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Is artificial intelligence capable of generating hospital discharge summaries from inpatient records?
Medical professionals have been burdened by clerical work, and artificial intelligence may efficiently support physicians by generating clinical summaries. However, whether hospital discharge summaries can be generated automatically from inpatient records stored in electronic health records remains...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931331/ https://www.ncbi.nlm.nih.gov/pubmed/36812600 http://dx.doi.org/10.1371/journal.pdig.0000158 |
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author | Ando, Kenichiro Okumura, Takashi Komachi, Mamoru Horiguchi, Hiromasa Matsumoto, Yuji |
author_facet | Ando, Kenichiro Okumura, Takashi Komachi, Mamoru Horiguchi, Hiromasa Matsumoto, Yuji |
author_sort | Ando, Kenichiro |
collection | PubMed |
description | Medical professionals have been burdened by clerical work, and artificial intelligence may efficiently support physicians by generating clinical summaries. However, whether hospital discharge summaries can be generated automatically from inpatient records stored in electronic health records remains unclear. Therefore, this study investigated the sources of information in discharge summaries. First, the discharge summaries were automatically split into fine-grained segments, such as those representing medical expressions, using a machine learning model from a previous study. Second, these segments in the discharge summaries that did not originate from inpatient records were filtered out. This was performed by calculating the n-gram overlap between inpatient records and discharge summaries. The final source origin decision was made manually. Finally, to reveal the specific sources (e.g., referral documents, prescriptions, and physician’s memory) from which the segments originated, they were manually classified by consulting medical professionals. For further and deeper analysis, this study designed and annotated clinical role labels that represent the subjectivity of the expressions and builds a machine learning model to assign them automatically. The analysis results revealed the following: First, 39% of the information in the discharge summary originated from external sources other than inpatient records. Second, patient’s past clinical records constituted 43%, and patient referral documents constituted 18% of the expressions derived from external sources. Third, 11% of the missing information was not derived from any documents. These are possibly derived from physicians’ memories or reasoning. According to these results, end-to-end summarization using machine learning is considered infeasible. Machine summarization with an assisted post-editing process is the best fit for this problem domain. |
format | Online Article Text |
id | pubmed-9931331 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-99313312023-02-16 Is artificial intelligence capable of generating hospital discharge summaries from inpatient records? Ando, Kenichiro Okumura, Takashi Komachi, Mamoru Horiguchi, Hiromasa Matsumoto, Yuji PLOS Digit Health Research Article Medical professionals have been burdened by clerical work, and artificial intelligence may efficiently support physicians by generating clinical summaries. However, whether hospital discharge summaries can be generated automatically from inpatient records stored in electronic health records remains unclear. Therefore, this study investigated the sources of information in discharge summaries. First, the discharge summaries were automatically split into fine-grained segments, such as those representing medical expressions, using a machine learning model from a previous study. Second, these segments in the discharge summaries that did not originate from inpatient records were filtered out. This was performed by calculating the n-gram overlap between inpatient records and discharge summaries. The final source origin decision was made manually. Finally, to reveal the specific sources (e.g., referral documents, prescriptions, and physician’s memory) from which the segments originated, they were manually classified by consulting medical professionals. For further and deeper analysis, this study designed and annotated clinical role labels that represent the subjectivity of the expressions and builds a machine learning model to assign them automatically. The analysis results revealed the following: First, 39% of the information in the discharge summary originated from external sources other than inpatient records. Second, patient’s past clinical records constituted 43%, and patient referral documents constituted 18% of the expressions derived from external sources. Third, 11% of the missing information was not derived from any documents. These are possibly derived from physicians’ memories or reasoning. According to these results, end-to-end summarization using machine learning is considered infeasible. Machine summarization with an assisted post-editing process is the best fit for this problem domain. Public Library of Science 2022-12-12 /pmc/articles/PMC9931331/ /pubmed/36812600 http://dx.doi.org/10.1371/journal.pdig.0000158 Text en © 2022 Ando et al https://creativecommons.org/licenses/by/4.0/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 author and source are credited. |
spellingShingle | Research Article Ando, Kenichiro Okumura, Takashi Komachi, Mamoru Horiguchi, Hiromasa Matsumoto, Yuji Is artificial intelligence capable of generating hospital discharge summaries from inpatient records? |
title | Is artificial intelligence capable of generating hospital discharge summaries from inpatient records? |
title_full | Is artificial intelligence capable of generating hospital discharge summaries from inpatient records? |
title_fullStr | Is artificial intelligence capable of generating hospital discharge summaries from inpatient records? |
title_full_unstemmed | Is artificial intelligence capable of generating hospital discharge summaries from inpatient records? |
title_short | Is artificial intelligence capable of generating hospital discharge summaries from inpatient records? |
title_sort | is artificial intelligence capable of generating hospital discharge summaries from inpatient records? |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931331/ https://www.ncbi.nlm.nih.gov/pubmed/36812600 http://dx.doi.org/10.1371/journal.pdig.0000158 |
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