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J Palliat Med. 2019 Oct 2. doi: 10.1089/jpm.2019.0268. [Epub ahead of print]

Variation in Serious Illness Communication among Surgical Patients Receiving Palliative Care.

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Department of Surgery, Massachusetts General Hospital, Boston, Massachusetts.
The Center for Surgery and Public Health, Brigham and Women's Hospital, Boston, Massachusetts.
Department of Surgery, University of California, San Diego, California.
Division of Trauma, Burns, and Surgical Critical Care, Brigham and Women's Hospital, Boston, Massachusetts.
Department of Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute, Boston, Massachusetts.


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.


natural language processing; palliative care communication; surgical palliative care


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