ArticleRadiation oncology (London, England)2015
Data management, documentation and analysis systems in radiation oncology: a multi-institutional survey.
Article in Radiation oncology (London, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Artificial Intelligence in Particle Therapy Treatment Planning: Current Advances and Future Directions.International journal of particle therapy · 2026Article
- Four-year experience with an in-house treatment management platform to streamline departmental operations in radiation oncology.Journal of applied clinical medical physics · 2026Article
- Review of Quality Audit in a Radiation Oncology Department in a Tertiary Care Hospital- 2 Year-Results.Asian Pacific journal of cancer prevention : APJCP · 2024Article
- Challenges and opportunities to integrate artificial intelligence in radiation oncology: a narrative review.Ewha medical journal · 2024Review
- Health data management practice and associated factors among health professionals working in public health facilities in Oromia Special Zone, Amhara, Ethiopia: a cross-sectional study.BMJ public health · 2024Article
- Health Data Management Practice and Associated Factors Among Health Professionals Working at Public Health Facilities in Resource Limited Settings.Advances in medical education and practice · 2021Article
- Review of Developments in Electronic, Clinical Data Collection, and Documentation Systems over the Last Decade - Are We Ready for Big Data in Routine Health Care?Frontiers in oncology · 2016Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionRecently, information availability has become more elaborate and widespread, and treatment decisions are based on a multitude of factors. Gathering relevant data, also referred to as Big Data, is therefore critical for reaching the best patient care, and enhancing interdisciplinary and clinical research. Combining patient data from all involved systems is essential to prepare unstructured data for analyses. This demands special coordination in data management. Our study aims to characterize current developments in German-speaking hospital departments and practices. We successfully conducted the survey with the members of the Deutsche Gesellschaft für Radioonkologie (DEGRO).
methodsA questionnaire was developed consisting of 17 questions related to data management, documentation and clinical trial analyses, reflecting the clinical topics such as basic patient information, imaging, follow-up information as well as connection of documentation tools with radiooncological treatment planning machines.
resultsA total of 44 institutions completed the online survey (University hospitals n = 17, hospitals n = 13, practices/institutes n = 14). University hospitals, community hospitals and private practices are equally equipped concerning IT infrastructure for clinical use. However, private practices have a low interest in research work. All respondents stated the biggest obstacles about introducing a documentation system into their unit lie in funding and support of the central IT departments. Only 27 % (12/44) of responsible persons are specialists for documentation and data management.
conclusionOur study gives an understanding of the challenges and solutions we need to be looking at for medical data storage. In the future, inter-departmental cross-links will enable the radiation oncology community to generate large-scale analyses.
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