The research team developed and tested algorithms that can predict postoperative adverse outcomes with a high degree of accuracy.
This research study addressed the overuse of blood cultures to diagnose sepsis by developing an electronic health record-embedded clinical decision support tool that draws upon the strengths of analytical and naturalistic decision making.
This project developed and enhanced CDS Connect, an online platform that aimed to demonstrate a systematic and replicable process for transforming evidence-based research findings, including findings from patient-centered outcomes research, into publicly available, shareable clinical decision support.
This research studied whether clinical decision support could reduce contextual errors, improve patient healthcare outcomes, and reduce misuse and overuse of medical services.
This research assessed the use of a health information exchange system in emergency department settings, finding that although overall usage is relatively low, additional functionalities such as single sign on add value to clinical decision making and enable faster retrieval of patient records from external sources compared to traditional methods when embedded into existing workflows.
This research combined the artificial intelligence technology technique Dynamic Logic with natural language processing to create a model to predict risk of death over the next 12 months and found it was better than benchmark statistical and machine learning algorithms.
Researchers created a drug allergy module that detects inconsistencies in allergy information within the electronic health record and uses a dynamic picklist that puts answers in order of how important they are based on the allergen input.
This research developed and evaluated the Prevent Diabetes Mellitus Clinical Decision Support tool and found that its use improved processes related to care.
This research developed and evaluated Translate Evidence into Action, a clinical decision support system that identifies and supports management of patients with acute respiratory distress syndrome.
This research investigated how shareable clinical decision support (CDS) resources fit into the CDS lifecycle involving CDS design, development, and deployment and assessed whether using resources through CDS Connect quantifiably improves efficiencies in the CDS lifecycle.
