Using Natural Language Processing to improve EHR Structured Data-based Surgical Site Infection Surveillance.
Jianlin Shi, Siru Liu, Liese CC Pruitt, Carolyn L Luppens, Jeffrey P Ferraro, Adi V Gundlapalli, Wendy W Chapman, Brian T Bucher
AMIA Annu Symp Proc | Published : 2019
Surgical Site Infection surveillance in healthcare systems is labor intensive and plagued by underreporting as current methodology relies heavily on manual chart review. The rapid adoption of electronic health records (EHRs) has the potential to allow the secondary use of EHR data for quality surveillance programs. This study aims to investigate the effectiveness of integrating natural language processing (NLP) outputs with structured EHR data to build machine learning models for SSI identification using real-world clinical data. We examined a set of models using structured data with and without NLP document-level, mention-level, and keyword features. The top-performing model was based on a ..View full abstract
Awarded by AHRQ HHS