Development and Evaluation of Sociotechnical Metrics To Inform Health IT Adaptation
The investigators used a mixed-methods approach to incorporate quantitative and qualitative research in developing and validating a health IT adaptation survey.
The investigators used a mixed-methods approach to incorporate quantitative and qualitative research in developing and validating a health IT adaptation survey.
This research showed that automated measurement of electronic order errors can be readily integrated into electronic health records to study the epidemiology of order errors and to test the effectiveness of proposed system improvements on order error outcomes.
This project developed a natural language processing electronic health record search tool that automatically identifies and ranks relevant clinical information based on a patient’s presenting complaint within the emergency department setting.
This pilot project implemented a Social Knowledge Networking system and concluded that it supported progress toward meaningful use of medication reconciliation technology in an electronic health record.
This research created, piloted, and evaluated FIQS, the Family Input to Quality and Safety tool, that allows pediatric patients and their caregivers to provide safety reports regarding their inpatient care.
This research studied how communication technologies facilitate or hinder communication between nurses and physicians with the ultimate goal of supporting effective communication.
This research investigated how electronic health record use affected clinical workflow, efficiency, and quality of care in the emergency department, and developed recommendations for future stages of Meaningful Use.
The research team developed and evaluated a natural language processing allergy module that was used to study different types of allergies in an electronic health record.
This project evaluated SMARxT, web-based education modules designed to teach resident physicians how to effectively navigate and counteract pharmaceutical-sponsored messaging within technology.
This project created a natural language processing-enabled clinical decision support system to pull patient information and determine recommendations for cervical cancer screening, and demonstrated improvement in overall screening and surveillance rates.