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David Sontag. Gopinath, Divya,M. Eng.Massachusetts Institute of Technology. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2021-01-06T18:31:32Z 2021-01-06T18:31:32Z 2020 2020 https://hdl.handle.net/1721.1/129149 1227275442 Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020 Cataloged from student-submitted PDF of thesis. Includes bibliographical references (pages 163-175). Electronic health records (EHRs) have irrevocably changed the practice of medicine by systematizing the collection of patient-level data. However, clinicians currently spend more time documenting information in EHRs than interacting directly with patients, and have adapted to time-intensive note-writing by authoring free-text notes overloaded with jargon and acronyms. Clinical notes are therefore difficult to parse and largely unstructured. This negatively impacts the ability of EHR systems to convey information between different clinicians and institutions, to communicate medical findings to patients, and to allow for programmatic ingestion of data to derive further automatically-learned insights. In this thesis, we present a new EHR system that addresses these problems by using novel machine learning methods to streamline the processes by which clinicians enter in new information and surface relevant details from past medical records. Our intelligent interface aids physicians as they type, allowing for automatic suggestion and live-tagging of clinical concepts to alleviate documentation burden, while simultaneously enabling clinical decision support and contextual information synthesis. Furthermore, as clinicians craft notes we automatically structure and curate their free-text inputs, allowing for further data-driven innovation and improvement. This EHR can reduce physician burnout, decrease diagnostic error, and improve patient outcomes, all while collecting a corpus of clean, labelled clinical data. Our system is currently deployed live at the Beth Israel Deaconess Medical Center Emergency Department and is in use by doctors. by Divya Gopinath. M.Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 175 pages eng Massachusetts Institute of Technology MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. ML-driven clinical documentation Thesis application/pdf 2021-01-06T18:31:31Z Publication Master EECS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="044d5f49-f588-4337-bdd4-60f5495a4229"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>ML-driven clinical documentation</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2020</PublicationDate> <Authors> <Author> <DisplayName>Gopinath, Divya,M. Eng.Massachusetts Institute of Technology.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Electronic health records (EHRs) have irrevocably changed the practice of medicine by systematizing the collection of patient-level data. However, clinicians currently spend more time documenting information in EHRs than interacting directly with patients, and have adapted to time-intensive note-writing by authoring free-text notes overloaded with jargon and acronyms. Clinical notes are therefore difficult to parse and largely unstructured. This negatively impacts the ability of EHR systems to convey information between different clinicians and institutions, to communicate medical findings to patients, and to allow for programmatic ingestion of data to derive further automatically-learned insights. In this thesis, we present a new EHR system that addresses these problems by using novel machine learning methods to streamline the processes by which clinicians enter in new information and surface relevant details from past medical records. Our intelligent interface aids physicians as they type, allowing for automatic suggestion and live-tagging of clinical concepts to alleviate documentation burden, while simultaneously enabling clinical decision support and contextual information synthesis. Furthermore, as clinicians craft notes we automatically structure and curate their free-text inputs, allowing for further data-driven innovation and improvement. This EHR can reduce physician burnout, decrease diagnostic error, and improve patient outcomes, all while collecting a corpus of clean, labelled clinical data. Our system is currently deployed live at the Beth Israel Deaconess Medical Center Emergency Department and is in use by doctors.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>