P. Menéndez Fernández-Miranda, A. Pérez Del Barrio , P. Sanz Bellon, E. Marqués Fraguela, J. Azcona Saenz, C. González-Carrero Sixto, N. Ferreiros Vázquez"">

How Business Intelligence will revolutionise radiation protection in radiology departments (original) (raw)

EuroSafe Imaging 2020 / ESI-03206

Congress:

EuroSafe Imaging 2020

Keywords:

RIS, Radiation safety, Dosimetric comparison, Action 4 - Dose management systems, Not applicable, Radioprotection / Radiation dose, Computer applications

Authors:

P. Menéndez Fernández-Miranda

, A. Pérez Del Barrio , P. Sanz Bellon, E. Marqués Fraguela, J. Azcona Saenz, C. González-Carrero Sixto, N. Ferreiros Vázquez

DOI:

10.26044/esi2020/ESI-03206

Background/introduction

OBJECTIVES The objective of this work is to analyse the weaknesses of the radiation monitoring systems (RMS) that are being used today, and to evaluate howBusiness Intelligence(BI) software will introduce new strategies to solve them. BACKGROUND RMS that continuously measureradiationin radiology departments are essential to ensuresafetyand satisfy regulatory requirements. For this purpose, radiology equipment send all the data related to each imaging study to RMS. Accurate and appropriate processing of this data is the keyto success in the control and management of the medical radiation....

Description of activity and work performed

Business intelligence(BI) refers to applications and practices for the collection, integration, analysis, and presentation of business information. The aim of these software is to allow the user for the easy interpretation of a large quantity of data which leads to a better decision making. However, these kind of software can be also used for the analysis of medical radiation data. Consequently, we propose to use a BI application as a revolutionary new solution for currently RMS limitations. 1. First step: data extraction Firstly, these systems...

Conclusion and recommendations

The current RMS show some limitations in the visualization, filtering, and analysis of the radiation data obtained by radiology equipment. New BI programs will overcome these problems and they will be essential to supply data to AI software.

Personal/organisational information

P. Menéndez Fernández-Miranda; Santander/ES - nothing to disclose A. Pérez Del Barrio; Santander/ES - nothing to disclose P. Sanz Bellon; Santander/ES - nothing to disclose E. Marqués Fraguela; Santander/ES - nothing to disclose C. González-Carrero Sixto; Santander/ES - nothing to disclose J. Azcona Saenz; Santander/ES - nothing to disclose N. Ferreiros Vázquez; Santander/ES - nothing to disclose

References

Chelico JD, Wilcox AB, Vawdrey DK, Kuperman, GJ. Designing a Clinical Data Warehouse Architecture to Support Quality Improvement Initiatives. AMIA Annu Symp Proc. 2016; 2016: 381-390. Foran DJ, Chen W, Chu H, Sadimin E, Loh D, Riedlinger G, et al. Roadmap to a Comprehensive Clinical Data Warehouse for Precision Medicine Applications in Oncology. Cancer Inform. 2017; 16: 1176935117694349. Karami M, Rahimi A, Shahmirzadi AH. Clinical Data Warehouse: An Effective Tool to Create Intelligence in Disease Management. Health Care Manag. 2017; 36: 380-384. Langer SG. DICOM...