Open AccessVariation in Serious Illness Communication among Surgical Patients Receiving Palliative Care
Author(s) -
Brooks V. Udelsman,
Katherine C. Lee,
Elizabeth J. Lilley,
David C. Chang,
Charlotta Lindvall,
Zara Cooper
Publication year - 2020
Publication title -
journal of palliative medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.986
H-Index - 90
eISSN - 1096-6218
pISSN - 1557-7740
DOI - 10.1089/jpm.2019.0268
Subject(s) - medicine , palliative care , retrospective cohort study , codebook , cohort , medline , artificial intelligence , nursing , computer science , political science , law
Background: Natural language processing (NLP), a form of computer-assisted data abstraction, rapidly identifies serious illness communication domains such as code-status confirmation and goals of care (GOC) discussions within free-text notes, using a codebook of phrases. Differences in the phrases associated with palliative care for patients with different types of illness are unknown. Objective: To compare communication of code-status clarification and GOC discussions between patients with advanced pancreatic cancer undergoing palliative procedures and patients admitted with life-threatening trauma. Design: Retrospective cohort study. Setting/Subjects: Patients with in-hospital admissions within two academic medical centers. Measurements: Sensitivity and specificity of NLP-identified communication domains compared with manual review. Results: Among patients with advanced pancreatic cancer ( n = 523), NLP identified code-status clarification in 54% of admissions and GOC discussions in 49% of admissions. The sensitivity and specificity for code-status clarification were 94% and 99% respectively, while the sensitivity and specificity for a GOC discussion were 93% and 100%, respectively. Using the same codebook in patients with life-threatening trauma ( n = 2093), NLP identified code-status clarification in 25.9% of admissions and GOC discussions in 6.3% of admissions. While NLP identification had 100% specificity, the sensitivity for code-status clarification and GOC discussion was reduced to 86% and 50%, respectively. Adding dynamic phrases such as "ongoing discussions" and phrases related to "family meetings" increased the sensitivity of the NLP codebook for code status to 98% and for GOC discussions to 100%. Conclusions: Communication of code status and GOC differ between patients with advanced cancer and those with life-threatening trauma. Recognition of these differences can aid in identification in patterns of palliative care delivery.