Literature DB >> 25467900

Business intelligence for the radiologist: making your data work for you.

Tessa S Cook1, Paul Nagy2.   

Abstract

Although it remains absent from most programs today, business intelligence (BI) has become an integral part of modern radiology practice management. BI facilitates the transition away from lack of understanding about a system and the data it produces toward incrementally more sophisticated comprehension of what has happened, could happen, and should happen. The individual components that make up BI are common across industries and include data extraction and transformation, process analysis and improvement, outcomes measures, performance assessment, graphical dashboarding, alerting, workflow analysis, and scenario modeling. As in other fields, these components can be directly applied in radiology to improve workflow, throughput, safety, efficacy, outcomes, and patient satisfaction. When approaching the subject of BI in radiology, it is important to know what data are available in your various electronic medical records, as well as where and how they are stored. In addition, it is critical to verify that the data actually represent what you think they do. Finally, it is critical for success to identify the features and limitations of the BI tools you choose to use and to plan your practice modifications on the basis of collected data. It is equally important to remember that BI plays a critical role in continuous process improvement; whichever BI tools you choose should be flexible to grow and evolve with your practice. Published by Elsevier Inc.

Entities:  

Keywords:  Analytics; business analytics; business intelligence; graphical dashboarding; information visualization

Mesh:

Year:  2014        PMID: 25467900     DOI: 10.1016/j.jacr.2014.09.008

Source DB:  PubMed          Journal:  J Am Coll Radiol        ISSN: 1546-1440            Impact factor:   5.532


  6 in total

Review 1.  Bits and bytes: the future of radiology lies in informatics and information technology.

Authors:  James A Brink; Ronald L Arenson; Thomas M Grist; Jonathan S Lewin; Dieter Enzmann
Journal:  Eur Radiol       Date:  2017-03-09       Impact factor: 5.315

2.  A Business Analytics Software Tool for Monitoring and Predicting Radiology Throughput Performance.

Authors:  Stephen Jones; Seán Cournane; Niall Sheehy; Lucy Hederman
Journal:  J Digit Imaging       Date:  2016-12       Impact factor: 4.056

3.  Utilization of Workflow Process Maps to Analyze Gaps in Critical Event Notification at a Large, Urban Hospital.

Authors:  Meredith Bowen; Adam Prater; Nabile M Safdar; Seena Dehkharghani; Jack A Fountain
Journal:  J Digit Imaging       Date:  2016-08       Impact factor: 4.056

Review 4.  A Foundation for Enterprise Imaging: HIMSS-SIIM Collaborative White Paper.

Authors:  Christopher J Roth; Louis M Lannum; Kenneth R Persons
Journal:  J Digit Imaging       Date:  2016-10       Impact factor: 4.056

5.  IRB Process Improvements: A Machine Learning Analysis.

Authors:  Kimberly Shoenbill; Yiqiang Song; Nichelle L Cobb; Marc K Drezner; Eneida A Mendonca
Journal:  J Clin Transl Sci       Date:  2017-04-26

6.  Management decisions of an Academic Radiology Department during COVID-19 pandemic: the important support of a business analytics software.

Authors:  Andrea Laghi; Virginia Tamburi; Michela Polici; Paolo Anibaldi; Adriano Marcolongo; Damiano Caruso
Journal:  Eur Radiol       Date:  2022-04-05       Impact factor: 7.034

  6 in total

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