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HDIC 2018 Session Recording-Natural Language Processing to Improve Accuracy

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Session recording from the 2018 Healthcare Documentation Integrity Conference (HDIC):


Natural Language Processing to Improve the Accuracy and Quality of Dictated Medical Documents

Foster Goss, DO, MMSc, FACEP



High-quality and accurate medical documents are critical for effective inter-provider communication and patient care. Electronic health records (EHRs) have evolved to offer clinicians a range of documentation methods, including traditional dictation, typed free-text documents, and template-based, structured documents. Physician use of speech recognition (SR) technology has risen in recent years because of its ease of use and efficiency at the point of care. However, high error rates, upwards of 10 to 23 percent, have been observed in SR-generated medical documents. To avoid SR errors, physicians must engage in careful proofreading and report editing, which is time-consuming for busy clinicians. As such, an increasing number of errors are entered into the permanent medical record through this technology, potentially jeopardizing the quality and accuracy of medical documents and ultimately patient care.

A solution to this problem is to improve accuracy through automated error detection using natural language processing (NLP). The goals of this project are to study the nature of SR-generated errors in clinical documents and to develop and evaluate innovative methods for automatic error detection and correction. In this session, learn how the research team will investigate statistical, machine learning, and knowledge-based approaches. This project has the potential to improve the accuracy, completeness, legibility, and accessibility of medical documents to enhance patient safety and health care delivery.


Foster R. Goss is an emergency physician at the University of Colorado Hospital and assistant professor of emergency medicine within the University of Colorado School of Medicine. Dr. Goss completed his residency training at Albert Einstein Medical Center and then pursued a National Library of Medicine-sponsored fellowship, a master’s in medical science in Biomedical Informatics at Harvard Medical School, and a Clinical Decision-Making fellowship at Tufts Medical Center. He is currently the Director for Research and Development for the UCHealth Care Innovation Center, a hub for integrating novel digital health technologies to solve important problems facing health systems. He is also a federally funded researcher whose research focuses on natural language processing, decision analysis, speech recognition, and information retrieval, as it relates to patient safety, coordination of care and documentation quality. He is currently the site principal investigator on the use of natural language processing to improve the accuracy and quality of dictated medical documents, a multi-institutional grant between the University of Colorado and Brigham and Women’s Hospital where he also holds a research position. His work has led to multiple publications and presentations at national conferences.


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