Information technology tools can automate imaging measures for Emergency Department patients with suspected pulmonary embolism

CT pulmonary angiography (CTPA) utilization rates for patients with suspected pulmonary embolism (PE) in the Emergency Department (ED) have increased steadily with associated radiation exposure, costs and overdiagnosis. A new measure is needed to more precisely assess efficiency of CTPA utilization normalized to numbers of patients presenting with suspected PE, based on patient signs and symptoms. This study used natural language processing (NLP) to develop, automate, and validate SPE (“Suspected Pulmonary Embolism [PE]”), a measure determining CTPA utilization in ED patients with suspected PE. This retrospective study was conducted 4/1/2013-3/31/2014 in a Level-1 ED. A NLP engine processed “Chief Complaint” sections of ED documentation, identifying patients with PE-suggestive symptoms based on four Concept Unique Identifiers (CUIs: shortness of breath, chest pain, pleuritic chest pain, anterior pleuritic chest pain). SPE was defined as proportion of ED visits for patients with potential PE undergoing CTPA. Manual reviews determined specificity, sensitivity and negative predictive value (NPV). Among 5,768 ED visits with 1+SPE CUI, and 795 CTPAs performed, SPE=13.8% (795/5,768). NLP identified patients with relevant CUIs with specificity=0.94 [95%CI (0.89-0.96)]; sensitivity=0.73 [95%CI (0.45-0.92)]; NPV=0.98. Using NLP on ED documentation can identify patients with suspected PE to computate a more clinically-relevant CTPA measure. This measure might then be used in an audit-and-feedback process to increase the appropriateness of imaging of patients with suspected PE in the ED.