The U-M Medical School Department of Anesthesiology is positioned to lead a transformation of medical education through a program of innovations that leverage our unique strengths.
Both graduate and undergraduate medical education communities have embraced competency-based progression leading to independent unsupervised practice. However, most graduate medical education programs rely heavily on time-based progression. A significant barrier to implementing competency-based progression is a lack of actionable data regarding the direct clinical experience of trainees.
There is a clear opportunity to bridge this gap by leveraging existing large electronic health record (EHR) databases to improve anesthesiology graduate medical education.
ASCENT is the Department of Anesthesiology’s research enterprise focused on innovation in anesthesiology education. As a multidisciplinary enterprise, ASCENT seeks to discover new insights regarding anesthesiology education and improve educational programming through the meaningful use of individualized clinical experience data. We expect these to bring measurable improvements to both education and healthcare outcomes.
We are:
- Focused on the innovative use of large EHR databases and application of data science in anesthesiology education
- Led by experts within education, healthcare informatics, outcomes research, faculty development, and diversity leadership
- Developing a community of learners and teachers who support each other in their progress along individualized learning plans for competency-to-expertise development
INSPECT (Improvement in the Scope and Precision of Educational Cases for Trainees)
Supported by a Foundation for Anesthesia Education and Research - Research in Education Grant (FAER-REG), INSPECT is a multicenter study to investigate the hemorrhage resuscitation experience of anesthesiology residents across a variety of academic institutions. This data will then be used to optimize data visualizations of clinical experience data for residency program use.
Quantifying the Experience of Trainees: The First Steps from Incidental to Intentional Clinical Experiences
Supported by a Michigan Medicine Graduate Medical Education Innovation Grant, this pilot study seeks to determine the feasibility and utility of large EHR datasets to support medical education focusing on intraoperative hemorrhage resuscitation as a test model.
ASCENT Anesthesia Case Logs
Anesthesiology case logs are used to ensure adequate depth and breadth of experience, but self-reported case logs have demonstrated limitations in accuracy. This project created continuously updated individualized case logs labeled with Accreditation Council of Graduate Medical Education (ACGME) index case types.
Program Associate, Anesthesiology
Associate Professor of Anesthesiology
Associate Chair of Education and Professionalism, Department of Anesthesiology
Associate Professor of Obstetrics and Gynecology
