AHRQ Webinar Will Outline How Natural Language Processing Can Improve the Safety of Health Information Technology

Registration is open for an AHRQ webinar Feb. 7, from 2 to 3:30 p.m. ET, to highlight tools and methods for improving the accuracy and safety of electronic health records (EHRs) through the use of natural language processing. Natural language processing is a field of science that uses computer software and algorithms to understand human language and then analyze the language information. This technology is used in health care to automate medical coding or correct speech recognition errors. On this Webinar, presenters will discuss research findings for a voice-generated, enhanced electronic note-taking system and describe how the use of natural language processing technologies are being used to improve the accuracy and timeliness of EHR documentation. Participants may earn 1.5 hours of CME/CE credits at no cost.

AHRQ News Now: hospital affiliations and costs, long-term uninsured, natural language processing

Event status:	Not started (Register)
Date and time:	Tuesday, February 7, 2017 2:00 pm Eastern Standard Time (New York, GMT-05:00) Change time zone
	Tuesday, February 7, 2017 1:00 pm Central Standard Time (Chicago, GMT-06:00)
	Tuesday, February 7, 2017 12:00 pm Mountain Standard Time (Denver, GMT-07:00)
	Tuesday, February 7, 2017 11:00 am Pacific Standard Time (San Francisco, GMT-08:00)
Panelist(s) Info:	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
Duration:	1 hour 30 minutes
Description:	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
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