The Center for Surgery and Public Health, Brigham and Women's Hospital, Boston, Massachusetts, USA; Department of Surgery, University of California, San Diego, La Jolla, California, USA.
Department of Surgery, Massachusetts General Hospital, Boston, Massachusetts, USA; Codman Center for Clinical Effectiveness, Massachusetts General Hospital, Boston, Massachusetts, USA.
J Pain Symptom Manage. 2020 Feb;59(2):225-232.e2. doi: 10.1016/j.jpainsymman.2019.09.017. Epub 2019 Sep 25.
The Trauma Quality Improvement Program Best Practice Guidelines recommend palliative care (PC) concurrent with restorative treatment for patients with life-threatening injuries. Measuring PC delivery is challenging: administrative data are nonspecific, and manual review is time intensive.
To identify PC delivery to patients with life-threatening trauma and compare the performance of natural language processing (NLP), a form of computer-assisted data abstraction, to administrative coding and gold standard manual review.
Patients 18 years and older admitted with life-threatening trauma were identified from two Level I trauma centers (July 2016-June 2017). Four PC process measures were examined during the trauma admission: code status clarification, goals-of-care discussion, PC consult, and hospice assessment. The performance of NLP and administrative coding were compared with manual review. Multivariable regression was used to determine patient and admission factors associated with PC delivery.
There were 76,791 notes associated with 2093 admissions. NLP identified PC delivery in 33% of admissions compared with 8% using administrative coding. Using NLP, code status clarification was most commonly documented (27%), followed by goals-of-care discussion (18%), PC consult (4%), and hospice assessment (4%). Compared with manual review, NLP performed more than 50 times faster and had a sensitivity of 93%, a specificity of 96%, and an accuracy of 95%. Administrative coding had a sensitivity of 21%, a specificity of 92%, and an accuracy of 68%. Factors associated with PC delivery included older age, increased comorbidities, and longer intensive care unit stay.
NLP performs with similar accuracy with manual review but with improved efficiency. NLP has the potential to accurately identify PC delivery and benchmark performance of best practice guidelines.
创伤质量改进计划最佳实践指南建议在为生命垂危的患者提供恢复性治疗的同时提供姑息治疗（PC）。衡量 PC 的实施情况具有挑战性：行政数据不具体，手动审查需要大量时间。
确定为生命垂危的创伤患者提供 PC，并比较自然语言处理（NLP），一种计算机辅助数据提取形式，与行政编码和黄金标准手动审查的性能。
从两个一级创伤中心（2016 年 7 月至 2017 年 6 月）确定 18 岁及以上患有生命垂危创伤的患者。在创伤住院期间检查了四个 PC 过程指标：明确患者的治疗意愿、讨论治疗目标、PC 咨询和临终关怀评估。比较 NLP 和行政编码与手动审查的性能。多变量回归用于确定与 PC 实施相关的患者和入院因素。
有 76791 份与 2093 次入院相关的记录。NLP 确定 33%的入院病例中有 PC 实施，而使用行政编码则为 8%。使用 NLP，最常记录的是明确患者的治疗意愿（27%），其次是讨论治疗目标（18%）、PC 咨询（4%）和临终关怀评估（4%）。与手动审查相比，NLP 的速度快 50 多倍，具有 93%的敏感性、96%的特异性和 95%的准确性。行政编码的敏感性为 21%，特异性为 92%，准确性为 68%。与 PC 实施相关的因素包括年龄较大、合并症增多和 ICU 住院时间延长。
NLP 的性能与手动审查相似，但效率更高。NLP 有可能准确识别 PC 的实施情况，并为最佳实践指南的实施情况提供基准。
