Skip to main content
AAO-HNSF 2026 Annual Meeting & OTO EXPO Main banner
Icon Legend
This session is not in your schedule.
This session is in your schedule. Click again to remove it.

Panel Presentation

Care Equity, Quality, and Safety

Reg-ent: From PDF to Point-of-care - Ai-extracted Audiograms Enabling Practical Identification of Cochlear Implant Candidates

Sunday, October 18, 2026
11:00 AM - 12:00 PM PT
Location: West Exhibit Hall A, Learning Zone 2

    Moderator(s)

  • Jocelyn S. Meyer, MPH

    Vice President, Research & Quality Executive Director, Reg-ent Registry
    American Academy of Otolaryngology - Head and Neck Surgery Foundation
    Alexandria, VA, United States

    • Panelist(s)

  • Matthew L. Bush, M.D., Ph.D., MBA, FACS

    Professor and Chair Department of Otolaryngology – Head and Neck Surgery
    University of Kentucky Medical Center
    Lexington, KY, United States

  • KS

    Kristine Schulz, DrPH, MPH

    Chief Research Officer
    AAO-HNSF, United States

  • SK

    Sam Kim, BS

    Senior Data Scientist
    AAO-HNSF
    Chevy Chase, Maryland, United States

Disclosure(s):
Jocelyn S. Meyer, MPH:

faculty for this accredited education activity has no relationship(s) with ineligible companies to disclose.

This session highlights Reg-ent innovation that uses AI to transform audiogram PDFs/images into discrete, queryable data. Attendees will learn how derived pure tone averages and word recognition scores can be applied to identify patients who may warrant cochlear implant evaluation and how a participant-facing tool supports review, workflow tracking, and follow-up across the referral pathway. The presentation will share practical implementation considerations, real-world registry value for participating practices, and early impact on improving care coordination and reducing missed or delayed evaluations. The session will close with a forward-looking discussion to spur member ideas for additional specialty-wide applications of data to improve outcomes, access, and health equity in otolaryngology.

Learning Objectives:

  • Upon completion, participants will be able to identify 2 ways to use structured Reg-ent data beyond MIPS—using an AI audiogram use case—to support CI candidate identification, care coordination, and inspire member innovation to improve outcomes, access, and equity.
  • Upon completion, participants will be able to describe how Reg-ent uses AI to extract structured, discrete data from audiograms and other reports that were previously untapped data resources.
  • Upon completion, participants will be able to explain how Reg-ent enables real-world evidence generation and quality improvement efforts that improve clinical outcomes.