BACKGROUND: Accreditation Council for Graduate Medical Education (ACGME) core competencies of systems-based practice and practice-based learning and improvement are difficult to assess, as they are often not directly measurable or observable. Reviewing day-of-surgery cancellations could provide resident learning opportunities in these areas. OBJECTIVE: An automated system to facilitate anesthesiology resident review of cancelled cases was implemented on the Preoperative Evaluation Clinic (PEC) rotation at the authors' institution. This study aims to evaluate its impact on resident education. METHODS: Residents on the PEC rotation during the 6 months preceding (n = 22) and following (n = 13) implementation in 2014 were surveyed about their experience performing cancelled case reviews in order to ascertain the effect of the intervention on their training. RESULTS: Significant changes were reported in the number of cases reviewed by each resident (p < 0.0001), perceived importance of review (p = 0.03), and ease of review (p = 0.03) after system implementation. There was also an increase in the proportion of cancelled cases reviewed from 17.3% (34 of 196) to 95.6% (194 of 203) (p < 0.0001). Non-significant trends were seen in perceived rotation effect on ACGME competencies, including systems-based practice. Several specific improvements to our clinical practice, including the creation of standardized guidelines, arose from these case reviews. CONCLUSION: Implementation of automated systems can improve compliance with educational goals by clarifying priorities and simplifying workflow. This system increased the number of cases reviewed by residents and the perceived importance of this review as a part of their educational experience.
BACKGROUND: Accreditation Council for Graduate Medical Education (ACGME) core competencies of systems-based practice and practice-based learning and improvement are difficult to assess, as they are often not directly measurable or observable. Reviewing day-of-surgery cancellations could provide resident learning opportunities in these areas. OBJECTIVE: An automated system to facilitate anesthesiology resident review of cancelled cases was implemented on the Preoperative Evaluation Clinic (PEC) rotation at the authors' institution. This study aims to evaluate its impact on resident education. METHODS: Residents on the PEC rotation during the 6 months preceding (n = 22) and following (n = 13) implementation in 2014 were surveyed about their experience performing cancelled case reviews in order to ascertain the effect of the intervention on their training. RESULTS: Significant changes were reported in the number of cases reviewed by each resident (p < 0.0001), perceived importance of review (p = 0.03), and ease of review (p = 0.03) after system implementation. There was also an increase in the proportion of cancelled cases reviewed from 17.3% (34 of 196) to 95.6% (194 of 203) (p < 0.0001). Non-significant trends were seen in perceived rotation effect on ACGME competencies, including systems-based practice. Several specific improvements to our clinical practice, including the creation of standardized guidelines, arose from these case reviews. CONCLUSION: Implementation of automated systems can improve compliance with educational goals by clarifying priorities and simplifying workflow. This system increased the number of cases reviewed by residents and the perceived importance of this review as a part of their educational experience.
Authors: Thomas S Seufert; Patricia M Mitchell; Allison R Wilcox; Julia E Rubin-Smith; Laura F White; Kerry K McCabe; Jeffrey I Schneider Journal: Acad Emerg Med Date: 2011-10 Impact factor: 3.451
Authors: Paul A Harris; Robert Taylor; Robert Thielke; Jonathon Payne; Nathaniel Gonzalez; Jose G Conde Journal: J Biomed Inform Date: 2008-09-30 Impact factor: 6.317
Authors: Jonathan P Wanderer; Thomas A Lasko; Joseph R Coco; Leslie C Fowler; Matthew D McEvoy; Xiaoke Feng; Matthew S Shotwell; Gen Li; Brian J Gelfand; Laurie L Novak; David A Owens; Daniel V Fabbri Journal: J Clin Anesth Date: 2020-11-01 Impact factor: 9.452