A data-driven approach for quality assessment of radiologic interpretations. (25th November 2015)

• Record Type: Journal Article
• Authors: Hsu, William; Han, Simon X; Arnold, Corey W; Bui, Alex AT; Enzmann, Dieter R
• Is Part Of: Journal of the American Medical Informatics Association. Volume 23:Number e1(2016:Apr.)
• Page Start: e152
• Page End: e156
• Publication Date: 2015-11-25
• Subjects: quality improvement -- radiology -- pathology -- information storage and retrieval -- mammography; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocv161
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Given the increasing emphasis on delivering high-quality, cost-efficient healthcare, improved methodologies are needed to measure the accuracy and utility of ordered diagnostic examinations in achieving the appropriate diagnosis. Here, we present a data-driven approach for performing automated quality assessment of radiologic interpretations using other clinical information (e.g., pathology) as a reference standard for individual radiologists, subspecialty sections, imaging modalities, and entire departments. Downstream diagnostic conclusions from the electronic medical record are utilized as "truth" to which upstream diagnoses generated by radiology are compared. The described system automatically extracts and compares patient medical data to characterize concordance between clinical sources. Initial results are presented in the context of breast imaging, matching 18 101 radiologic interpretations with 301 pathology diagnoses and achieving a precision and recall of 84% and 92%, respectively. The presented data-driven method highlights the challenges of integrating multiple data sources and the application of information extraction tools to facilitate healthcare quality improvement.