PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
May 29, 2026Journal of Clinical Oncology0 citations

Enhancing hematology-oncology education through a structured AI-based board review series: An ASCO Leadership Development Education Scholars initiative.

View Full Paper
MGManasi GhatgeMKMargaret KrackelerALAnthony Lott

Key Points

  • This initiative aims to enhance the hematology-oncology education of fellows through structured board review and peer-led learning.
  • Implemented a longitudinal, fellow-led board review curriculum with faculty mentorship.
  • Utilized weekly anonymous multiple-choice questions distributed via Microsoft Teams polls.
  • Conducted pre- and post-session assessments to evaluate knowledge acquisition.
  • 63% correct answers in pre-test assessments (126 of 200 responses) improved to 98% in post-test (196 of 200 responses), indicating a 35% increase.
  • Educational gains were observed across key oncology domains including molecular biomarkers and therapy selection.

Abstract

9028 Background: As an ASCO Leadership Development Education Scholars Initiative, structured board review is essential for hematology–oncology fellows to consolidate knowledge, support examination readiness, and strengthen clinical decision-making in an increasingly complex therapeutic landscape. Existing approaches are often fragmented and insufficiently tailored to trainee-specific knowledge gaps. To address this need, we developed a pilot, structured board review series within a single academic fellowship program integrating peer-led teaching, faculty mentorship, and anonymous question-based learning aligned with board examination content and real-world oncology practice. Methods: A longitudinal, fellow-led board review curriculum was implemented within a hematology–oncology fellowship program with subspecialty faculty mentorship to ensure clinical accuracy and guideline-based context. Fellows engaged through weekly anonymous board-style multiple-choice questions distributed via Microsoft Teams polls. Questions were curated from ASCO Self-Evaluation Program materials based on high-yield topics identified from ASCO and ASH in-training examinations, with AI-assisted topic prioritization. Objective knowledge acquisition was assessed using identical pre- and post-session questions. Each topic spanned one month with eight pre-session questions and the same eight questions administered post-session. Across five sessions, five fellows answered eight questions per session, yielding 200 learner responses for both pre- and post-intervention assessments. This initiative was conducted as an educational quality improvement project and did not meet criteria for human subjects research. Results: Educational gains were observed across multiple high-yield oncology domains, including molecular biomarker interpretation, guideline-directed therapy selection, toxicity recognition and management, and familiarity with emerging treatment modalities. In the pre-test assessment, 63% of questions were answered correctly (126 of 200 responses). Following the educational intervention, 98% of questions were answered correctly (196 of 200 responses), representing an absolute improvement of 70 correct responses (35%). Conclusions: This structured, fellow-led, faculty-supported board review initiative represents a feasible and effective model for delivering high-yield oncology education within hematology–oncology fellowship training. The curriculum demonstrated measurable knowledge gains, high trainee engagement, and successful integration of AI-informed topic selection with in-training examination data, highlighting the potential for learner-centered curricula to modernize subspecialty medical education and expand to more fellowship programs.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Ghatge et al. (2026) studied this question.

synapsesocial.com/papers/6a192e68fab5b468c44176e2https://doi.org/10.1200/jco.2026.44.16_suppl.9028
Ask AI
Helpful
Bookmark
Share
View Full Paper