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Title: Analysis of Time-Critical Processes in Hospital Care during a Mass Casualty Incident
Language: English
Authors: Rohwoldt, Talitha 
Keywords: Time-Critical Processes; Hospital Care; MCI; hospital response processes
Issue Date: 30-Mar-2026
Abstract: 
This study investigates in-hospital process times during a simulated mass casualty incident (MCI) with a focus on clinical and logistical workflows. The objective was to analyze timecritical intervals along the patient pathway and to identify potential bottlenecks in hospital response processes. Data were collected during a full-scale simulation exercise involving two participating hospitals. Using standardized observation sheets, predefined time points were recorded for each patient, including triage, internal transport, diagnostic imaging, and clinical decisionmaking. The collected data were subsequently processed and analyzed to evaluate the duration and variability of key process steps. The results demonstrate that early stages of patient management, particularly triage and inhospital transport, were performed consistently and within short time frames. In contrast, later stages of the clinical workflow, especially the transition from shock room management to diagnostic imaging and subsequent clinical decision-making, showed greater variability and, in some cases, incomplete data. These phases were identified as potential bottlenecks within the patient pathway. The findings suggest that while structured procedures enable efficient initial patient assessment, the coordination of downstream clinical processes remains a critical factor influencing overall system performance. In addition, the study highlights the importance of communication, resource allocation, and workflow organization in managing patient flow during MCI scenarios. Despite limitations related to sample size and the use of a simulation-based setting, this study provides valuable insights into hospital processes under surge conditions. The results underline the need for further research, particularly involving larger datasets and real-world scenarios, to support the optimization of hospital emergency preparedness and response strategies.
URI: https://hdl.handle.net/20.500.12738/20321
Institute: Fakultät Life Sciences 
Type: Thesis
Thesis type: Master Thesis
Advisor: Tolg, Boris 
Referee: Behrens, Stefan 
Enthalten in den Sammlungen:Theses

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