Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis

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 c...

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Published in:BMC medical research methodology Vol. 17; no. 1; pp. 123 - 9
Main Authors: Panje, Cédric M., Glatzer, Markus, von Rappard, Joscha, Rothermundt, Christian, Hundsberger, Thomas, Zumstein, Valentin, Plasswilm, Ludwig, Putora, Paul Martin
Format: Journal Article
Language:English
Published: London BioMed Central 16.08.2017
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Algorithms
Analysis
Androgens
Cancer
Cancer therapies
Clinical decision making
Clinical Decision-Making - methods
Consensus
Consensus finding
Data collection
Decision making
Decision tree
Decision Trees
Evidence-based medicine
Experts
Gastric cancer
Health Sciences
Humans
Kidney cancer
Medical research
Medicine
Medicine & Public Health
Medicine, Experimental
Metastasis
Methods
Prostate cancer
quality
Radiation therapy
Radiotherapy
reporting
Research Article
Statistical Theory and Methods
Statistics for Life Sciences
Swarm-based medicine
Theory of Medicine/Bioethics
Workloads
Switzerland
Consensus finding
Swarm-based medicine
Radiotherapy
Decision tree
Consensus
Cancer
ISSN:1471-2288, 1471-2288
Online Access:Get full text
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Summary: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.
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ISSN:1471-2288
1471-2288
DOI:10.1186/s12874-017-0400-y