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Updated: Jun 10, 2025

A Novel Approach for the Administration of Medications and Fluids in Emergency Scenarios and Settings
Published on: November 9, 2016
[Utilization of registers to create an evidence-based approach in catastrophes and civil defence]
Sebastian Imach1, Rolf Lefering2, Benny Kölbel3
1Klinik für Orthopädie, Unfallchirurgie und Sporttraumatologie, Krankenhaus Köln Merheim, Kliniken der Stadt Köln, Universität Witten/Herdecke, Ostmerheimer Str. 200, 51109, Köln, Deutschland. imachse@kliniken-koeln.de.
Abstract:
Medical data registers are a key instrument of medical care research and a valuable tool for medical quality assurance. The structured plausibility tested documentation of large case numbers on a longitudinally oriented time axis with different points in time of data acquisition enables statements to be made on numerous relevant outcomes, not only the mortality of patients. For incidents outside the daily routine care in trauma surgery, such as natural disasters, accidents with multiple casualties and nonmilitary treatment of the domestic population in defence situations, such registers can provide data-based recommendations for action. These data, mainly obtained from routine traumatological treatment, enable a targeted resource management in the abovenamed incidents, which are associated with mass casualties. Due to the utilization of registers from the military field or from international registers, the perspective is additionally extended with respect to treatment strategies and injury patterns. Whether data can also be generated in a suitable manner for the abovenamed registers in specific disaster situations and can provide a direct gain of knowledge from the incident, must be critically discussed. The maintenance of the register datasets is time-consuming and has been subjected to a more stringent regulation at least since May 2018, when the European Union General Data Protection Regulation (EU-GDPR) came into force. The future Register Act in Germany will hopefully achieve greater simplification in the documentation of routine data.
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