Comparison of Computational Phenotyping Approaches in Identifying Patients with Dementia in the Emergency Department

Poster Number: P09
Presentation Time: 05:00 PM - 06:30 PM

Abstract Keywords: Aging in Place, Clinical Decision Support, Computational Biology, Chronic Care Management, Real-World Evidence Generation
Primary Track: Applications
Programmatic Theme: Clinical Informatics

We compared 20 computational phenotyping algorithms using electronic health record data to identify dementia in patients aged 65+ in the emergency department (ED). Results showed variability in dementia prevalence (0.3% to 8.5%) across algorithms, with a higher prevalence in older patients and females. Computational phenotyping holds promise for enhancing dementia recognition in EDs and improving care quality.

Speaker(s):
Inessa Cohen, MPH
Yale University

Author(s):
Inessa Cohen, MPH - Yale University; Richard Taylor, MD MHS - Yale University; Aidan Gilson - Yale School of Medicine; Isaac Faustino, MS - Yale University; Natalia Festa, MD, MHS - Yale University; James Lai, MD, MHS - New York University; Phillip Magidson, MD, MPH - Johns Hopkins University; Adam Mecca, MD, PhD - Yale University; Debra Tomasino, MA - NYU Grossman School of Medicine; Haipeng Xue, MS - Yale University; Ula Hwang, MD, MPH - NYU Grossman School of Medicine;