Natural Language Processing Accurately Measures Adherence to Best Practice Guidelines for Palliative Care in Trauma

Abstract

ContextThe 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. ObjectivesTo 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. MethodsPatients 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. ResultsThere 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. ConclusionNLP 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.