Literature DB >> 31289979

Unlocking Radiology Reporting Data: an Implementation of Synoptic Radiology Reporting in Low-Dose CT Cancer Screening.

Alexander K Goel1, Debbie DiLella2, Gus Dotsikas2, Maria Hilts2, David Kwan3, Lindsay Paxton2.   

Abstract

Cancer Care Ontario (CCO) is the clinical advisor to the Ontario Ministry of Health and Long-Term Care for the funding and delivery of cancer services. Data contained in radiology reports are inaccessible for analysis without significant manual cost and effort. Synoptic reporting includes highly structured reporting and discrete data capture, which could unlock these data for clinical and evaluative purposes. To assess the feasibility of implementing synoptic radiology reporting, a trial implementation was conducted at one hospital within CCO's Lung Cancer Screening Pilot for People at High Risk. This project determined that it is feasible to capture synoptic data with some barriers. Radiologists require increased awareness when reporting cases with a large number of nodules due to lack of automation within the system. These challenges may be mitigated by implementation of some report automation. Domains such as pathology and public health reporting have addressed some of these challenges with standardized reports based on interoperable standards, and radiology could borrow techniques from these domains to assist in implementing synoptic reporting. Data extraction from the reports could also be significantly automated to improve the process and reduce the workload in collecting the data. RadLex codes aided the difficult data extraction process, by helping label potential ambiguity with common terms and machine-readable identifiers.

Entities:  

Keywords:  RadLex; Structured Data capture; Structured Reporting; Synoptic Reporting

Mesh:

Year:  2019        PMID: 31289979      PMCID: PMC6841890          DOI: 10.1007/s10278-019-00214-2

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


  3 in total

1.  Cohort study of structured reporting compared with conventional dictation.

Authors:  Annette J Johnson; Michael Y M Chen; J Shannon Swan; Kimberly E Applegate; Benjamin Littenberg
Journal:  Radiology       Date:  2009-08-25       Impact factor: 11.105

2.  Usability evaluation of a personal health record.

Authors:  Noa Segall; Jeffrey G Saville; Pete L'Engle; Boyd Carlson; Melanie C Wright; Kevin Schulman; James E Tcheng
Journal:  AMIA Annu Symp Proc       Date:  2011-10-22

3.  Standardized synoptic cancer pathology reporting: a population-based approach.

Authors:  John R Srigley; Tom McGowan; Andrea Maclean; Marilyn Raby; Jillian Ross; Sarah Kramer; Carol Sawka
Journal:  J Surg Oncol       Date:  2009-06-15       Impact factor: 3.454

  3 in total
  8 in total

1.  Contextual Structured Reporting in Radiology: Implementation and Long-Term Evaluation in Improving the Communication of Critical Findings.

Authors:  Allard W Olthof; Anne L M Leusveld; Jan Cees de Groot; Petra M C Callenbach; Peter M A van Ooijen
Journal:  J Med Syst       Date:  2020-07-28       Impact factor: 4.460

2.  Augmented Radiologist Workflow Improves Report Value and Saves Time: A Potential Model for Implementation of Artificial Intelligence.

Authors:  Huy M Do; Lillian G Spear; Moozhan Nikpanah; S Mojdeh Mirmomen; Laura B Machado; Alexandra P Toscano; Baris Turkbey; Mohammad Hadi Bagheri; James L Gulley; Les R Folio
Journal:  Acad Radiol       Date:  2020-01       Impact factor: 3.173

3.  Development and application of an electronic synoptic report for reporting and management of low-dose computed tomography lung cancer screening examination.

Authors:  Alain Tremblay; Nicole Ezer; Paul Burrowes; John Henry MacGregor; Andrew Lee; Gavin A Armstrong; Raoul Pereira; Michael Bristow; Jana L Taylor; Paul MacEachern; Niloofar Taghizadeh; Rommy Koetzler; Eric Bedard
Journal:  BMC Med Imaging       Date:  2022-06-11       Impact factor: 2.795

4.  Incidence and real-world burden of brain metastases from solid tumors and hematologic malignancies in Ontario: a population-based study.

Authors:  Steven Habbous; Katharina Forster; Gail Darling; Katarzyna Jerzak; Claire M B Holloway; Arjun Sahgal; Sunit Das
Journal:  Neurooncol Adv       Date:  2020-12-22

5.  PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation.

Authors:  Shui-Hua Wang; Yin Zhang; Xiaochun Cheng; Xin Zhang; Yu-Dong Zhang
Journal:  Comput Math Methods Med       Date:  2021-03-08       Impact factor: 2.238

6.  Deep Learning-Based Natural Language Processing in Radiology: The Impact of Report Complexity, Disease Prevalence, Dataset Size, and Algorithm Type on Model Performance.

Authors:  A W Olthof; P M A van Ooijen; L J Cornelissen
Journal:  J Med Syst       Date:  2021-09-04       Impact factor: 4.460

7.  Rethinking Clinical Trial Radiology Workflows and Student Training: Integrated Virtual Student Shadowing Experience, Education, and Evaluation.

Authors:  Lillian G Spear; Jane A Dimperio; Sherry S Wang; Huy M Do; Les R Folio
Journal:  J Digit Imaging       Date:  2022-02-22       Impact factor: 4.903

Review 8.  Multispecialty Enterprise Imaging Workgroup Consensus on Interactive Multimedia Reporting Current State and Road to the Future: HIMSS-SIIM Collaborative White Paper.

Authors:  Christopher J Roth; David A Clunie; David J Vining; Seth J Berkowitz; Alejandro Berlin; Jean-Pierre Bissonnette; Shawn D Clark; Toby C Cornish; Monief Eid; Cree M Gaskin; Alexander K Goel; Genevieve C Jacobs; David Kwan; Damien M Luviano; Morgan P McBee; Kelly Miller; Abdul Moiz Hafiz; Ceferino Obcemea; Anil V Parwani; Veronica Rotemberg; Elliot L Silver; Erik S Storm; James E Tcheng; Karen S Thullner; Les R Folio
Journal:  J Digit Imaging       Date:  2021-06-15       Impact factor: 4.056

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.