Literature DB >> 17412207

Plain-radiographic image labeling: a process to improve clinical outcomes.

Kenneth T Aakre1, C Daniel Johnson.   

Abstract

PURPOSE: To determine the rate of film-labeling errors and to describe a process for improved plain-film image labeling and the clinical outcomes from this process improvement.
METHODS: Image-labeling errors (absent or incorrectly assigned left or right lateral identifier marker, absent or incorrect patient-identifying number, absent or incorrect examination date, incorrect marker placement, absent technologist initial marker, or incorrect body-part order) were measured among 2,536 consecutive plain-film radiographs over a 2-week period. Following a process improvement initiative based on failure mode effectiveness analysis, left-side and right-side indicator markers, patient demographics, and date labels were identified as the most common sources of error. An improvement initiative using larger and colored left and right lateral indicator markers, an automated process to label patient demographics, and direct patient verification of identification was begun. The numbers of labeling errors were again assessed in 2,421 consecutive plain radiographs over a 2-week period. The error rates before and after the improvement initiatives were compared.
RESULTS: Plain-radiographic labeling errors occurred in 62 of 2,536 (2.4%) images before the improvement initiative. Labeling errors were reduced to 17 of 2,421 (0.70%; 95% exact binomial confidence interval, 0.4% to 1.1%; P < .001, chi-square test) by using the improvement tools.
CONCLUSIONS: Plain radiographic image labeling can be improved using bar-code reading of patient demographic information, linked to patient Digital Imaging and Communications in Medicine modality work lists and image printing. Patient verification of demographic information is key and can be electronically managed. Lateral marker identification can be improved with larger (more easily read) and color-coded indicators.

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Year:  2006        PMID: 17412207     DOI: 10.1016/j.jacr.2006.07.005

Source DB:  PubMed          Journal:  J Am Coll Radiol        ISSN: 1546-1440            Impact factor:   5.532


  7 in total

1.  Detection and Correction of Laterality Errors in Radiology Reports.

Authors:  Young Han Lee; Jaemoon Yang; Jin-Suck Suh
Journal:  J Digit Imaging       Date:  2015-08       Impact factor: 4.056

2.  Deep Learning Method for Automated Classification of Anteroposterior and Posteroanterior Chest Radiographs.

Authors:  Tae Kyung Kim; Paul H Yi; Jinchi Wei; Ji Won Shin; Gregory Hager; Ferdinand K Hui; Haris I Sair; Cheng Ting Lin
Journal:  J Digit Imaging       Date:  2019-12       Impact factor: 4.056

3.  Increasing rate of detection of wrong-patient radiographs: use of photographs obtained at time of radiography.

Authors:  Srini Tridandapani; Senthil Ramamurthy; Samuel J Galgano; James M Provenzale
Journal:  AJR Am J Roentgenol       Date:  2013-04       Impact factor: 3.959

4.  A clinical audit of anatomical side marker use in a paediatric medical imaging department.

Authors:  Kate Barry; Saravana Kumar; Rebecca Linke; Emma Dawes
Journal:  J Med Radiat Sci       Date:  2016-05-25

5.  Deep-Learning-Based Semantic Labeling for 2D Mammography and Comparison of Complexity for Machine Learning Tasks.

Authors:  Paul H Yi; Abigail Lin; Jinchi Wei; Alice C Yu; Haris I Sair; Ferdinand K Hui; Gregory D Hager; Susan C Harvey
Journal:  J Digit Imaging       Date:  2019-08       Impact factor: 4.056

6.  A clinical audit of anatomical side marker use in a pediatric medical imaging department: A quantitative and qualitative investigation.

Authors:  Lilian Chung; Saravana Kumar; Joanne Oldfield; Maureen Phillips; Megan Stratfold
Journal:  PLoS One       Date:  2020-11-24       Impact factor: 3.240

Review 7.  Integrating patient digital photographs with medical imaging examinations.

Authors:  Senthil Ramamurthy; Pamela Bhatti; Chesnal D Arepalli; Mohamed Salama; James M Provenzale; Srini Tridandapani
Journal:  J Digit Imaging       Date:  2013-10       Impact factor: 4.056

  7 in total

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