K Lennquist Montán1,2,3, L Riddez4, S Lennquist5, A C Olsberg6, H Lindberg7, D Gryth8, P Örtenwall9. 1. Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. lennquist@hotmail.com. 2. Centre for Prehospital and Disaster Medicine, Regionens Hus, 405 44, Gothenburg, Sweden. lennquist@hotmail.com. 3. , Åsevägen 1, 182 39, Danderyd, Sweden. lennquist@hotmail.com. 4. Department of Molecular Medicine and Surgery, Karolinska Institute, Solna, Sweden. 5. Department of Surgery (professor emeritus), University of Linköping, Linköping, Sweden. 6. Emergency Department, Karolinska University Hospital, Solna, Sweden. 7. Stockholm County Council, Stockholm, Sweden. 8. Department of Physiology and Pharmacology, Karolinska Institute, Solna, Sweden. 9. Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Abstract
AIM: The aim of this study was to use a simulation model developed for the scientific evaluation of methodology in disaster medicine to test surge capacity (SC) in a major hospital responding to a simulated major incident with a scenario copied from a real incident. METHODS: The tested hospital was illustrated on a system of magnetic boards, where available resources, staff, and patients treated in the hospital at the time of the test were illustrated. Casualties were illustrated with simulation cards supplying all data required to determine procedures for diagnosis and treatment, which all were connected to real consumption of time and resources. RESULTS: The first capacity-limiting factor was the number of resuscitation teams that could work parallel in the emergency department (ED). This made it necessary to refer severely injured to other hospitals. At this time, surgery (OR) and intensive care (ICU) had considerable remaining capacity. Thus, the reception of casualties could be restarted when the ED had been cleared. The next limiting factor was lack of ventilators in the ICU, which permanently set the limit for SC. At this time, there was still residual OR capacity. With access to more ventilators, the full surgical capacity of the hospital could have been utilized. CONCLUSIONS: The tested model was evaluated as an accurate tool to determine SC. The results illustrate that SC cannot be determined by testing one single function in the hospital, since all functions interact with each other and different functions can be identified as limiting factors at different times during the response.
AIM: The aim of this study was to use a simulation model developed for the scientific evaluation of methodology in disaster medicine to test surge capacity (SC) in a major hospital responding to a simulated major incident with a scenario copied from a real incident. METHODS: The tested hospital was illustrated on a system of magnetic boards, where available resources, staff, and patients treated in the hospital at the time of the test were illustrated. Casualties were illustrated with simulation cards supplying all data required to determine procedures for diagnosis and treatment, which all were connected to real consumption of time and resources. RESULTS: The first capacity-limiting factor was the number of resuscitation teams that could work parallel in the emergency department (ED). This made it necessary to refer severely injured to other hospitals. At this time, surgery (OR) and intensive care (ICU) had considerable remaining capacity. Thus, the reception of casualties could be restarted when the ED had been cleared. The next limiting factor was lack of ventilators in the ICU, which permanently set the limit for SC. At this time, there was still residual OR capacity. With access to more ventilators, the full surgical capacity of the hospital could have been utilized. CONCLUSIONS: The tested model was evaluated as an accurate tool to determine SC. The results illustrate that SC cannot be determined by testing one single function in the hospital, since all functions interact with each other and different functions can be identified as limiting factors at different times during the response.
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