Literature DB >> 31887096

Common Causes of Outpatient CT and MRI Callback Examinations: Opportunities for Improvement.

Amber L Liles1,2, Isaac R Francis1, Vivek Kalia1,2, John Kim1,2, Matthew S Davenport1,2,3.   

Abstract

OBJECTIVE. The purposes of this study were to investigate factors driving callback MRI and CT examinations and to discern opportunities for optimizing the patient experience by reducing future callbacks. MATERIALS AND METHODS. All consecutive outpatient CT and MRI callback examinations from October 2015 to October 2017 in four radiology subspecialties (cardiothoracic imaging, abdominal imaging, neuroradiology, musculoskeletal imaging) were reviewed at an academic quaternary care center. Callback details (modality, subspecialty, protocoling radiologist, protocol assigned, protocol performed, interpreting radiologist, and reason for callback) were recorded, and reason for callback was categorized. Callback rates were calculated and compared across subspecialties and modalities. RESULTS. There were 194 callbacks among 147,068 MRI and 195,578 CT examinations. The callback rate for MRI was approximately nine times that of CT (MRI, 0.114% [n = 168]; CT, 0.013% [n = 26]). The callback rate was highest for musculoskeletal radiology (CT, 0.090% [7/7802]; MRI, 0.265% [73/27501]; p < 0.0001). Of 65 subspecialty radiologists, nine initiated 52% (101/194) of all callback examinations, and 20 initiated 80% (155/194). One musculoskeletal radiologist was responsible for 11.8% (23/194) of all callbacks. The most common reasons for callbacks were protocol error (28% [55/194]), inadequate anatomic coverage (21% [40/194]), incomplete examination (13% [25/194]), and perceived suboptimal image quality (11% [22/194]). The three most common causes of callbacks (62% [120/194] of all callbacks) were largely preventable. CONCLUSION. Outpatient callback examinations are uncommon, occur more often for MRI than CT, and are often preventable. Callback proclivities likely vary between attending radiologists. Targeted improvement efforts may mitigate callbacks.

Entities:  

Keywords:  callback; patient experience; protocol; quality; recall

Mesh:

Year:  2019        PMID: 31887096     DOI: 10.2214/AJR.19.21839

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  3 in total

1.  Stratification of cystic renal masses into benign and potentially malignant: applying machine learning to the bosniak classification.

Authors:  Nityanand Miskin; Lei Qin; Shanna A Matalon; Sree H Tirumani; Francesco Alessandrino; Stuart G Silverman; Atul B Shinagare
Journal:  Abdom Radiol (NY)       Date:  2020-07-01

2.  Deep CTS: a Deep Neural Network for Identification MRI of Carpal Tunnel Syndrome.

Authors:  Haiying Zhou; Qi Bai; Xianliang Hu; Ahmad Alhaskawi; Yanzhao Dong; Zewei Wang; Binjie Qi; Jianyong Fang; Vishnu Goutham Kota; Mohamed Hasan Abdulla Hasa Abdulla; Sohaib Hasan Abdullah Ezzi; Hui Lu
Journal:  J Digit Imaging       Date:  2022-06-03       Impact factor: 4.056

3.  CT texture analysis predicts abdominal aortic aneurysm post-endovascular aortic aneurysm repair progression.

Authors:  Ning Ding; Yunxiu Hao; Zhiwei Wang; Xiao Xuan; Lingyan Kong; Huadan Xue; Zhengyu Jin
Journal:  Sci Rep       Date:  2020-07-23       Impact factor: 4.379

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.