Overcoming Systems Factors in Case Logging with Artificial Intelligence Tools

Ruchi Thanawala, Jonathan Jesneck, Julia Shelton, Rebecca Rhee, Neal E. Seymour

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction: Case logs are foundational data in surgical education, yet cases are consistently under-reported. Logging behavior is driven by multiple human and systems factors, including time constraints, ease of case data retrieval, access to data-entry tools, and procedural code decision tools. Methods: We examined case logging trends at three mid-sized, general surgery training programs from September 2016-October 2020, January 2019-October 2020 and May 2019-October 2020, respectively. Across the programs we compared the number of cases logged per week when residents logged directly to ACGME versus via a resident education platform with machine learning-based case logging assistance tools. We examined case logging patterns across 4 consecutive phases: baseline default ACGME logging prior to platform access (P0 “Manual”), full platform logging assistance (P1 “Assisted”), partial platform assistance requiring manual data entry without data integrations (P2 “Notebook”), and resumed fully integrated platform with logging assistance (P3 “Resumed”). Results: 31,385 cases were logged utilizing the platform since 2016 by 171 residents across the 3 programs.Intelligent case logging assistance significantly increased case logging rates, from 1.44 ± 1.48 cases by manual entry in P0 to 4.77 ± 2.45 cases per resident per week via the platform in P1 (p-value < 0.00001). Despite the burden of manual data entry when the platform's data connectivity was paused, the tool helped to increase overall case logging into ACGME to 2.85 ± 2.37 cases per week (p-value = 0.0002). Upon resuming the data connectivity, case logging levels rose to 4.54 ± 3.33 cases per week via the platform, equivalent to P1 levels (insignificant difference, p-value = 0.57). Conclusions: Mapping the influence of systems and human factors in high-quality case logs allows us to target interventions to continually improve the training of surgical residents. System level factors such as access to alternate automation-drive tools and operative schedule integrated platforms to assist in ACGME case log has a significant impact on the number of cases captured in logs.

Original languageEnglish (US)
JournalJournal of Surgical Education
DOIs
StateAccepted/In press - 2022
Externally publishedYes

Keywords

  • ACGME operative logs
  • Artificial Intelligence
  • Machine Learning
  • Quantitative Data in Medical Education
  • Reinforcement Learning
  • Surgical education

ASJC Scopus subject areas

  • Surgery
  • Education

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