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[Algorithms in trauma management].

Authors: C, Waydhas; K G, Kanz; S, Ruchholtz; D, Nast-Kolb;

[Algorithms in trauma management].

Abstract

A well-controlled, meticulous process has a far higher probability of resulting in a high quality of medical care than improvization and unstructured creativity. Algorithms display decision-making treatment processes and problem-solving strategies by giving clearly defined and formalized guidelines. The flow chart for decision-making follows the yes/no dichotomy of binary logic. The systematic ordering of decision points and consequent actions is guided by medical priority and thus regulates the time-frame and sequence of each single step in a logical manner. With the help of clinical algorithms highly complex processes such as the management of the severely injured patient can be translated into a clearly structured, logical pathway. Clinical algorithms represent scientifically recognized treatment rules, indicate a solution for solving problems and help users to organize ideas and recognize connections. They delineate a consistent and valid guideline, while allowing deviations in proven exceptions. The use of algorithms allows a systematic search for errors in the process of quality management. In emergency situations they suggest a structured means of problem solving for the less experienced user. Algorithms are useful instruments in the teaching of medical decision-making.

Related Organizations
Keywords

Patient Care Team, Critical Care, Quality Assurance, Health Care, Multiple Trauma, Decision Trees, First Aid, Humans, Algorithms

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    influence
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
55
Average
Top 10%
Top 10%
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