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Use of Expert Relevancy Ratings to Validate Task-Specific Search Strategies for Electronic Medical Records
As electronic medical records (EMRs) grow in size and complexity, there is increasing need for automated EMR tools that highlight the medical record items most germane to a practitioner’s task-specific needs. The development of such tools would be aided by gold standards of information relevance for...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Gunther Eysenbach
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4288078/ https://www.ncbi.nlm.nih.gov/pubmed/25601018 http://dx.doi.org/10.2196/medinform.3205 |
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author | Harvey, Harlan Krishnaraj, Arun Alkasab, Tarik K |
author_facet | Harvey, Harlan Krishnaraj, Arun Alkasab, Tarik K |
author_sort | Harvey, Harlan |
collection | PubMed |
description | As electronic medical records (EMRs) grow in size and complexity, there is increasing need for automated EMR tools that highlight the medical record items most germane to a practitioner’s task-specific needs. The development of such tools would be aided by gold standards of information relevance for a series of different clinical scenarios. We have previously proposed a process in which exemplar medical record data are extracted from actual patients’ EMRs, anonymized, and presented to clinical experts, who then score each medical record item for its relevance to a specific clinical scenario. In this paper, we present how that body of expert relevancy data can be used to create a test framework to validate new EMR search strategies. |
format | Online Article Text |
id | pubmed-4288078 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Gunther Eysenbach |
record_format | MEDLINE/PubMed |
spelling | pubmed-42880782015-01-15 Use of Expert Relevancy Ratings to Validate Task-Specific Search Strategies for Electronic Medical Records Harvey, Harlan Krishnaraj, Arun Alkasab, Tarik K JMIR Med Inform Viewpoint As electronic medical records (EMRs) grow in size and complexity, there is increasing need for automated EMR tools that highlight the medical record items most germane to a practitioner’s task-specific needs. The development of such tools would be aided by gold standards of information relevance for a series of different clinical scenarios. We have previously proposed a process in which exemplar medical record data are extracted from actual patients’ EMRs, anonymized, and presented to clinical experts, who then score each medical record item for its relevance to a specific clinical scenario. In this paper, we present how that body of expert relevancy data can be used to create a test framework to validate new EMR search strategies. Gunther Eysenbach 2014-03-11 /pmc/articles/PMC4288078/ /pubmed/25601018 http://dx.doi.org/10.2196/medinform.3205 Text en ©Harlan Harvey, Arun Krishnaraj, Tarik K Alkasab. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 11.03.2014. http://creativecommons.org/licenses/by/2.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Viewpoint Harvey, Harlan Krishnaraj, Arun Alkasab, Tarik K Use of Expert Relevancy Ratings to Validate Task-Specific Search Strategies for Electronic Medical Records |
title | Use of Expert Relevancy Ratings to Validate Task-Specific Search Strategies for Electronic Medical Records |
title_full | Use of Expert Relevancy Ratings to Validate Task-Specific Search Strategies for Electronic Medical Records |
title_fullStr | Use of Expert Relevancy Ratings to Validate Task-Specific Search Strategies for Electronic Medical Records |
title_full_unstemmed | Use of Expert Relevancy Ratings to Validate Task-Specific Search Strategies for Electronic Medical Records |
title_short | Use of Expert Relevancy Ratings to Validate Task-Specific Search Strategies for Electronic Medical Records |
title_sort | use of expert relevancy ratings to validate task-specific search strategies for electronic medical records |
topic | Viewpoint |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4288078/ https://www.ncbi.nlm.nih.gov/pubmed/25601018 http://dx.doi.org/10.2196/medinform.3205 |
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