Literature DB >> 16165403

Radiology interpretation process modeling.

Rita Noumeir1.   

Abstract

Information and communication technology in healthcare promises optimized patient care while ensuring efficiency and cost-effectiveness. However, the promised results are not yet achieved; the healthcare process requires analysis and radical redesign to achieve improvements in care quality and productivity. Healthcare process reengineering is thus necessary and involves modeling its workflow. Even though the healthcare process is very large and not very well modeled yet, its sub-processes can be modeled individually, providing fundamental pieces of the whole model. In this paper, we are interested in modeling the radiology interpretation process that results in generating a diagnostic radiology report. This radiology report is an important clinical element of the patient healthcare record and assists in healthcare decisions. We present the radiology interpretation process by identifying its boundaries and by positioning it on the large healthcare process map. Moreover, we discuss an information data model and identify roles, tasks and several information flows. Furthermore, we describe standard frameworks to enable radiology interpretation workflow implementations between heterogeneous systems.

Entities:  

Mesh:

Year:  2005        PMID: 16165403     DOI: 10.1016/j.jbi.2005.07.001

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  10 in total

1.  Influence of radiology report format on reading time and comprehension.

Authors:  Elizabeth A Krupinski; E Tyler Hall; Stacy Jaw; Bruce Reiner; Eliot Siegel
Journal:  J Digit Imaging       Date:  2012-02       Impact factor: 4.056

Review 2.  Customization of medical report data.

Authors:  Bruce I Reiner
Journal:  J Digit Imaging       Date:  2010-08       Impact factor: 4.056

3.  Analyzing PACS Usage Patterns by Means of Process Mining: Steps Toward a More Detailed Workflow Analysis in Radiology.

Authors:  Daniel Forsberg; Beverly Rosipko; Jeffrey L Sunshine
Journal:  J Digit Imaging       Date:  2016-02       Impact factor: 4.056

4.  Benefits of the DICOM structured report.

Authors:  Rita Noumeir
Journal:  J Digit Imaging       Date:  2006-12       Impact factor: 4.056

5.  Citation analysis of the prognosis of Haux et al. for the year 2013.

Authors:  Jürgen Stausberg
Journal:  J Med Syst       Date:  2014-07       Impact factor: 4.460

6.  Reducing missed laboratory results: defining temporal responsibility, generating user interfaces for test process tracking, and retrospective analyses to identify problems.

Authors:  Sureyya Tarkan; Catherine Plaisant; Ben Shneiderman; A Zachary Hettinger
Journal:  AMIA Annu Symp Proc       Date:  2011-10-22

7.  Workflow in clinical trial sites & its association with near miss events for data quality: ethnographic, workflow & systems simulation.

Authors:  Elias Cesar Araujo de Carvalho; Adelia Portero Batilana; Wederson Claudino; Luiz Fernando Lima Reis; Rafael A Schmerling; Jatin Shah; Ricardo Pietrobon
Journal:  PLoS One       Date:  2012-06-29       Impact factor: 3.240

8.  Radiology Reporting System Data Exchange With the Electronic Health Record System: A Case Study in Iran.

Authors:  Maryam Ahmadi; Marjan Ghazisaeidi; Azadeh Bashiri
Journal:  Glob J Health Sci       Date:  2015-03-18

9.  Applying Systems Engineering Reduces Radiology Transport Cycle Times in the Emergency Department.

Authors:  Benjamin A White; Brian J Yun; Michael H Lev; Ali S Raja
Journal:  West J Emerg Med       Date:  2017-02-21

10.  Active Learning of the HL7 Medical Standard.

Authors:  Rita Noumeir
Journal:  J Digit Imaging       Date:  2019-06       Impact factor: 4.056

  10 in total

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