Literature DB >> 31515753

Viewing Imaging Studies: How Patient Location and Imaging Site Affect Referring Physicians.

Fatemeh Homayounieh1, Ramandeep Singh2, Tianqi Chen3, Ellen J Sugarman4, Thomas J Schultz4, Subba R Digumarthy2, Keith J Dreyer2, Mannudeep K Kalra2.   

Abstract

The purpose of this study was to assess if clinical indications, patient location, and imaging sites predict the viewing pattern of referring physicians for CT and MR of the head, chest, and abdomen. Our study included 166,953 CT/MR images of head/chest/abdomen in 2016-2017 in the outpatient (OP, n = 83,981 CT/MR), inpatient (IP, n = 51,052), and emergency (ED, n = 31,920) settings. There were 125,329 CT/MR performed in the hospital setting and 41,624 in one of the nine off-campus locations. We extracted information regarding body region (head/chest/abdomen), patient location, and imaging site from the electronic medical records (EPIC). We recorded clinical indications and the number of times referring physicians viewed CT/MR (defined as the number of separate views of imaging in the EPIC). Data were analyzed with the Microsoft SQL and SPSS statistical software. About 33% of IP CT and MR studies are viewed > 6 times compared to 7% for OP and 19% of ED studies (p < 0.001). Conversely, most OP studies (55%) were viewed 1-2 times only, compared to 21% for IP and 38% for ED studies (p < 0.001). In-hospital exams are viewed (≥ 6 views; 39% studies) more frequently than off-campus imaging (≥ 6 views; 17% studies) (p < 0.001). For head CT/MR, certain clinical indications (i.e., stroke) had higher viewing rates compared to other clinical indications such as malignancy, headache, and dizziness. Conversely, for chest CT, dyspnea-hypoxia had much higher viewing rates (> 6 times) in IP (55%) and ED (46%) than in OP settings (22%). Patient location and imaging site regardless of clinical indications have a profound effect on viewing patterns of referring physicians. Understanding viewing patterns of the referring physicians can help guide interpretation priorities and finding communication for imaging exams based on patient location, imaging site, and clinical indications. The information can help in the efficient delivery of patient care.

Entities:  

Keywords:  CT; Imaging use; MR; Patient location; Radiology reports; Referring physician

Year:  2020        PMID: 31515753      PMCID: PMC7165203          DOI: 10.1007/s10278-019-00279-z

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


  9 in total

1.  Enterprise utilization of "always on-line" diagnostic study archive.

Authors:  Kevin W McEnery; Charles T Suitor; Stephen K Thompson; Jeffrey S Shepard; William A Murphy
Journal:  J Digit Imaging       Date:  2002-03-20       Impact factor: 4.056

2.  Audit of radiology communication systems for critical, urgent, and unexpected significant findings.

Authors:  K A Duncan; K J Drinkwater; N Dugar; D C Howlett
Journal:  Clin Radiol       Date:  2015-12-28       Impact factor: 2.350

3.  Objective Comparison Using Guideline-based Query of Conventional Radiological Reports and Structured Reports.

Authors:  Máté E Maros; Ralf Wenz; Alex Förster; Matthias F Froelich; Christoph Groden; Wieland H Sommer; Stefan O Schönberg; Thomas Henzler; Holger Wenz
Journal:  In Vivo       Date:  2018 Jul-Aug       Impact factor: 2.155

4.  Actionable findings and the role of IT support: report of the ACR Actionable Reporting Work Group.

Authors:  Paul A Larson; Lincoln L Berland; Brent Griffith; Charles E Kahn; Lawrence A Liebscher
Journal:  J Am Coll Radiol       Date:  2014-01-30       Impact factor: 5.532

5.  Communicating Radiology Test Results: Are Our Phone Calls Excessive, Just Right, or Not Enough?

Authors:  Zeeshaan S Bhatti; Richard K J Brown; Ella A Kazerooni; Matthew S Davenport
Journal:  Acad Radiol       Date:  2017-11-23       Impact factor: 3.173

6.  Determining Adherence to Follow-up Imaging Recommendations.

Authors:  Thusitha Mabotuwana; Vadiraj Hombal; Sandeep Dalal; Christopher S Hall; Martin Gunn
Journal:  J Am Coll Radiol       Date:  2018-03       Impact factor: 5.532

7.  Assisting radiologists with reporting urgent findings to referring physicians: A machine learning approach to identify cases for prompt communication.

Authors:  Xing Meng; Craig H Ganoe; Ryan T Sieberg; Yvonne Y Cheung; Saeed Hassanpour
Journal:  J Biomed Inform       Date:  2019-04-05       Impact factor: 6.317

Review 8.  Structured Reporting in Clinical Routine.

Authors:  Daniel Pinto Dos Santos; Johann-Martin Hempel; Peter Mildenberger; Roman Klöckner; Thorsten Persigehl
Journal:  Rofo       Date:  2018-08-13

9.  A software system to collect expert relevance ratings of medical record items for specific clinical tasks.

Authors:  H Benjamin Harvey; Arun Krishnaraj; Tarik K Alkasab
Journal:  JMIR Med Inform       Date:  2014-02-28
  9 in total

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