Journal article
Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis.
- Panje CM Department of Radiation Oncology, Kantonsspital St. Gallen, Rorschacherstrasse 95, 9007, St. Gallen, Switzerland.
- Glatzer M Department of Radiation Oncology, Kantonsspital St. Gallen, Rorschacherstrasse 95, 9007, St. Gallen, Switzerland.
- von Rappard J Department of Nephrology, Inselspital Bern, Bern, Switzerland.
- Rothermundt C Department of Medical Oncology, Kantonsspital St. Gallen, St. Gallen, Switzerland.
- Hundsberger T Department of Medical Oncology, Kantonsspital St. Gallen, St. Gallen, Switzerland.
- Zumstein V Department of Urology, Kantonsspital St. Gallen, St. Gallen, Switzerland.
- Plasswilm L Department of Radiation Oncology, Kantonsspital St. Gallen, Rorschacherstrasse 95, 9007, St. Gallen, Switzerland.
- Putora PM Department of Radiation Oncology, Kantonsspital St. Gallen, Rorschacherstrasse 95, 9007, St. Gallen, Switzerland. paulmartin.putora@kssg.ch.
- 2017-08-18
Published in:
- BMC medical research methodology. - 2017
Cancer
Consensus
Consensus finding
Decision tree
Radiotherapy
Swarm-based medicine
Algorithms
Clinical Decision-Making
Decision Trees
Humans
English
BACKGROUND
The objective consensus methodology has recently been applied in consensus finding in several studies on medical decision-making among clinical experts or guidelines. The main advantages of this method are an automated analysis and comparison of treatment algorithms of the participating centers which can be performed anonymously.
METHODS
Based on the experience from completed consensus analyses, the main steps for the successful implementation of the objective consensus methodology were identified and discussed among the main investigators.
RESULTS
The following steps for the successful collection and conversion of decision trees were identified and defined in detail: problem definition, population selection, draft input collection, tree conversion, criteria adaptation, problem re-evaluation, results distribution and refinement, tree finalisation, and analysis.
CONCLUSION
This manuscript provides information on the main steps for successful collection of decision trees and summarizes important aspects at each point of the analysis.
The objective consensus methodology has recently been applied in consensus finding in several studies on medical decision-making among clinical experts or guidelines. The main advantages of this method are an automated analysis and comparison of treatment algorithms of the participating centers which can be performed anonymously.
METHODS
Based on the experience from completed consensus analyses, the main steps for the successful implementation of the objective consensus methodology were identified and discussed among the main investigators.
RESULTS
The following steps for the successful collection and conversion of decision trees were identified and defined in detail: problem definition, population selection, draft input collection, tree conversion, criteria adaptation, problem re-evaluation, results distribution and refinement, tree finalisation, and analysis.
CONCLUSION
This manuscript provides information on the main steps for successful collection of decision trees and summarizes important aspects at each point of the analysis.
- Language
-
- English
- Open access status
- gold
- Identifiers
-
- DOI 10.1186/s12874-017-0400-y
- PMID 28814269
- Persistent URL
- https://folia.unifr.ch/global/documents/44164
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