Disabilities and Generative Artificial Intelligence Education in Rheumatology Fellowship: Educational Module and Simulation Exercise.

Journal: Arthritis care & research
Published Date:

Abstract

OBJECTIVE: People with rheumatic and musculoskeletal diseases (RMDs) can acquire disabilities that affect their participation in work and school. Generative artificial intelligence (genAI) may reduce documentation time, providing a tool for providers to efficiently write letters for accommodation and maximally advocate for people with disabilities. Therefore, fellows-in-training (FITs) must develop skills in disability advocacy and genAI for clinical care. We created an online module and simulation activity in which FITs wrote letters for accommodation using genAI. We aimed to improve FITs' confidence implementing these skills and to identify areas needing further instruction. METHODS: FITs completed an online module and simulation activity engineering genAI prompts for letters for accommodation. Educators scored FITs' skills against a rubric. FITs measured their confidence conducting these skills in retrospective pre-post Likert scales that we compared with Wilcoxon signed rank tests. RESULTS: Twenty-three FITs participated; 18 FITs (78.6%) completed the Likert scales. On average, FITs scored 83.83% against the rubric. The strongest skill was engineering genAI prompts (95.45%), and the lowest was differentiating the scope of practice between rheumatology providers and disabilities professionals coordinating accommodations (74.55%). FITs' confidence significantly improved across all objectives, most notably in engineering genAI prompts and finalizing letters drafted with genAI (P < 0.0001). CONCLUSION: Our educational materials teach FITs to incorporate genAI into writing letters for accommodation and addressing disparities from medical disabilities. Such skill development prepares FITs to enter the rheumatology workforce confident in their abilities to integrate genAI into clinical activities and deliver care to people with disabilities from RMDs.

Authors

  • Sana G Cheema
    Washington University in St. Louis School of Medicine, St. Louis, MO.
  • Matthew A Sullivan
    Washington University in St. Louis School of Medicine, St. Louis, MO.
  • Emily Balczewski
    University of Michigan Medical School, Ann Arbor, MI.
  • Amanda S Alexander
    University of Alabama at Birmingham Marnix E. Heersink School of Medicine, Birmingham, AL.
  • Anisha B Dua
    Northwestern University Feinberg School of Medicine, Chicago, IL.
  • Lacey Feigl-Lenzen
    Washington University in St. Louis School of Medicine, St. Louis, MO.
  • Brian Jaros
    Northwestern University Feinberg School of Medicine, Chicago, IL.
  • Jason Kolfenbach
    University of Colorado Anschutz School of Medicine, Aurora, CO.
  • Nicholas Kortan
    University of Colorado Anschutz School of Medicine, Aurora, CO.
  • Lisa Zickuhr
    Washington University in St. Louis School of Medicine, St. Louis, MO.

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