Systematic Review of AI-Driven EMR Optimization Strategies to Enhance Clinical Efficiency in Otolaryngology Practice

Monday, October 13, 2025

2:36 PM - 2:42 PM EDT

Location: 109-110

Disclosure(s):

Yara H. Hamdi:

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

Mohamed S. Sorour, MBBS:

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

Introduction: Otolaryngology practice demands precise documentation and seamless integration of clinical workflows, often hindered by time-intensive EMR systems. Recent advancements in AI-driven tools present opportunities for enhancing EMR functionality, reducing clinician burden, and improving patient outcomes. However, a comprehensive review of these strategies and their efficacy remains unexplored.

Methods: We conducted a systematic review following PRISMA guidelines, using R and Python for data extraction, synthesis, and visualization. Database searches in PubMed, Scopus, and Embase identified studies published between 2000 and 2024. Inclusion criteria focused on AI-driven EMR optimization strategies in otolaryngology or related specialties. Extracted data included study design, AI methodologies, clinical efficiency outcomes, and implementation challenges. A meta-analysis was performed where applicable, with subgroup analyses for AI techniques and practice settings.

Results: A total of 57 studies met the inclusion criteria. AI applications included predictive analytics, natural language processing, and automated workflow tools. The meta-analysis revealed a significant reduction in documentation time (average 23%, p< 0.01) and increased physician satisfaction scores (by 18%, p=0.03). Subgroup analyses highlighted improved outcomes in practices utilizing voice-to-text AI systems and automated coding tools. Key challenges included data interoperability and clinician resistance during implementation.

Conclusions: AI-driven EMR optimization significantly enhances clinical efficiency in otolaryngology. While these tools reduce administrative burdens and improve user experience, targeted strategies addressing implementation barriers are essential. This review provides actionable insights for integrating AI technologies in otolaryngology practices.

Presenting Author(s)

Yara Hamdi, Mrs

Undergraduate student
Badr University in Cairo
Houston , Texas, United States

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

Senior Author(s)

ME

Mahmoud M. Elsayed, MD

Neurosurgery Resident and Clinical Researcher
MME Foundation
Mansoura, Egypt

Disclosure information not submitted.

Co-Author(s)

Yara Hamdi, Mrs

Undergraduate student
Badr University in Cairo
Houston , Texas, United States

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

Zeina Sayed, n/a

Newgiza University, United States

Disclosure information not submitted.
FH

Fatma Hussein Raafat

Medical intern
Newgiza University
6th of October , Al Jizah, Egypt

Disclosure information not submitted.
MY

Mennatullah Yasser, MBBS

Medical intern (House officer)
Newgiza University
Cairo, Al Qahirah, Egypt

Disclosure information not submitted.
YM

Yara Mohamed, MBBS

Medical student
Newgiza University
Rehab city, Al Jizah, Egypt

Disclosure information not submitted.
KE

Kenzy Elessawy, n/a

Newgiza University, United States

Disclosure information not submitted.
AE

Ahmed Aly Elsayed, PhD

Professor.
Alexandria university.
ALXENDRIA, Egypt

Disclosure information not submitted.
AS

Aisha Shariff, n/a

Newgiza University, United States

Disclosure information not submitted.
Mohamed S. Sorour, MBBS

Medical Student
Newgiza University
Zagazig, Ash Sharqiyah, Egypt

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