Using Natural Language Processing to Assess Goals-of-Care Conversations for Patients With Cancer.

PURPOSE: Goals-of-care (GOC) discussions during advanced serious illness and end-of-life (EOL) care are critical. Institutions are increasingly tracking the frequency and timing of GOC documentation, but large-scale content assessments have been limited. We aimed to use natural language processing (NLP) to assess GOC documentation quality and associations with EOL care for patients with cancer. METHODS: This is a retrospective review of patients at a single US center who died with cancer between 2018 and 2022, and had documented GOC notes in the last 12 months of life. Eight GOC components were identified: current understanding of illness, information preferences, prognostic disclosure, goals, fears, acceptable function, trade-offs, and family involvement. NLP software searched for the aggregate presence of these components at the patient level within extracted GOC notes. We evaluated associations between these eight components and receipt of aggressive EOL care (chemotherapy within 14 days of death, no hospice care, or hospice admission ≤3 days of death). RESULTS: Two thousand thirty-one patients met inclusion criteria. The most common GOC component addressed was family involvement (75.0%) and the least common was fears (21.1%). Only 5.4% had all eight components documented. More comprehensive GOC notes were associated with lower rates of aggressive EOL care; 73.2% received aggressive care when 0/8 components were documented, compared with 56.8% and 50.3% with six or seven components discussed, respectively. In multivariate logistic regression, GOC components documented (≤6 v ≥7: OR, 2.13; P < .0001) and primary tumor site (lymphoma: OR, 2.86; P < .0001) were independent predictors of aggressive EOL care. CONCLUSION: Increasingly comprehensive and higher-quality GOC documentation is associated with a lower likelihood of receiving aggressive EOL care. Opportunities to improve the quality and documentation of GOC conversations may affect EOL care for patients with cancer.

Duke Scholars

Author Donna Niedzwiecki Biostatistics & Bioinformatics, Division of Biostatistics

Author Jessica Ma Medicine, Geriatrics and Palliative Care

Author Brittany A Davidson Obstetrics and Gynecology, Gynecologic Oncology

Author Thomas William LeBlanc Medicine, Hematologic Malignancies and Cellular Therapy