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Natural Language Processing to Assess End-of-Life Quality Indicators in Cancer Patients Receiving Palliative Surgery.

Natural Language Processing to Assess End-of-Life Quality Indicators in Cancer Patients Receiving Palliative Surgery.

Natural Language Processing to Assess End-of-Life Quality Indicators in Cancer Patients Receiving Palliative Surgery.

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Lindvall C1,2Lilley EJ3,4Zupanc SN1Chien I1,5Udelsman BV6Walling A7,8Cooper Z4Tulsky JA1,2.

Author information:

  1. Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, Massachusetts.
  2. Division of Palliative Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts.
  3. Department of Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey.
  4. Department of Surgery, Brigham and Women’s Hospital, Boston, Massachusetts.
  5. Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, Massachusetts.
  6. Department of Surgery, Massachusetts General Hospital, Boston, Massachusetts.
  7. Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
  8. 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.

DOI: 10.1089/jpm.2018.0326
PMID: 30328764