Definition of a Novel Imaging Quality Measure for the Evaluation of Emergency Department Patients with Suspected Pulmonary Embolism: Use of AI NLP to Validate and Automate It |Biomedgrid

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 quality measure is needed to precisely assess efficiency of CTPA utilization, normalized to numbers of patients presenting with suspected PE and based on patient signs and symptoms. This study used Artificial Intelligence approaches such as ontology-driven 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 PEsuggestive symptoms based on four Concept Unique Identifiers (CUIs: shortness of breath, chest pain, pleuritic chest pain, anterior pleuritic chest pain).