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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-12-01
|
| Series: | PLOS Digital Health |
| Online Access: | https://doi.org/10.1371/journal.pdig.0000158 |
| _version_ | 1797698284515491840 |
|---|---|
| author | Kenichiro Ando Takashi Okumura Mamoru Komachi Hiromasa Horiguchi Yuji Matsumoto |
| author_facet | Kenichiro Ando Takashi Okumura Mamoru Komachi Hiromasa Horiguchi Yuji Matsumoto |
| author_sort | Kenichiro Ando |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-12T03:51:56Z |
| format | Article |
| id | doaj.art-c7933daf078f4cccbc863516bcbe5e2f |
| institution | Directory Open Access Journal |
| issn | 2767-3170 |
| language | English |
| last_indexed | 2024-03-12T03:51:56Z |
| publishDate | 2022-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj.art-c7933daf078f4cccbc863516bcbe5e2f2023-09-03T12:20:01ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702022-12-01112e000015810.1371/journal.pdig.0000158Is artificial intelligence capable of generating hospital discharge summaries from inpatient records?Kenichiro AndoTakashi OkumuraMamoru KomachiHiromasa HoriguchiYuji MatsumotoMedical 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.https://doi.org/10.1371/journal.pdig.0000158 |
| spellingShingle | Kenichiro Ando Takashi Okumura Mamoru Komachi Hiromasa Horiguchi Yuji Matsumoto Is artificial intelligence capable of generating hospital discharge summaries from inpatient records? PLOS Digital Health |
| 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 |
| url | https://doi.org/10.1371/journal.pdig.0000158 |
| work_keys_str_mv | AT kenichiroando isartificialintelligencecapableofgeneratinghospitaldischargesummariesfrominpatientrecords AT takashiokumura isartificialintelligencecapableofgeneratinghospitaldischargesummariesfrominpatientrecords AT mamorukomachi isartificialintelligencecapableofgeneratinghospitaldischargesummariesfrominpatientrecords AT hiromasahoriguchi isartificialintelligencecapableofgeneratinghospitaldischargesummariesfrominpatientrecords AT yujimatsumoto isartificialintelligencecapableofgeneratinghospitaldischargesummariesfrominpatientrecords |