Title | Voice-dictated versus typed-in clinician notes: linguistic properties and the potential implications on natural language processing. |
Publication Type | Journal Article |
Year of Publication | 2011 |
Authors | Zheng, K, Mei, Q, Yang, L, Manion, FJ, Balis, UJ, Hanauer, DA |
Journal | AMIA Annu Symp Proc |
Volume | 2011 |
Pagination | 1630-8 |
Date Published | 2011 |
ISSN | 1942-597X |
Keywords | Computer Peripherals, Electronic Health Records, Humans, Linguistics, Medical Records, Narration, Natural Language Processing, Speech Recognition Software, User-Computer Interface |
Abstract | In this study, we comparatively examined the linguistic properties of narrative clinician notes created through voice dictation versus those directly entered by clinicians via a computer keyboard. Intuitively, the nature of voice-dictated notes would resemble that of natural language, while typed-in notes may demonstrate distinctive language features for reasons such as intensive usage of acronyms. The study analyses were based on an empirical dataset retrieved from our institutional electronic health records system. The dataset contains 30,000 voice-dictated notes and 30,000 notes that were entered manually; both were encounter notes generated in ambulatory care settings. The results suggest that between the narrative clinician notes created via these two different methods, there exists a considerable amount of lexical and distributional differences. Such differences could have a significant impact on the performance of natural language processing tools, necessitating these two different types of documents being differentially treated. |
Alternate Journal | AMIA Annu Symp Proc |
PubMed ID | 22195229 |
PubMed Central ID | PMC3243272 |
Grant List | HHSN276201000032C / / PHS HHS / United States UL1RR024986 / RR / NCRR NIH HHS / United States |
Research reported in this publication was supported by the National Cancer Institutes of
Health under Award Number P30CA046592. The content is solely the responsibility
of the authors and does not necessarily represent the official views of the
National Institutes of Health.
Research reported in this publication was supported by the National Cancer Institutes of
Health under Award Number P30CA046592 by the use of the following Cancer Center
Shared Resource(s): Biostatistics, Analytics & Bioinformatics; Flow Cytometry;
Transgenic Animal Models; Tissue and Molecular Pathology; Structure & Drug
Screening; Cell & Tissue Imaging; Experimental Irradiation; Preclinical
Imaging & Computational Analysis; Health Communications; Immune Monitoring;
Pharmacokinetics)
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