Event date: 2/7/2017 2:00 PM - 3:30 PM Export event Alyssa Thomas / Tuesday, January 10, 2017 / Categories: EHR Implementation, HIT Events Improving Health IT Safety through the Use of Natural Language Processing to Improve Accuracy of EHR An AHRQ Web Conference Web Conference on Improving Health IT Safety through the Use Natural Language Processing to Improve Accuracy of EHR Documentation Objectives: Discuss the development and evaluation of an enhanced electronic note system that leverages voice recognition and NLP technologies to create electronic physician notes in the EHR. Discuss the challenges of introducing speech recognition technology into existing medical culture and current clinician workflow, including user preferences and the quality of documents generated by this technology. Explain the need for an automated error detection system using NLP for improving the accuracy and quality of speech recognition generated medical documents, and discuss the development and evaluation of such a system. Presenters: Thomas Payne, M.D., Professor of Medicine; Adjunct Professor, Departments of Health Services and Biomedical Informatics and Medical Education; Medical Director, Information Technology Services, University of Washington Li Zhou, M.D., Ph.D., Assistant Professor of Medicine, Harvard Medical School, Brigham and Women's Hospital Moderator: Chris Dymek, Ed.D., Director, Health IT Division, Agency for Healthcare Research and Quality Previous Article HITEQ Highlights: Using Health Information Technology to Enhance Opioid Use Disorder Treatment Next Article NextGen and Ryan White HIV/AIDS Program Data Management Print 8306 Tags: EHRHealth ITAHRQelectronic health recordsnatural language processing Resource Links Link to the recorded web conference Link to the web conference slides Link to the web conference Q&A Related Resources AI Fundamentals and Applications in Primary Care Live Webinar Sensitive Information and the Electronic Patient Record Health IT Optimization for Effective PrEP Services ONC & CDC Integration Framework Lessons Learned in Social Need Screening