Marta E Heilbrun1, Brian E Chapman2, Evan Narasimhan3, Neel Patel3, Danielle Mowery4. 1. Department of Radiology and imaging Sciences, Emory University School of Medicine, Atlanta, Georgia. Electronic address: marta.heilbrun@emory.edu. 2. Department of Radiology and Imaging Sciences, University of Utah School of Medicine, Salt Lake City, Utah. 3. Department of Diagnostic Radiology, Oregon Health and Science University, Portland, Oregon. 4. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
Abstract
OBJECTIVE: Time-sensitive communication of critical imaging findings like pneumothorax or pulmonary embolism to referring physicians is essential for patient safety. The definitive communication is the radiology free-text report. Quality assurance initiatives require that institutions audit these communications, a time-intensive manual task. We propose using a rule-based natural language processing system to improve the process for auditing critical findings communications. METHODS: We present a pilot assessment of the feasibility of using an automated critical finding identification system to assist quality assurance teams' evaluation of critical findings communication compliance. Our assessment is based on chest imaging reports. Critical findings are identified in radiology reports using pyConTextNLP, an open source Python implementation of the ConText algorithm. RESULTS: In our test set, there were 75 reports with critical findings and 591 reports without critical findings. pyConTextNLP correctly identified 69 of the positive cases with 8 false-positives for a sensitivity of 0.92 and a specificity of 0.99. DISCUSSION: Natural language processing can provide valuable assistance to auditing critical findings communications.
OBJECTIVE: Time-sensitive communication of critical imaging findings like pneumothorax or pulmonary embolism to referring physicians is essential for patient safety. The definitive communication is the radiology free-text report. Quality assurance initiatives require that institutions audit these communications, a time-intensive manual task. We propose using a rule-based natural language processing system to improve the process for auditing critical findings communications. METHODS: We present a pilot assessment of the feasibility of using an automated critical finding identification system to assist quality assurance teams' evaluation of critical findings communication compliance. Our assessment is based on chest imaging reports. Critical findings are identified in radiology reports using pyConTextNLP, an open source Python implementation of the ConText algorithm. RESULTS: In our test set, there were 75 reports with critical findings and 591 reports without critical findings. pyConTextNLP correctly identified 69 of the positive cases with 8 false-positives for a sensitivity of 0.92 and a specificity of 0.99. DISCUSSION: Natural language processing can provide valuable assistance to auditing critical findings communications.
Authors: Allard W Olthof; Anne L M Leusveld; Jan Cees de Groot; Petra M C Callenbach; Peter M A van Ooijen Journal: J Med Syst Date: 2020-07-28 Impact factor: 4.460
Authors: Arlene Casey; Emma Davidson; Michael Poon; Hang Dong; Daniel Duma; Andreas Grivas; Claire Grover; Víctor Suárez-Paniagua; Richard Tobin; William Whiteley; Honghan Wu; Beatrice Alex Journal: BMC Med Inform Decis Mak Date: 2021-06-03 Impact factor: 2.796
Authors: Jianlin Shi; John F Hurdle; Stacy A Johnson; Jeffrey P Ferraro; David E Skarda; Samuel R G Finlayson; Matthew H Samore; Brian T Bucher Journal: Surgery Date: 2021-06-03 Impact factor: 4.348