Format

Send to

Choose Destination
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.

Author information

1
Department of Surgery, Massachusetts General Hospital, Boston, Massachusetts.
2
The Center for Surgery and Public Health, Brigham and Women's Hospital, Boston, Massachusetts.
3
Department of Surgery, University of California, San Diego, California.
4
Division of Trauma, Burns, and Surgical Critical Care, Brigham and Women's Hospital, Boston, Massachusetts.
5
Department of Psychosocial Oncology and Palliative Care, Dana Farber Cancer Institute, Boston, Massachusetts.

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.

KEYWORDS:

natural language processing; palliative care communication; surgical palliative care

PMID:
31580763
DOI:
10.1089/jpm.2019.0268

Supplemental Content

Full text links

Icon for Atypon
Loading ...
Support Center