Using natural language processing to identify acute care patients who lack advance directives, decisional capacity, and surrogate decision makers.
The prevalence of patients who are Incapacitated with No Evident Advance Directives or Surrogates (INEADS) remains unknown because such data are not routinely captured in structured electronic health records. This study sought to develop and validate a natural language processing (NLP) algorithm to...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0270220 |
| _version_ | 1828788318196727808 |
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| author | Jiyoun Song Maxim Topaz Aviv Y Landau Robert Klitzman Jingjing Shang Patricia Stone Margaret McDonald Bevin Cohen |
| author_facet | Jiyoun Song Maxim Topaz Aviv Y Landau Robert Klitzman Jingjing Shang Patricia Stone Margaret McDonald Bevin Cohen |
| author_sort | Jiyoun Song |
| collection | DOAJ |
| description | The prevalence of patients who are Incapacitated with No Evident Advance Directives or Surrogates (INEADS) remains unknown because such data are not routinely captured in structured electronic health records. This study sought to develop and validate a natural language processing (NLP) algorithm to identify information related to being INEADS from clinical notes. We used a publicly available dataset of critical care patients from 2001 through 2012 at a United States academic medical center, which contained 418,393 relevant clinical notes for 23,904 adult admissions. We developed 17 subcategories indicating reduced or elevated potential for being INEADS, and created a vocabulary of terms and expressions within each. We used an NLP application to create a language model and expand these vocabularies. The NLP algorithm was validated against gold standard manual review of 300 notes and showed good performance overall (F-score = 0.83). More than 80% of admissions had notes containing information in at least one subcategory. Thirty percent (n = 7,134) contained at least one of five social subcategories indicating elevated potential for being INEADS, and <1% (n = 81) contained at least four, which we classified as high likelihood of being INEADS. Among these, n = 8 admissions had no subcategory indicating reduced likelihood of being INEADS, and appeared to meet the definition of INEADS following manual review. Among the remaining n = 73 who had at least one subcategory indicating reduced likelihood of being INEADS, manual review of a 10% sample showed that most did not appear to be INEADS. Compared with the full cohort, the high likelihood group was significantly more likely to die during hospitalization and within four years, to have Medicaid, to have an emergency admission, and to be male. This investigation demonstrates potential for NLP to identify INEADS patients, and may inform interventions to enhance advance care planning for patients who lack social support. |
| first_indexed | 2024-12-12T00:50:29Z |
| format | Article |
| id | doaj.art-d6e701edef754441954df81911dfb01d |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-12T00:50:29Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-d6e701edef754441954df81911dfb01d2022-12-22T00:44:02ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01177e027022010.1371/journal.pone.0270220Using natural language processing to identify acute care patients who lack advance directives, decisional capacity, and surrogate decision makers.Jiyoun SongMaxim TopazAviv Y LandauRobert KlitzmanJingjing ShangPatricia StoneMargaret McDonaldBevin CohenThe prevalence of patients who are Incapacitated with No Evident Advance Directives or Surrogates (INEADS) remains unknown because such data are not routinely captured in structured electronic health records. This study sought to develop and validate a natural language processing (NLP) algorithm to identify information related to being INEADS from clinical notes. We used a publicly available dataset of critical care patients from 2001 through 2012 at a United States academic medical center, which contained 418,393 relevant clinical notes for 23,904 adult admissions. We developed 17 subcategories indicating reduced or elevated potential for being INEADS, and created a vocabulary of terms and expressions within each. We used an NLP application to create a language model and expand these vocabularies. The NLP algorithm was validated against gold standard manual review of 300 notes and showed good performance overall (F-score = 0.83). More than 80% of admissions had notes containing information in at least one subcategory. Thirty percent (n = 7,134) contained at least one of five social subcategories indicating elevated potential for being INEADS, and <1% (n = 81) contained at least four, which we classified as high likelihood of being INEADS. Among these, n = 8 admissions had no subcategory indicating reduced likelihood of being INEADS, and appeared to meet the definition of INEADS following manual review. Among the remaining n = 73 who had at least one subcategory indicating reduced likelihood of being INEADS, manual review of a 10% sample showed that most did not appear to be INEADS. Compared with the full cohort, the high likelihood group was significantly more likely to die during hospitalization and within four years, to have Medicaid, to have an emergency admission, and to be male. This investigation demonstrates potential for NLP to identify INEADS patients, and may inform interventions to enhance advance care planning for patients who lack social support.https://doi.org/10.1371/journal.pone.0270220 |
| spellingShingle | Jiyoun Song Maxim Topaz Aviv Y Landau Robert Klitzman Jingjing Shang Patricia Stone Margaret McDonald Bevin Cohen Using natural language processing to identify acute care patients who lack advance directives, decisional capacity, and surrogate decision makers. PLoS ONE |
| title | Using natural language processing to identify acute care patients who lack advance directives, decisional capacity, and surrogate decision makers. |
| title_full | Using natural language processing to identify acute care patients who lack advance directives, decisional capacity, and surrogate decision makers. |
| title_fullStr | Using natural language processing to identify acute care patients who lack advance directives, decisional capacity, and surrogate decision makers. |
| title_full_unstemmed | Using natural language processing to identify acute care patients who lack advance directives, decisional capacity, and surrogate decision makers. |
| title_short | Using natural language processing to identify acute care patients who lack advance directives, decisional capacity, and surrogate decision makers. |
| title_sort | using natural language processing to identify acute care patients who lack advance directives decisional capacity and surrogate decision makers |
| url | https://doi.org/10.1371/journal.pone.0270220 |
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