• Maytinee Lilaonitkul, Christopher W Cosden, John C Markley, May Pian-Smith, Grace Lim, Peter Yeh, Pedram Aleshi, Christy Boscardin, Kristina Sullivan, and Ronald B George.
• Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, USA. maytinee.lilaonitkul@ucsf.edu.
• Can J Anaesth. 2023 Oct 1; 70 (10): 161116221611-1622.

PurposeThe standard for anesthesia residency training in the USA mainly relies on the Accreditation Council for Graduate Medical Education (ACGME) Outcome Project, a framework that lacks specific directives for subspecialties including obstetric anesthesia. We aimed to identify core competencies in obstetric anesthesiology that can be adapted to different residency training programs to help improve the quality of training and accountability of the institutions within the USA.MethodsWe identified a preliminary list of competencies from review of existing competency-based obstetric anesthesia training curricula and practice guidelines. We used a modified Delphi methodology to achieve expert consensus among members of the Society for Obstetric Anesthesia and Perinatology education committee. The panellists were asked to evaluate the importance of each competency using a five-point Likert scale, with consensus after two rounds defined at 80% agreement. The responders were also asked at which level of training each competency should be attained.ResultsThe Delphi rounds had 75% response rate and derived 94 competencies that were categorized under the six ACGME domains: patient care (38), medical knowledge (45), system-based practice (two), practice-based learning and improvement (five), interpersonal communication skills (two), and professionalism (two).ConclusionWe generated a residency training competency list for obstetric anesthesiology through expert consensus. This list can be used by residency training programs to develop a structured competency-based curriculum with tangible milestones, thereby reducing heterogeneity in the standard of training.© 2023. The Author(s).

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