scholarly journals Calculating Key Figures for Radiology Departments Using a Clinical Data Warehouse – A Technical Case Report

2021 ◽  
Author(s):  
Leon Liman ◽  
Georg Fette ◽  
Jonathan Krebs ◽  
Frank Puppe

Optimizing the utilization of radiology departments is one of the primary objectives for many hospitals. To support this, a solution has been developed, which at first transforms the export of different Radiological Information Systems (RIS) into the data format of a clinical data warehouse (CDW). Additional features, like for example the time between the creation of a radiologic request and the finalization of the diagnosis for the created images, can then be defined using a simple interface and are calculated and saved in the CDW as well. Finally, the query language of the CDW can be used to create custom reports with all the RIS data including the calculated features and export them into the standard formats Excel and CSV. The solution has been successfully tested with data from two German hospitals.

2006 ◽  
Vol 52 (2) ◽  
pp. 192-197
Author(s):  
Qiyan Zhang ◽  
Yasushi Matsumura ◽  
Tadamasa Teratani ◽  
Sachiko Yoshimoto ◽  
Takahiro Mineno ◽  
...  

2012 ◽  
Vol 19 (5) ◽  
pp. 782-785 ◽  
Author(s):  
Abdelali Boussadi ◽  
Thibaut Caruba ◽  
Eric Zapletal ◽  
Brigitte Sabatier ◽  
Pierre Durieux ◽  
...  

2021 ◽  
pp. 561-569
Author(s):  
Steven A. Eschrich ◽  
Jamie K. Teer ◽  
Phillip Reisman ◽  
Erin Siegel ◽  
Chandan Challa ◽  
...  

PURPOSE The use of genomics within cancer research and clinical oncology practice has become commonplace. Efforts such as The Cancer Genome Atlas have characterized the cancer genome and suggested a wealth of targets for implementing precision medicine strategies for patients with cancer. The data produced from research studies and clinical care have many potential secondary uses beyond their originally intended purpose. Effective storage, query, retrieval, and visualization of these data are essential to create an infrastructure to enable new discoveries in cancer research. METHODS Moffitt Cancer Center implemented a molecular data warehouse to complement the extensive enterprise clinical data warehouse (Health and Research Informatics). Seven different sequencing experiment types were included in the warehouse, with data from institutional research studies and clinical sequencing. RESULTS The implementation of the molecular warehouse involved the close collaboration of many teams with different expertise and a use case–focused approach. Cornerstones of project success included project planning, open communication, institutional buy-in, piloting the implementation, implementing custom solutions to address specific problems, data quality improvement, and data governance, unique aspects of which are featured here. We describe our experience in selecting, configuring, and loading molecular data into the molecular data warehouse. Specifically, we developed solutions for heterogeneous genomic sequencing cohorts (many different platforms) and integration with our existing clinical data warehouse. CONCLUSION The implementation was ultimately successful despite challenges encountered, many of which can be generalized to other research cancer centers.


Author(s):  
Kwang Seob Lee ◽  
Dong‐Gyo Shin ◽  
Jin‐Hee Hwang ◽  
Ranhee Kim ◽  
Chang Hoon Han ◽  
...  

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Dominic Girardi ◽  
Johannes Dirnberger ◽  
Michael Giretzlehner

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