Literature DB >> 27714473

Radiologists' Variation of Time to Read Across Different Procedure Types.

Daniel Forsberg1,2, Beverly Rosipko3, Jeffrey L Sunshine3.   

Abstract

The workload of US radiologists has increased over the past two decades as measured through total annual relative value units (RVUs). This increase in RVUs generated suggests that radiologists' productivity has increased. However, true productivity (output unit per input unit; RVU per time) is at large unknown since actual time required to interpret and report a case is rarely recorded. In this study, we analyzed how the time to read a case varies between radiologists over a set of different procedure types by retrospectively extracting reading times from PACS usage logs. Specifically, we tested two hypotheses that; i) relative variation in time to read per procedure type increases as the median time to read a procedure type increases, and ii) relative rankings in terms of median reading speed for individual radiologists are consistent across different procedure types. The results that, i) a correlation of -0.25 between the coefficient of variation and median time to read and ii) that only 12 out of 46 radiologists had consistent rankings in terms of time to read across different procedure types, show both hypotheses to be without support. The results show that workload distribution will not follow any general rule for a radiologist across all procedures or a general rule for a specific procedure across many readers. Rather the findings suggest that improved overall practice efficiency can be achieved only by taking into account radiologists' individual productivity per procedure type when distributing unread cases.

Keywords:  Efficiency; PACS; Productivity; Radiology workflow

Mesh:

Year:  2017        PMID: 27714473      PMCID: PMC5267601          DOI: 10.1007/s10278-016-9911-z

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


  14 in total

1.  Measuring the academic radiologist's clinical productivity: survey results for subspecialty sections.

Authors:  R L Arenson; Y Lu; S C Elliott; C Jovais; D E Avrin
Journal:  Acad Radiol       Date:  2001-06       Impact factor: 3.173

2.  Productivity of radiologists in 1997: estimates based on analysis of resource-based relative value units.

Authors:  P M Conoley
Journal:  AJR Am J Roentgenol       Date:  2000-09       Impact factor: 3.959

3.  Radiology groups' workload in relative value units and factors affecting it.

Authors:  J H Sunshine; J H Burkhardt
Journal:  Radiology       Date:  2000-03       Impact factor: 11.105

4.  Measuring and managing radiologist productivity, part 2: beyond the clinical numbers.

Authors:  Richard Duszak; Lawrence R Muroff
Journal:  J Am Coll Radiol       Date:  2010-07       Impact factor: 5.532

5.  Workload of radiologists in United States in 2006-2007 and trends since 1991-1992.

Authors:  Mythreyi Bhargavan; Adam H Kaye; Howard P Forman; Jonathan H Sunshine
Journal:  Radiology       Date:  2009-06-09       Impact factor: 11.105

6.  Automating radiologist workflow, part 2: hands-free navigation.

Authors:  Bruce Reiner
Journal:  J Am Coll Radiol       Date:  2008-11       Impact factor: 5.532

7.  Factors Affecting Radiologist's PACS Usage.

Authors:  Daniel Forsberg; Beverly Rosipko; Jeffrey L Sunshine
Journal:  J Digit Imaging       Date:  2016-12       Impact factor: 4.056

8.  The Resource-Based Relative Value Scale. Toward the development of an alternative physician payment system.

Authors:  W C Hsiao; P Braun; E R Becker; S R Thomas
Journal:  JAMA       Date:  1987-08-14       Impact factor: 56.272

9.  Productivity of radiologists: estimates based on analysis of relative value units.

Authors:  P M Conoley; S W Vernon
Journal:  AJR Am J Roentgenol       Date:  1991-12       Impact factor: 3.959

10.  Design, implementation, and assessment of a radiology workflow management system.

Authors:  Mark J Halsted; Craig M Froehle
Journal:  AJR Am J Roentgenol       Date:  2008-08       Impact factor: 3.959

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  7 in total

1.  Integration of fully automated computer-aided pulmonary nodule detection into CT pulmonary angiography studies in the emergency department: effect on workflow and diagnostic accuracy.

Authors:  Amirhossein Mozaffary; Tugce Agirlar Trabzonlu; Pamela Lombardi; Adeel R Seyal; Rishi Agrawal; Vahid Yaghmai
Journal:  Emerg Radiol       Date:  2019-07-27

2.  Who PACS a Punch? The Role of the Picture Archiving and Communication System/Radiology Information System (PACS/RIS) in Quantifying Experiential Learning in Radiology Residency.

Authors:  Abraham Gerhardus Wilhelmus Greyling; Richard Denys Pitcher
Journal:  J Digit Imaging       Date:  2018-12       Impact factor: 4.056

Review 3.  Overview of Noninterpretive Artificial Intelligence Models for Safety, Quality, Workflow, and Education Applications in Radiology Practice.

Authors:  Yasasvi Tadavarthi; Valeria Makeeva; William Wagstaff; Henry Zhan; Anna Podlasek; Neil Bhatia; Marta Heilbrun; Elizabeth Krupinski; Nabile Safdar; Imon Banerjee; Judy Gichoya; Hari Trivedi
Journal:  Radiol Artif Intell       Date:  2022-02-02

4.  Artificial intelligence-based vessel suppression for detection of sub-solid nodules in lung cancer screening computed tomography.

Authors:  Ramandeep Singh; Mannudeep K Kalra; Fatemeh Homayounieh; Chayanin Nitiwarangkul; Shaunagh McDermott; Brent P Little; Inga T Lennes; Jo-Anne O Shepard; Subba R Digumarthy
Journal:  Quant Imaging Med Surg       Date:  2021-04

Review 5.  Artificial Intelligence for the Future Radiology Diagnostic Service.

Authors:  Seong K Mun; Kenneth H Wong; Shih-Chung B Lo; Yanni Li; Shijir Bayarsaikhan
Journal:  Front Mol Biosci       Date:  2021-01-28

6.  Improved assessment of middle ear recurrent/residual cholesteatomas using temporal subtraction CT.

Authors:  Akira Baba; Satoshi Matsushima; Takeshi Fukuda; Hideomi Yamauchi; Hiroaki Fujioka; Jun Hasumi; Shohei Yoshimoto; Tomokazu Shoji; Sho Kurihara; Yutaka Yamamoto; Hiromi Kojima; Ryo Kurokawa; Mariko Kurokawa; Yoshiaki Ota; Hiroya Ojiri
Journal:  Jpn J Radiol       Date:  2021-10-24       Impact factor: 2.374

7.  Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation.

Authors:  Minghuan Wang; Chen Xia; Lu Huang; Shabei Xu; Chuan Qin; Jun Liu; Ying Cao; Pengxin Yu; Tingting Zhu; Hui Zhu; Chaonan Wu; Rongguo Zhang; Xiangyu Chen; Jianming Wang; Guang Du; Chen Zhang; Shaokang Wang; Kuan Chen; Zheng Liu; Liming Xia; Wei Wang
Journal:  Lancet Digit Health       Date:  2020-09-22
  7 in total

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