STUDY OBJECTIVE: As use of radiology studies increases, there is a concurrent increase in incidental findings (eg, lung nodules) for which the radiologist issues recommendations for additional imaging for follow-up. Busy emergency physicians may be challenged to carefully communicate recommendations for additional imaging not relevant to the patient's primary evaluation. The emergence of electronic health records and natural language processing algorithms may help address this quality gap. We seek to describe recommendations for additional imaging from our institution and develop and validate an automated natural language processing algorithm to reliably identify recommendations for additional imaging. METHODS: We developed a natural language processing algorithm to detect recommendations for additional imaging, using 3 iterative cycles of training and validation. The third cycle used 3,235 radiology reports (1,600 for algorithm training and 1,635 for validation) of discharged emergency department (ED) patients from which we determined the incidence of discharge-relevant recommendations for additional imaging and the frequency of appropriate discharge documentation. The test characteristics of the 3 natural language processing algorithm iterations were compared, using blinded chart review as the criterion standard. RESULTS: Discharge-relevant recommendations for additional imaging were found in 4.5% (95% confidence interval [CI] 3.5% to 5.5%) of ED radiology reports, but 51% (95% CI 43% to 59%) of discharge instructions failed to note those findings. The final natural language processing algorithm had 89% (95% CI 82% to 94%) sensitivity and 98% (95% CI 97% to 98%) specificity for detecting recommendations for additional imaging. For discharge-relevant recommendations for additional imaging, sensitivity improved to 97% (95% CI 89% to 100%). CONCLUSION: Recommendations for additional imaging are common, and failure to document relevant recommendations for additional imaging in ED discharge instructions occurs frequently. The natural language processing algorithm's performance improved with each iteration and offers a promising error-prevention tool.
STUDY OBJECTIVE: As use of radiology studies increases, there is a concurrent increase in incidental findings (eg, lung nodules) for which the radiologist issues recommendations for additional imaging for follow-up. Busy emergency physicians may be challenged to carefully communicate recommendations for additional imaging not relevant to the patient's primary evaluation. The emergence of electronic health records and natural language processing algorithms may help address this quality gap. We seek to describe recommendations for additional imaging from our institution and develop and validate an automated natural language processing algorithm to reliably identify recommendations for additional imaging. METHODS: We developed a natural language processing algorithm to detect recommendations for additional imaging, using 3 iterative cycles of training and validation. The third cycle used 3,235 radiology reports (1,600 for algorithm training and 1,635 for validation) of discharged emergency department (ED) patients from which we determined the incidence of discharge-relevant recommendations for additional imaging and the frequency of appropriate discharge documentation. The test characteristics of the 3 natural language processing algorithm iterations were compared, using blinded chart review as the criterion standard. RESULTS: Discharge-relevant recommendations for additional imaging were found in 4.5% (95% confidence interval [CI] 3.5% to 5.5%) of ED radiology reports, but 51% (95% CI 43% to 59%) of discharge instructions failed to note those findings. The final natural language processing algorithm had 89% (95% CI 82% to 94%) sensitivity and 98% (95% CI 97% to 98%) specificity for detecting recommendations for additional imaging. For discharge-relevant recommendations for additional imaging, sensitivity improved to 97% (95% CI 89% to 100%). CONCLUSION: Recommendations for additional imaging are common, and failure to document relevant recommendations for additional imaging in ED discharge instructions occurs frequently. The natural language processing algorithm's performance improved with each iteration and offers a promising error-prevention tool.
Authors: Gaurav Trivedi; Charmgil Hong; Esmaeel R Dadashzadeh; Robert M Handzel; Harry Hochheiser; Shyam Visweswaran Journal: Int J Med Inform Date: 2019-06-06 Impact factor: 4.046
Authors: Elizabeth H Dibble; David W Swenson; Cynthia Cobb; Timothy J Paul; Andrew E Karn; David C Portelli; Jonathan S Movson Journal: Emerg Radiol Date: 2016-10-14
Authors: Timothy D Imler; Justin Morea; Charles Kahi; Eric A Sherer; Jon Cardwell; Cynthia S Johnson; Huiping Xu; Dennis Ahnen; Fadi Antaki; Christopher Ashley; Gyorgy Baffy; Ilseung Cho; Jason Dominitz; Jason Hou; Mark Korsten; Anil Nagar; Kittichai Promrat; Douglas Robertson; Sameer Saini; Amandeep Shergill; Walter Smalley; Thomas F Imperiale Journal: Am J Gastroenterol Date: 2015-03-10 Impact factor: 10.864
Authors: Florian Jungmann; G Arnhold; B Kämpgen; T Jorg; C Düber; P Mildenberger; R Kloeckner Journal: J Digit Imaging Date: 2020-08 Impact factor: 4.056
Authors: Jonathan Shelver; Chris H Wendt; Melissa McClure; Brian Bell; Angela E Fabbrini; Thomas Rector; Kathryn Rice Journal: J Am Coll Radiol Date: 2017-04-21 Impact factor: 5.532