low. Our aim was to identify predictors of patients' receipt of empiric antibiotic therapies recommended by a CDSS when the prescribing physician had an initial preference for using broad-spectrum antibiotics.