Natural Language Processing to Assess Palliative Care and End-of-Life Process Measures in Patients With Breast Cancer With Leptomeningeal Disease.

Abstract

Palliative care consultation during serious life-limiting illness can reduce symptom burden and improve quality of care. However, quantifying the impact of palliative care is hindered by the limitations of manual chart review and administrative coding. Using novel natural language process (NLP) techniques, we examined associations between palliative care consultations and performance on nationally endorsed metrics for high-quality end-of-life (EOL) care in patients with leptomeningeal disease (LMD) secondary to metastatic breast cancer. Patients with breast cancer with LMD were identified using administrative billing codes and NLP review of magnetic resonance imaging reports at 2 tertiary care centers between 2010 and 2016. Next, NLP was used to review clinical notes to (1) determine the presence of palliative care consultations and (2) determine the performance of process measures associated with high-quality EOL care, including discussions of goals of care, code status limitations, and hospice. Associations between palliative care consultation and documentation of EOL process measures were assessed using logistic regression. We identified 183 cases of LMD. Median age was 56 (interquartile range [IQR]: 46-64) years and median survival was 150 days (IQR: 67-350). Within 6 months of diagnosis, 88.5% of patients had documentation of ≥1 process measure, including discussions of goals of care (63.4%), code status limitations (62.8%), or hospice (72.1%). Palliative care consultation was a predictor of subsequent documentation of goals of care (odds ratio [OR], 3.15; 95% confidence interval [CI], 1.58-6.27) and hospice discussions (OR, 4.61; 95% CI, 2.12-10.03). Palliative care involvement is associated with increased performance of EOL process measures in patients with breast cancer with LMD.