Literature DB >> 21257928

Informatics in radiology: Efficiency metrics for imaging device productivity.

Mengqi Hu1, William Pavlicek, Patrick T Liu, Muhong Zhang, Steve G Langer, Shanshan Wang, Vicki Place, Rafael Miranda, Teresa Tong Wu.   

Abstract

Acute awareness of the costs associated with medical imaging equipment is an ever-present aspect of the current healthcare debate. However, the monitoring of productivity associated with expensive imaging devices is likely to be labor intensive, relies on summary statistics, and lacks accepted and standardized benchmarks of efficiency. In the context of the general Six Sigma DMAIC (design, measure, analyze, improve, and control) process, a World Wide Web-based productivity tool called the Imaging Exam Time Monitor was developed to accurately and remotely monitor imaging efficiency with use of Digital Imaging and Communications in Medicine (DICOM) combined with a picture archiving and communication system. Five device efficiency metrics-examination duration, table utilization, interpatient time, appointment interval time, and interseries time-were derived from DICOM values. These metrics allow the standardized measurement of productivity, to facilitate the comparative evaluation of imaging equipment use and ongoing efforts to improve efficiency. A relational database was constructed to store patient imaging data, along with device- and examination-related data. The database provides full access to ad hoc queries and can automatically generate detailed reports for administrative and business use, thereby allowing staff to monitor data for trends and to better identify possible changes that could lead to improved productivity and reduced costs in association with imaging services. © RSNA, 2011.

Entities:  

Mesh:

Year:  2011        PMID: 21257928     DOI: 10.1148/rg.312105714

Source DB:  PubMed          Journal:  Radiographics        ISSN: 0271-5333            Impact factor:   5.333


  6 in total

1.  A flexible database architecture for mining DICOM objects: the DICOM data warehouse.

Authors:  Steve G Langer
Journal:  J Digit Imaging       Date:  2012-04       Impact factor: 4.056

2.  An automated DICOM database capable of arbitrary data mining (including radiation dose indicators) for quality monitoring.

Authors:  Shanshan Wang; William Pavlicek; Catherine C Roberts; Steve G Langer; Muhong Zhang; Mengqi Hu; Richard L Morin; Beth A Schueler; Clinton V Wellnitz; Teresa Wu
Journal:  J Digit Imaging       Date:  2011-04       Impact factor: 4.056

3.  Normalizing Heterogeneous Medical Imaging Data to Measure the Impact of Radiation Dose.

Authors:  Luís A Bastião Silva; Luís S Ribeiro; Milton Santos; Nuno Neves; Dulce Francisco; Carlos Costa; José Luis Oliveira
Journal:  J Digit Imaging       Date:  2015-12       Impact factor: 4.056

Review 4.  ETL Framework for Real-Time Business Intelligence over Medical Imaging Repositories.

Authors:  Tiago Marques Godinho; Rui Lebre; João Rafael Almeida; Carlos Costa
Journal:  J Digit Imaging       Date:  2019-10       Impact factor: 4.056

5.  Sensor-based architecture for medical imaging workflow analysis.

Authors:  Luís A Bastião Silva; Samuel Campos; Carlos Costa; José Luis Oliveira
Journal:  J Med Syst       Date:  2014-06-24       Impact factor: 4.460

6.  Understanding Scanner Utilization With Real-Time DICOM Metadata Extraction.

Authors:  Pradeeban Kathiravelu; Ashish Sharma; Puneet Sharma
Journal:  IEEE Access       Date:  2021-01-11       Impact factor: 3.476

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.