A Natural Language Processing System That Links Medical Terms in Electronic Health Record Notes to Lay Definitions: System Development Using Physician Reviews

Many health care systems now allow patients to access their electronic health record (EHR) notes online through patient portals. Medical jargon in EHR notes can confuse patients, which may interfere with potential benefits of patient access to EHR notes. The aim of this study was to develop and eval...

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Veröffentlicht in:	Journal of medical Internet research Jg. 20; H. 1; S. e26
Hauptverfasser:	Chen, Jinying, Druhl, Emily, Polepalli Ramesh, Balaji, Houston, Thomas K, Brandt, Cynthia A, Zulman, Donna M, Vimalananda, Varsha G, Malkani, Samir, Yu, Hong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Canada Gunther Eysenbach MD MPH, Associate Professor 22.01.2018 JMIR Publications
Schlagworte:	Access Algorithms Ambiguity Blood pressure Candidates Comprehension Content analysis Drugs Electronic health records Evaluation Feedback Health Health care Health education Health literacy Health services Human-computer interaction Influenza Innovations Internet Jargon Lay people Medical language Medical records Medical terminology Natural language processing Original Paper Patient satisfaction Patients Personal health Physicians Practitioner patient relationship Relevance feedback Subject specialists Supervision Systems development Usability Validity Veterans Vocabularies & taxonomies Web portals United States > US Pittsburgh Pennsylvania usability testing computer software electronic health records natural language processing consumer health informatics
ISSN:	1438-8871, 1439-4456, 1438-8871
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Abstract	Many health care systems now allow patients to access their electronic health record (EHR) notes online through patient portals. Medical jargon in EHR notes can confuse patients, which may interfere with potential benefits of patient access to EHR notes. The aim of this study was to develop and evaluate the usability and content quality of NoteAid, a Web-based natural language processing system that links medical terms in EHR notes to lay definitions, that is, definitions easily understood by lay people. NoteAid incorporates two core components: CoDeMed, a lexical resource of lay definitions for medical terms, and MedLink, a computational unit that links medical terms to lay definitions. We developed innovative computational methods, including an adapted distant supervision algorithm to prioritize medical terms important for EHR comprehension to facilitate the effort of building CoDeMed. Ten physician domain experts evaluated the user interface and content quality of NoteAid. The evaluation protocol included a cognitive walkthrough session and a postsession questionnaire. Physician feedback sessions were audio-recorded. We used standard content analysis methods to analyze qualitative data from these sessions. Physician feedback was mixed. Positive feedback on NoteAid included (1) Easy to use, (2) Good visual display, (3) Satisfactory system speed, and (4) Adequate lay definitions. Opportunities for improvement arising from evaluation sessions and feedback included (1) improving the display of definitions for partially matched terms, (2) including more medical terms in CoDeMed, (3) improving the handling of terms whose definitions vary depending on different contexts, and (4) standardizing the scope of definitions for medicines. On the basis of these results, we have improved NoteAid's user interface and a number of definitions, and added 4502 more definitions in CoDeMed. Physician evaluation yielded useful feedback for content validation and refinement of this innovative tool that has the potential to improve patient EHR comprehension and experience using patient portals. Future ongoing work will develop algorithms to handle ambiguous medical terms and test and evaluate NoteAid with patients.
AbstractList	Many health care systems now allow patients to access their electronic health record (EHR) notes online through patient portals. Medical jargon in EHR notes can confuse patients, which may interfere with potential benefits of patient access to EHR notes. The aim of this study was to develop and evaluate the usability and content quality of NoteAid, a Web-based natural language processing system that links medical terms in EHR notes to lay definitions, that is, definitions easily understood by lay people. NoteAid incorporates two core components: CoDeMed, a lexical resource of lay definitions for medical terms, and MedLink, a computational unit that links medical terms to lay definitions. We developed innovative computational methods, including an adapted distant supervision algorithm to prioritize medical terms important for EHR comprehension to facilitate the effort of building CoDeMed. Ten physician domain experts evaluated the user interface and content quality of NoteAid. The evaluation protocol included a cognitive walkthrough session and a postsession questionnaire. Physician feedback sessions were audio-recorded. We used standard content analysis methods to analyze qualitative data from these sessions. Physician feedback was mixed. Positive feedback on NoteAid included (1) Easy to use, (2) Good visual display, (3) Satisfactory system speed, and (4) Adequate lay definitions. Opportunities for improvement arising from evaluation sessions and feedback included (1) improving the display of definitions for partially matched terms, (2) including more medical terms in CoDeMed, (3) improving the handling of terms whose definitions vary depending on different contexts, and (4) standardizing the scope of definitions for medicines. On the basis of these results, we have improved NoteAid's user interface and a number of definitions, and added 4502 more definitions in CoDeMed. Physician evaluation yielded useful feedback for content validation and refinement of this innovative tool that has the potential to improve patient EHR comprehension and experience using patient portals. Future ongoing work will develop algorithms to handle ambiguous medical terms and test and evaluate NoteAid with patients. Background: Many health care systems now allow patients to access their electronic health record (EHR) notes online through patient portals. Medical jargon in EHR notes can confuse patients, which may interfere with potential benefits of patient access to EHR notes. Objective: The aim of this study was to develop and evaluate the usability and content quality of NoteAid, a Web-based natural language processing system that links medical terms in EHR notes to lay definitions, that is, definitions easily understood by lay people. Methods: NoteAid incorporates two core components: CoDeMed, a lexical resource of lay definitions for medical terms, and MedLink, a computational unit that links medical terms to lay definitions. We developed innovative computational methods, including an adapted distant supervision algorithm to prioritize medical terms important for EHR comprehension to facilitate the effort of building CoDeMed. Ten physician domain experts evaluated the user interface and content quality of NoteAid. The evaluation protocol included a cognitive walkthrough session and a postsession questionnaire. Physician feedback sessions were audio-recorded. We used standard content analysis methods to analyze qualitative data from these sessions. Results: Physician feedback was mixed. Positive feedback on NoteAid included (1) Easy to use, (2) Good visual display, (3) Satisfactory system speed, and (4) Adequate lay definitions. Opportunities for improvement arising from evaluation sessions and feedback included (1) improving the display of definitions for partially matched terms, (2) including more medical terms in CoDeMed, (3) improving the handling of terms whose definitions vary depending on different contexts, and (4) standardizing the scope of definitions for medicines. On the basis of these results, we have improved NoteAid’s user interface and a number of definitions, and added 4502 more definitions in CoDeMed. Conclusions: Physician evaluation yielded useful feedback for content validation and refinement of this innovative tool that has the potential to improve patient EHR comprehension and experience using patient portals. Future ongoing work will develop algorithms to handle ambiguous medical terms and test and evaluate NoteAid with patients. Many health care systems now allow patients to access their electronic health record (EHR) notes online through patient portals. Medical jargon in EHR notes can confuse patients, which may interfere with potential benefits of patient access to EHR notes.BACKGROUNDMany health care systems now allow patients to access their electronic health record (EHR) notes online through patient portals. Medical jargon in EHR notes can confuse patients, which may interfere with potential benefits of patient access to EHR notes.The aim of this study was to develop and evaluate the usability and content quality of NoteAid, a Web-based natural language processing system that links medical terms in EHR notes to lay definitions, that is, definitions easily understood by lay people.OBJECTIVEThe aim of this study was to develop and evaluate the usability and content quality of NoteAid, a Web-based natural language processing system that links medical terms in EHR notes to lay definitions, that is, definitions easily understood by lay people.NoteAid incorporates two core components: CoDeMed, a lexical resource of lay definitions for medical terms, and MedLink, a computational unit that links medical terms to lay definitions. We developed innovative computational methods, including an adapted distant supervision algorithm to prioritize medical terms important for EHR comprehension to facilitate the effort of building CoDeMed. Ten physician domain experts evaluated the user interface and content quality of NoteAid. The evaluation protocol included a cognitive walkthrough session and a postsession questionnaire. Physician feedback sessions were audio-recorded. We used standard content analysis methods to analyze qualitative data from these sessions.METHODSNoteAid incorporates two core components: CoDeMed, a lexical resource of lay definitions for medical terms, and MedLink, a computational unit that links medical terms to lay definitions. We developed innovative computational methods, including an adapted distant supervision algorithm to prioritize medical terms important for EHR comprehension to facilitate the effort of building CoDeMed. Ten physician domain experts evaluated the user interface and content quality of NoteAid. The evaluation protocol included a cognitive walkthrough session and a postsession questionnaire. Physician feedback sessions were audio-recorded. We used standard content analysis methods to analyze qualitative data from these sessions.Physician feedback was mixed. Positive feedback on NoteAid included (1) Easy to use, (2) Good visual display, (3) Satisfactory system speed, and (4) Adequate lay definitions. Opportunities for improvement arising from evaluation sessions and feedback included (1) improving the display of definitions for partially matched terms, (2) including more medical terms in CoDeMed, (3) improving the handling of terms whose definitions vary depending on different contexts, and (4) standardizing the scope of definitions for medicines. On the basis of these results, we have improved NoteAid's user interface and a number of definitions, and added 4502 more definitions in CoDeMed.RESULTSPhysician feedback was mixed. Positive feedback on NoteAid included (1) Easy to use, (2) Good visual display, (3) Satisfactory system speed, and (4) Adequate lay definitions. Opportunities for improvement arising from evaluation sessions and feedback included (1) improving the display of definitions for partially matched terms, (2) including more medical terms in CoDeMed, (3) improving the handling of terms whose definitions vary depending on different contexts, and (4) standardizing the scope of definitions for medicines. On the basis of these results, we have improved NoteAid's user interface and a number of definitions, and added 4502 more definitions in CoDeMed.Physician evaluation yielded useful feedback for content validation and refinement of this innovative tool that has the potential to improve patient EHR comprehension and experience using patient portals. Future ongoing work will develop algorithms to handle ambiguous medical terms and test and evaluate NoteAid with patients.CONCLUSIONSPhysician evaluation yielded useful feedback for content validation and refinement of this innovative tool that has the potential to improve patient EHR comprehension and experience using patient portals. Future ongoing work will develop algorithms to handle ambiguous medical terms and test and evaluate NoteAid with patients.
Author	Polepalli Ramesh, Balaji Vimalananda, Varsha G Houston, Thomas K Zulman, Donna M Malkani, Samir Yu, Hong Chen, Jinying Druhl, Emily Brandt, Cynthia A
AuthorAffiliation	8 School of Medicine Boston University Boston, MA United States 7 Veterans Affairs Palo Alto Health Care System Menlo Park, CA United States 1 Department of Quantitative Health Sciences University of Massachusetts Medical School Worcester, MA United States 3 Optum Boston, MA United States 5 Center for Medical Informatics Yale University New Haven, CT United States 6 Division of Primary Care and Population Health Stanford University School of Medicine Stanford, CA United States 9 Diabetes Center of Excellence University of Massachusetts Medical School Worcester, MA United States 2 Bedford Veterans Affairs Medical Center Center for Healthcare Organization and Implementation Research Bedford, MA United States 4 Veterans Affairs Connecticut Health Care System West Haven, CT United States
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BackLink	https://www.ncbi.nlm.nih.gov/pubmed/29358159$$D View this record in MEDLINE/PubMed
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Copyright	Jinying Chen, Emily Druhl, Balaji Polepalli Ramesh, Thomas K Houston, Cynthia A Brandt, Donna M Zulman, Varsha G Vimalananda, Samir Malkani, Hong Yu. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 22.01.2018. 2018. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Jinying Chen, Emily Druhl, Balaji Polepalli Ramesh, Thomas K Houston, Cynthia A Brandt, Donna M Zulman, Varsha G Vimalananda, Samir Malkani, Hong Yu. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 22.01.2018. 2018
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License	Jinying Chen, Emily Druhl, Balaji Polepalli Ramesh, Thomas K Houston, Cynthia A Brandt, Donna M Zulman, Varsha G Vimalananda, Samir Malkani, Hong Yu. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 22.01.2018. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
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Snippet	Many health care systems now allow patients to access their electronic health record (EHR) notes online through patient portals. Medical jargon in EHR notes... Background: Many health care systems now allow patients to access their electronic health record (EHR) notes online through patient portals. Medical jargon in...
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SubjectTerms	Access Algorithms Ambiguity Blood pressure Candidates Comprehension Content analysis Drugs Electronic health records Evaluation Feedback Health Health care Health education Health literacy Health services Human-computer interaction Influenza Innovations Internet Jargon Lay people Medical language Medical records Medical terminology Natural language processing Original Paper Patient satisfaction Patients Personal health Physicians Practitioner patient relationship Relevance feedback Subject specialists Supervision Systems development Usability Validity Veterans Vocabularies & taxonomies Web portals
Title	A Natural Language Processing System That Links Medical Terms in Electronic Health Record Notes to Lay Definitions: System Development Using Physician Reviews
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