Literature DB >> 28050717

Improving Patient Safety: Avoiding Unread Imaging Exams in the National VA Enterprise Electronic Health Record.

Sarah Bastawrous1,2, Benjamin Carney3.   

Abstract

In the current digital and filmless age of radiology, rates of unread radiology exams remain low, however, may still exist in unique environments. Veterans Affairs (VA) health care systems may experience higher rates of unread exams due to coexistence of Veterans Health Information Systems and Technology Architecture (VistA) imaging and commercial picture archiving and communication systems (PACS). The purpose of this patient safety initiative was to identify any unread exams and causes leading to unread exams. Following approval by departmental quality assurance committee, a comprehensive review was performed of all radiology exams within VistA imaging from July 1, 2009 to June 30, 2014 to identify unread radiology exams. Over the 5-year period, the total unread exam rate was calculated to be 0.17%, with the highest yearly unread exam rate of 0.25%. The leading majority of unread exam type was plain radiographs. Analysis revealed unfinished dictations, unassociated accession numbers, technologist errors, and inefficient radiologist work lists as top contributors to unread exams. Once unread radiology exams were discovered and the causes identified, valuable process changes were implemented within our department to ensure simultaneous tracking of all unread exams in VistA imaging as well as the commercial PACS.

Entities:  

Keywords:  Electronic Medical Record (EMR); PACS; Quality assurance; Unread exams

Mesh:

Year:  2017        PMID: 28050717      PMCID: PMC5422229          DOI: 10.1007/s10278-016-9937-2

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  6 in total

1.  The use of digital imaging and communications in medicine (DICOM) in the integration of imaging into the electronic patient record at the Department of Veterans Affairs.

Authors:  P M Kuzmak; R E Dayhoff
Journal:  J Digit Imaging       Date:  2000-05       Impact factor: 4.056

2.  Picture archiving and communication systems (PACS) and the loss of patient examination records.

Authors:  J J Smith; L Berlin
Journal:  AJR Am J Roentgenol       Date:  2001-06       Impact factor: 3.959

3.  PACS and unread images.

Authors:  Robert W Evers; David M Yousem; Tom Deluca; Norman J Beauchamp; Sidney Smith
Journal:  Acad Radiol       Date:  2002-11       Impact factor: 3.173

4.  Root Cause Analysis: Learning from Adverse Safety Events.

Authors:  Olga R Brook; Jonathan B Kruskal; Ronald L Eisenberg; David B Larson
Journal:  Radiographics       Date:  2015-10       Impact factor: 5.333

5.  From errors to process improvement.

Authors:  Robert L Siegle
Journal:  J Am Coll Radiol       Date:  2004-02       Impact factor: 5.532

6.  Economic and clinical impact of filmless operation in a multifacility environment.

Authors:  E L Siegel
Journal:  J Digit Imaging       Date:  1998-11       Impact factor: 4.056

  6 in total
  5 in total

1.  Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs.

Authors:  Jared A Dunnmon; Darvin Yi; Curtis P Langlotz; Christopher Ré; Daniel L Rubin; Matthew P Lungren
Journal:  Radiology       Date:  2018-11-13       Impact factor: 29.146

2.  Computed tomography technologist notes in PACS to radiologists: what are they telling us and how does it increase value?

Authors:  Corey T Jensen; Sanaz Javadi; Priya Bhosale; Ahmed W Moawad; Mohammed Saleh; Dhakshinamoorthy Ganeshan; Ajaykumar C Morani
Journal:  Abdom Radiol (NY)       Date:  2021-02-07

3.  MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports.

Authors:  Alistair E W Johnson; Tom J Pollard; Seth J Berkowitz; Nathaniel R Greenbaum; Matthew P Lungren; Chih-Ying Deng; Roger G Mark; Steven Horng
Journal:  Sci Data       Date:  2019-12-12       Impact factor: 6.444

4.  Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists.

Authors:  Pranav Rajpurkar; Jeremy Irvin; Robyn L Ball; Kaylie Zhu; Brandon Yang; Hershel Mehta; Tony Duan; Daisy Ding; Aarti Bagul; Curtis P Langlotz; Bhavik N Patel; Kristen W Yeom; Katie Shpanskaya; Francis G Blankenberg; Jayne Seekins; Timothy J Amrhein; David A Mong; Safwan S Halabi; Evan J Zucker; Andrew Y Ng; Matthew P Lungren
Journal:  PLoS Med       Date:  2018-11-20       Impact factor: 11.069

5.  PENet-a scalable deep-learning model for automated diagnosis of pulmonary embolism using volumetric CT imaging.

Authors:  Shih-Cheng Huang; Tanay Kothari; Imon Banerjee; Chris Chute; Robyn L Ball; Norah Borus; Andrew Huang; Bhavik N Patel; Pranav Rajpurkar; Jeremy Irvin; Jared Dunnmon; Joseph Bledsoe; Katie Shpanskaya; Abhay Dhaliwal; Roham Zamanian; Andrew Y Ng; Matthew P Lungren
Journal:  NPJ Digit Med       Date:  2020-04-24
  5 in total

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