Using natural language processing to identify acute care patients who lack advance directives, decisional capacity, and surrogate decision makers.

Autor: Song J; Columbia University School of Nursing, New York, New York, United States of America., Topaz M; Columbia University School of Nursing, New York, New York, United States of America.; Data Science Institute, Columbia University, New York, New York, United States of America.; Visiting Nurse Service of New York, New York, New York, United States of America., Landau AY; Data Science Institute, Columbia University, New York, New York, United States of America.; Columbia School of Social Work, New York, New York, United States of America., Klitzman R; Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, United States of America.; Mailman School of Public Health, Columbia University, New York, New York, United States of America., Shang J; Columbia University School of Nursing, New York, New York, United States of America., Stone P; Columbia University School of Nursing, New York, New York, United States of America., McDonald M; Visiting Nurse Service of New York, New York, New York, United States of America., Cohen B; Center for Nursing Research and Innovation, Mount Sinai Health System, New York, New York, United States of America.; Department of Geriatric and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2022 Jul 11; Vol. 17 (7), pp. e0270220. Date of Electronic Publication: 2022 Jul 11 (Print Publication: 2022).
DOI: 10.1371/journal.pone.0270220
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje