Wednesday, January 22, 2025
1/22/2025
This K24 mid-career investigator award in patient-oriented research is to support the mentoring, research, and career development activities of Dr. Tell Bennett. Dr. Bennett is an Associate Professor in the University of Colorado School of Medicine and a practicing pediatric ICU physician and informaticist/data scientist with research concentrations in predictive analytics, electronic health record (EHR) data, and clinical decision support (CDS) tool implementation. He is the Informatics Director for the Colorado Clinical and Translational Sciences Institute (CCTSI) and Vice Chair of Clinical Informatics in the Department of Biomedical Informatics. His combined leadership roles have enabled him to build a rich mentoring environment for patient-oriented informatics research. This K24 application proposes to sustain and grow that mentorship program. He currently mentors clinician-scientists in a variety of clinical fields including intensive care, pharmacy, surgery, endocrinology, malignant hematology, and clinical psychology. The K24 Mentoring Plan aligns with Dr. Bennett’s mission to grow patient-oriented research using informatics and data science methods and tools. The mentoring plan leverages educational, career development, and research support programs available through the CCTSI and the new Department. The K24 Research Plan is to develop and implement machine learning and computational physiology models deployable as EHR-based CDS tools. Dr. Bennett currently leads and mentors projects developing CDS tools in both outpatient and inpatient care settings and in a variety of clinical domains include heart failure, traumatic brain injury, serious bacterial infection and sepsis, COVID- 19, thyroid cancer, and postpartum depression. In this K24, developing CDS tools to improve decision-making and outcomes in children with acute respiratory failure (ARF) is a natural next step in this work. ARF is a common, important, and NIH-relevant condition that causes significant pediatric morbidity and mortality. The aims of the project are to 1) develop and validate a dynamic machine learning-based real-time prediction model for intubation in children with ARF and 2) phenotype current lung injury state in mechanically ventilated children using computational physiology models. These models can be deployed as CDS tools and tested as interventions in future clinical trials to improve patient outcomes. The K24 Career Development Plan includes formal mentorship training and coursework in signal processing and dynamical systems models. This group of skills will make Dr. Bennett a more successful mentor.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).