Natural Language Processing to Assess End-of-Life Quality Indicators in Cancer Patients Receiving Palliative Surgery.
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- Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, Massachusetts.
- Division of Palliative Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts.
- Department of Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey.
- Department of Surgery, Brigham and Women’s Hospital, Boston, Massachusetts.
- Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, Massachusetts.
- Department of Surgery, Massachusetts General Hospital, Boston, Massachusetts.
- Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
- Palliative Care, VA Greater Los Angeles Healthcare System, Los Angeles, California.
BACKGROUND: Palliative surgical procedures are frequently performed to reduce symptoms in patients with advanced cancer, but quality is difficult to measure.
OBJECTIVE: To determine whether natural language processing (NLP) of the electronic health record (EHR) can be used to (1) identify a population of cancer patients receiving palliative gastrostomy and (2) assess documentation of end-of-life process measures in the EHR.
DESIGN/SETTING: Retrospective cohort study of 302 adult cancer patients who received a gastrostomy tube at a single tertiary medical center.
MEASUREMENTS: Sensitivity and specificity of NLP compared to gold standard of manual chart abstraction in identifying a palliative indication for gastrostomy tube placement and documentation of goals of care discussions, code status determination, palliative care referral, and hospice assessment.
RESULTS: Among 302 cancer patients who underwent gastrostomy, 68 (22.5%) were classified by NLP as having a palliative indication for the procedure compared to 71 patients (23.5%) classified by human coders. Human chart abstraction took >2600 times longer than NLP (28 hours vs. 38 seconds). NLP identified the correct patients with 95.8% sensitivity and 97.4% specificity. NLP also identified end-of-life process measures with high sensitivity (85.7%-92.9%,) and specificity (96.7%-98.9%). In the two months leading up to palliative gastrostomy placement, 20.5% of patients had goals of care discussions documented. During the index hospitalization, 67.7% had goals of care discussions documented.
CONCLUSIONS: NLP offers opportunities to identify patients receiving palliative surgical procedures and can rapidly assess established end-of-life process measures with an accuracy approaching that of human coders.