1. Variation in Serious Illness Communication among Surgical Patients Receiving Palliative Care.
- Author
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Udelsman, Brooks V., Lee, Katherine C., Lilley, Elizabeth J., Chang, David C., Lindvall, Charlotta, and Cooper, Zara
- Subjects
ACADEMIC medical centers ,CATASTROPHIC illness ,COMMUNICATION ,LONGITUDINAL method ,NATURAL language processing ,PALLIATIVE treatment ,PANCREATIC tumors ,PATIENTS ,STATISTICAL sampling ,SURGERY ,WOUNDS & injuries ,RETROSPECTIVE studies ,DESCRIPTIVE statistics - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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