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

Abstract

Background: 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. Objectives: 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. Methods: 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. Results: 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). Conclusion: Palliative care involvement is associated with increased performance of EOL process measures in patients with breast cancer with LMD.

Impact of an internal medicine nocturnist service on care of patients with cancer at a large Canadian teaching hospital: a quality-improvement study

“…Documentation of code status is recognized as an important process measure in hospital medicine and provides tangible evidence of improvements in quality. 23,24 Although not altogether surprising, this finding reinforces the potential benefits of reducing resident workload and shifting some proportion of new admissions onto experienced hospitalists.…”

A systematic review of natural language processing applied to radiology reports

“…Ten studies use NLP to create specific cohorts for research purposes and six reported the performance of their tools. Out of these papers, the majority (n = 8) created cohorts for specific medical conditions including fatty liver disease [92,93] hepatocellular cancer [94], ureteric stones [95], vertebral fracture [96], traumatic brain injury [97,98], and leptomeningeal disease secondary to metastatic breast cancer [99]. Five papers identified cohorts focused on particular radiology findings including ground glass opacities (GGO) [100], cerebral microbleeds (CMB) [101], pulmonary nodules [102,103], changes in the spine correlated to back pain [1] and identifying radiological evidence of people having suffered a fall.…”

“…Amongst the epidemiology studies there were various analytical aims, but they primarily focused on estimating the prevalence or incidence of conditions or imaging findings and looking for associations of these conditions/findings with specific population demographics, associated factors or comorbidities. The focus of one study differed in that it applied NLP to healthcare evaluation, investigating the association of palliative care consultations and measures of high-quality end-of-life (EOL) care [99].…”

Using Natural Language Processing to Classify Serious Illness Communication with Oncology Patients

“…Natural language processing (NLP) can offer an efficient, accurate alternative for identification of SIC in the EHR 14,15 , and has been used to identify care-planning discussions and palliative care delivery. 16,17,18 Despite early progress, more sophisticated approaches are needed to classify and evaluate SIC documentation. At this time, NLP approaches for identification of SIC predominantly rely on keywords derived from chart review.…”

“…Although these results are preliminary, the methodology employed here allows for greater real-world applicability than other reports of NLP approaches to SIC identification thus far, which have all been keyword-based. 15,16,17,18 Recent applications of these methods have seen success in patient groups drawn from pragmatic trials in oncology, 35,36 but due to their lexical basis these efforts have required manual annotation of hundreds of clinical notes, and may be weighted towards inpatient admissions or medical crises requiring treatment decisions. 35 Our method may lay the foundation for more nuanced identification of patient-specific priorities and prognostic communication more upstream in the disease trajectory, which would have significant utility across a wide array of clinical contexts.…”