This technology is an artificial intelligence software that extracts clinician expertise from nurse documentation to predict clinical decisions.
In hospital settings, early identification of patients at risk for deterioration could prevent avoidable adverse outcomes, such as mortality and sepsis. Clinical decision support systems and patient surveillance algorithms can assist clinicians in diagnostics, patient monitoring, and treatment pathways to identify at-risk patients. However, these systems typically rely heavily on physiologic factors, such as labs and vital signs, that are often late indicators of deterioration. Current systems can also lead to alert fatigue, have inefficient processing times, and provide biased outputs.
This software is a machine-learning-based predictive model that utilizes clinician documentation along with patient clinical, physiological, and contextual data to predict the patient's clinical trajectory and provide care recommendations. This approach, called “Communicating Narrative Concerns Entered by Registered Nurses” (CONCERN), harnesses clinician expertise in understanding subtle, yet observable clinical changes that are not well-captured in physiological data and electronic health records, which may lead to earlier identification of deterioration compared to existing metrics. This technology also incorporates bias mitigation for equitable care and includes a front-facing app for clinicians, patients, and caregivers to track patient progress.
This technology was validated in a multi-site clinical trial, demonstrating that the software decreased patient mortality, sepsis, length of stay, and unanticipated transfers to the ICU.
Sarah Collins Rosetti, R.N., Ph.D., FACMI, FAMIA
Patent Pending (US20250087365)
IR CU2480, CU24273, CU24275
Licensing Contact: Kristin Neuman
