'Rapid Learning health care in oncology' - An approach towards decision support systems enabling customised radiotherapy'
Publication year
2013Source
Radiotherapy and Oncology, 109, 1, (2013), pp. 159-164ISSN
Publication type
Article / Letter to editor

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Organization
Radiation Oncology
IQ Healthcare
Journal title
Radiotherapy and Oncology
Volume
vol. 109
Issue
iss. 1
Page start
p. 159
Page end
p. 164
Subject
NCEBP 3: Implementation Science; ONCOL 3: Translational research; NCEBP 3: Implementation ScienceAbstract
PURPOSE: An overview of the Rapid Learning methodology, its results, and the potential impact on radiotherapy. MATERIAL AND RESULTS: Rapid Learning methodology is divided into four phases. In the data phase, diverse data are collected about past patients, treatments used, and outcomes. Innovative information technologies that support semantic interoperability enable distributed learning and data sharing without additional burden on health care professionals and without the need for data to leave the hospital. In the knowledge phase, prediction models are developed for new data and treatment outcomes by applying machine learning methods to data. In the application phase, this knowledge is applied in clinical practice via novel decision support systems or via extensions of existing models such as Tumour Control Probability models. In the evaluation phase, the predictability of treatment outcomes allows the new knowledge to be evaluated by comparing predicted and actual outcomes. CONCLUSION: Personalised or tailored cancer therapy ensures not only that patients receive an optimal treatment, but also that the right resources are being used for the right patients. Rapid Learning approaches combined with evidence based medicine are expected to improve the predictability of outcome and radiotherapy is the ideal field to study the value of Rapid Learning. The next step will be to include patient preferences in the decision making.
This item appears in the following Collection(s)
- Academic publications [204968]
- Faculty of Medical Sciences [81049]
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