Charlotta Lindvall1,2, Elizabeth J Lilley3,4, Sophia N Zupanc1, Isabel Chien1,5, Brooks V Udelsman6, Anne Walling7,8, Zara Cooper4, James A Tulsky1,2. 1. 1 Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, Massachusetts. 2. 2 Division of Palliative Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts. 3. 3 Department of Surgery, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey. 4. 4 Department of Surgery, Brigham and Women's Hospital, Boston, Massachusetts. 5. 5 Department of Electrical Engineering and Computer Science, CSAIL, MIT, Cambridge, Massachusetts. 6. 6 Department of Surgery, Massachusetts General Hospital, Boston, Massachusetts. 7. 7 Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California. 8. 8 Palliative Care, VA Greater Los Angeles Healthcare System, Los Angeles, California.
Abstract
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.
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 cancerpatients receiving palliative gastrostomy and (2) assess documentation of end-of-life process measures in the EHR. DESIGN/ SETTING: Retrospective cohort study of 302 adult cancerpatients 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 cancerpatients 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.
Entities:
Keywords:
natural language processing; palliative care measures; venting gastrostomy tube
Authors: Jill M Steiner; Christina Morse; Robert Y Lee; J Randall Curtis; Ruth A Engelberg Journal: J Pain Symptom Manage Date: 2020-06-26 Impact factor: 3.612
Authors: Joseph A Greer; Beverly Moy; Areej El-Jawahri; Vicki A Jackson; Mihir Kamdar; Juliet Jacobsen; Charlotta Lindvall; Jennifer A Shin; Simone Rinaldi; Heather A Carlson; Angela Sousa; Emily R Gallagher; Zhigang Li; Samantha Moran; Magaret Ruddy; Maya V Anand; Julia E Carp; Jennifer S Temel Journal: J Natl Compr Canc Netw Date: 2022-02 Impact factor: 11.908
Authors: Anahita Davoudi; Hegler Tissot; Abigail Doucette; Peter E Gabriel; Ravi Parikh; Danielle L Mowery; Stephen P Miranda Journal: AMIA Annu Symp Proc Date: 2022-05-23
Authors: Tamryn F Gray; Anne Kwok; Khuyen M Do; Sandra Zeng; Edward T Moseley; Yasser M Dbeis; Renato Umeton; James A Tulsky; Areej El-Jawahri; Charlotta Lindvall Journal: JMIR Med Inform Date: 2022-06-15
Authors: Alison M Uyeda; J Randall Curtis; Ruth A Engelberg; Lyndia C Brumback; Yue Guo; James Sibley; William B Lober; Trevor Cohen; Janaki Torrence; Joanna Heywood; Sudiptho R Paul; Erin K Kross; Robert Y Lee Journal: J Pain Symptom Manage Date: 2022-02-16 Impact factor: 5.576