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

Abstract

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.

The use of natural language processing in palliative care research: A scoping review

“…Clinical notes and electronic medical records were the most common primary data sources, used in 57 studies (69.5%). 21,23–27,29,30,33–36,40,42–46,48–52,54,55,57–64,66–68,70–73,75,79–93,95 Other primary sources included audio recordings ( n = 6, 7.3%), 6,28,32,38,39,65 administrative data ( n = 5, 6.1%), 37,47,53,77,...…”

“…Brizzi et al, 25 Kern et al, 49 Lau et al, 53 Lee et al, 54,55 Lindvall et al, 58 Poort et al, 71 Udelsman et al 84 Conversation analysis/conversational dynamics during serious illness conversations 7 (8.5) van den Broek-Altenburg et al, 6 Clarfeld et al, 28 Durieux et al, 32 Gramling et al, 38,39 Manukyan et al, 65 Ross et al 72 Symptom identification 6 (7.3) DiMartino et al, 30 Forsyth et al, 35 Heintzelman et al, 43 López-Torrecilla et al, 60 Taggart et al, 83 Yang et al 91 Decision-making 4 (4.8) Barrett et al, 24 Ouchi et al, 70 Saeed et al, 73 Almasalha et al 85 Identification of patients likely to benefit from palliative care or palliative needs or palliative status 4 (4.8) Murphree et al, 68 Sandham et al, 75 Song et al, 79 Zhang et al 97 Clinical phenotypes identification 3 (3.6) Ernecoff et al, 34 Jay et al, 45 natural language processing software to complete data analysis. This suggests that each software has its own strengths, and that weaknesses may be overcome by the combined use of more than one software solution to achieve the desired goal of the analyses.…”

“…Clinical notes and electronic medical records were the most common primary data sources, used in 57 studies (69.5%). 21,[23][24][25][26][27]29,30,[33][34][35][36]40,[42][43][44][45][46][48][49][50][51][52]54,55,[57][58][59][60][61][62][63][64][66][67][68][70][71][72][73]75,[79][80][81][82][83][84][85][86][87][88][89]…”