Age-Related Hearing Loss Guidelines: What They Mean for You (LIVE Webinar Sponsored by Cochlear)
This webinar is sponsored by Cochlear. 
Age-related hearing loss (ARHL) is one of the most prevalent chronic conditions affecting older adults—yet gaps in diagnosis, referral, and treatment persist across the healthcare system. In this 60-minute webinar, Dr. Kevin Zhan from Northwestern Medicine, who contributed to the development of the new ARHL guidelines, will discuss these recommendations and their potential impact on clinical practice and community outreach efforts.
This session will cover:
- An introduction to the scope and impact of ARHL
- Prevalence data and the current challenges in timely referrals
- The rationale behind the development of the guidelines
- A detailed review of what the guidelines recommend
- Practical strategies for using this information to educate, enhance care and increase referrals from other healthcare providers
Whether you are fitting hearing aids or collaborating across care teams, this webinar will help you align your work with the latest standards and contribute to better outcomes for aging adults with hearing loss.
Dr. Kevin Zhan is an Assistant Professor of Otology & Neurotology and medical director of the Northwestern Medicine Cochlear Implant program. He also treats pediatric patients at Lurie Children’s Hospital. He completed his Otolaryngology residency at The Ohio State University and subsequent Otology, Neurotology & skullbase surgery fellowship at Washington University in St. Louis, both high volume CI centers. He was heavily involved in the American Academy of Otolaryngology as a trainee and served on the Section for Resident and Fellows governing council for six years, ultimately as Chair of the SRF. He has a significant passion for teaching, research, and building the skullbase surgery and cochlear implant programs at Northwestern. He was recently awarded the American Cochlear Implant Alliance Pilot Innovations grant to further investigate cochlear implant utilization and access in the US using large datasets.