Literature DB >> 26866338

Patient Recall Imaging in the Ambulatory Setting.

Soterios Gyftopoulos1,2,3, Danny Kim1,2,3, Eric Aaltonen1,2,3, Leora I Horwitz2,3,4.   

Abstract

OBJECTIVE: Recalling a patient to repeat a radiology examination is an adverse and, in certain cases, preventable event. Our objectives were to assess the rate of patient recalls for all imaging performed in the outpatient setting at our institution and to characterize the underlying reasons for the recalls.
MATERIALS AND METHODS: We performed a retrospective review of all repeat imaging requests for an inadequate initial imaging study between January 2012 and March 2015.
RESULTS: We identified 100 recall requests (mean, 2.6 requests per month), for an overall recall rate of approximately 1 in 8046 ambulatory studies and 1 in 1684 MRI studies. Nearly all recalls (98%) involved adults. A total of 95% of the recalls were for MRI studies. The most common reason for a patient recall request was an incomplete examination, making up 24% of all requests. The other causes were inadequate coverage of the area of interest (22%), protocoling errors (20%), poor imaging quality (15%), additional imaging to clarify a finding (11%), insufficient contrast visualization (7%), and incorrect patient information (1%).
CONCLUSION: We found that patient recalls for imaging in the outpatient setting at our institution are not common. When recalls did occur, they were most often related to the acquisition of MR images. Improved technologist education on MRI protocoling and enhanced communication between ordering clinicians and radiologists to clarify the purpose of imaging might reduce the need for repeat ambulatory imaging.

Entities:  

Keywords:  MRI; ambulatory imaging; appropriate imaging; health services; patient recall

Mesh:

Year:  2016        PMID: 26866338     DOI: 10.2214/AJR.15.15268

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


  2 in total

1.  Efficiency Improvement in a Busy Radiology Practice: Determination of Musculoskeletal Magnetic Resonance Imaging Protocol Using Deep-Learning Convolutional Neural Networks.

Authors:  Young Han Lee
Journal:  J Digit Imaging       Date:  2018-10       Impact factor: 4.056

Review 2.  Automated Protocoling for MRI Exams-Challenges and Solutions.

Authors:  Jonas Denck; Oliver Haas; Jens Guehring; Andreas Maier; Eva Rothgang
Journal:  J Digit Imaging       Date:  2022-08-30       Impact factor: 4.903

  2 in total

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