Literature DB >> 31216200

Technologist Productivity and Accuracy in Assigning Protocols for Abdominal CT and MRI Examinations at an Academic Medical Center: Implications for Physician Workload.

Daniel I Glazer1, David P Alper2, Leslie K Lee1, Rose L Wach3, Stuart M Hooton3, Giles W Boland1, Ramin Khorasani1,2.   

Abstract

OBJECTIVE. The purpose of this study was to evaluate the technologist productivity and accuracy in assigning protocols for abdominal CT and MRI examinations compared with a standard work flow whereby protocols are assigned by physicians. MATERIALS AND METHODS. In this quality improvement project at a large academic medical center, two CT technologists and two MRI technologists assigned protocols for examinations during a 15-week study period. The primary outcome measure was mean number of protocols assigned by technologists per hour. Secondary outcome measures were proportion of examinations with protocols assigned by technologists and rate of filing of quality assurance reports for protocols completed by technologists. A two-tailed t test was used to compare mean number of protocols; a chi-square test was used to compare proportions between CT and MRI. RESULTS. The mean number of protocols assigned by technologists per hour was not different between CT and MRI (CT, 22/h; MRI, 19/h; p = 0.28). CT and MRI technologist protocols accounted for 1650 of 4867 (33.9%) CT examinations (range, 23-275 per week) and 569 of 2388 (23.8%) MRI examinations (range, 0-95 per week) (p < 0.001). Radiologist quality assurance reports on inaccurate protocols were rare: three for CT (3/1650 [0.18%]), five for MRI (5/569 [0.88%]) (p = 0.017). A retrospective review of randomly selected CT and MRI protocols revealed no errors (80/80 correct). No patients were called back for repeat imaging due to protocol error. CONCLUSION. Technologists can efficiently and accurately assign protocols for abdominal CT and MRI examinations at an academic medical center, leading to increased radiologist time spent on other value-added activities.

Entities:  

Keywords:  CT; MRI; protocols; quality; technologists; work flow optimization

Year:  2019        PMID: 31216200     DOI: 10.2214/AJR.19.21353

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


  1 in total

1.  Automatic discrimination of different sequences and phases of liver MRI using a dense feature fusion neural network: a preliminary study.

Authors:  Shu-Hui Wang; Jing Du; Hui Xu; Dawei Yang; Yuxiang Ye; Yinan Chen; Yajing Zhu; Te Ba; Chunwang Yuan; Zheng-Han Yang
Journal:  Abdom Radiol (NY)       Date:  2021-05-31
  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.