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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.
The project team developed automated methods for identifying relevant new information versus redundant information in electronic health record clinical notes.
This project tested three types of clinical decision support alerts and found that pop-up alerts were the most effective, but were the least preferred by dental providers.
This project conducted a randomized clinical trial to evaluate the impact of two clinical decision support interventions designed to improve the quality and safety of dental care in patients with medically complex conditions.