Search

Background: Natural language processing (NLP), a form of computer-assisted data abstraction, rapidly identifies serious illness communication domains such as code-status confirmation and goals of care (GOC) discussions within free-text notes, using a codebook of phrases. Differences in the phrases associated with palliative care for patients with different types of illness are unknown. Objective: To compare communication of code-status clarification and GOC discussions between patients with advanced pancreatic cancer undergoing palliative procedures and patients admitted with life-threatening trauma. Design: Retrospective cohort study. Setting/Subjects: Patients with in-hospital admissions within two academic medical centers. Measurements: Sensitivity and specificity of NLP-identified communication domains compared with manual review. Results: Among patients with advanced pancreatic cancer (n = 523), NLP identified code-status clarification in 54% of admissions and GOC discussions in 49% of admissions. The sensitivity and specificity for code-status clarification were 94% and 99% respectively, while the sensitivity and specificity for a GOC discussion were 93% and 100%, respectively. Using the same codebook in patients with life-threatening trauma (n = 2093), NLP identified code-status clarification in 25.9% of admissions and GOC discussions in 6.3% of admissions. While NLP identification had 100% specificity, the sensitivity for code-status clarification and GOC discussion was reduced to 86% and 50%, respectively. Adding dynamic phrases such as "ongoing discussions" and phrases related to "family meetings" increased the sensitivity of the NLP codebook for code status to 98% and for GOC discussions to 100%. Conclusions: Communication of code status and GOC differ between patients with advanced cancer and those with life-threatening trauma. Recognition of these differences can aid in identification in patterns of palliative care delivery. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: The surprise question is a validated tool for identifying patients with increased risk of death within one year who could, therefore, benefit from palliative care. However, its utility in surgery is unknown. Objective: We sought to determine whether the surprise question predicted 12-month mortality in older emergency general surgery patients. Design: This was a prospective cohort study. Setting/Subjects: Emergency general surgery attendings and surgical residents in or beyond their third year of training at a single tertiary care academic hospital from January to July 2014. Measurements: Surgeons responded to the surprise question within 72 hours of evaluating patients, ≥65 years, hospitalized with an acute surgical condition. Patient data, including demographic and clinical characteristics, were extracted from the medical record. Mortality within 12 months of initial evaluation was determined by using Social Security death data. Results: Ten attending surgeons and 18 surgical residents provided 163 responses to the surprise question for 119 patients: 60% of responses were 'No, I would not be surprised' and 40% were 'Yes, I would be surprised.' A 'No' response was associated with increased odds of death within 12 months in binary logistic regression (OR 4.8 [95% CI 2.1-11.1]). Conclusions: The surprise question is a valuable tool for identifying older patients with higher risk of death, and it may be a useful screening criterion for older emergency general surgery patients who would benefit from palliative care evaluation. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented regarding a study that quantified clinical indicators of hospitalized older adults for palliative care while under acute surgical conditions for integrating palliative care interventions in emergency surgical practice.