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Automated Case Cancellation Review System Improves Systems-Based Practice.

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An automated system significantly increased anesthesiology residents' review of cancelled surgery cases, enhancing their education in systems-based practice and practice-based learning. This improved resident training and clinical practice.

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Area of Science:

  • Medical Education
  • Anesthesiology
  • Healthcare Systems

Background:

  • Assessing Accreditation Council for Graduate Medical Education (ACGME) core competencies like systems-based practice is challenging due to their indirect measurability.
  • Reviewing day-of-surgery cancellations offers potential learning opportunities for residents in these ACGME competencies.
  • An automated system was developed to aid anesthesiology residents in reviewing cancelled surgical cases.

Purpose of the Study:

  • To evaluate the impact of an automated system on anesthesiology resident education during the Preoperative Evaluation Clinic (PEC) rotation.
  • To assess changes in resident engagement with and perception of cancelled case reviews post-implementation.

Main Methods:

  • A survey was administered to residents on the PEC rotation before and after the implementation of an automated cancelled case review system.
  • The study compared resident experiences and perceptions during the 6 months preceding and following the system's implementation in 2014.
  • Data analysis focused on changes in the number, perceived importance, and ease of reviewing cancelled cases.

Main Results:

  • Significant increases were observed in the number of cases reviewed per resident (p < 0.0001), perceived importance (p = 0.03), and ease of review (p = 0.03).
  • The proportion of reviewed cancelled cases rose dramatically from 17.3% to 95.6% (p < 0.0001).
  • While trends were non-significant, residents perceived a positive effect on ACGME competencies, and clinical practice improvements, such as standardized guidelines, emerged from these reviews.

Conclusions:

  • Automated systems enhance compliance with educational objectives by clarifying priorities and streamlining workflows.
  • The implemented system successfully increased resident case reviews and their perceived educational value.
  • This approach demonstrates the effectiveness of automated tools in improving resident training and clinical practice in anesthesiology.