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Hospital Surge Capacity during Expo 2015 in Milano, Italy
- Roberto Faccincani, Francesco Della Corte, Giovanni Sesana, Riccardo Stucchi, Eric Weinstein, Itamar Ashkenazi, Pierluigi Ingrassia
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- Journal:
- Prehospital and Disaster Medicine / Volume 33 / Issue 5 / October 2018
- Published online by Cambridge University Press:
- 29 August 2018, pp. 459-465
- Print publication:
- October 2018
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Introduction
Hospital Acute Care Surge Capacity (HACSC), Hospital Acute Care Surge Threshold (HACST), and Total Hospital Capacity (THC) are scales that were developed to quantify surge capacity in the event of a multiple-casualty incident (MCI). These scales take into consideration the need for adequate care for both critical (T1) and moderate (T2) trauma patients. The objective of this study was to verify the validity of these scales in nine hospitals of the Milano (Italy) metropolitan area that prepared for a possible MCI during EXPO 2015.
MethodsBoth HACSC and HACST were computed for individual hospitals. These were compared to surge capacities declared by individual hospitals during EXPO 2015, and also to surge capacity evaluated during a simulation organized on August 23, 2016.
ResultsBoth HACSC and HACST were smaller compared to capacities measured and reported by the hospitals, as well as those found during the simulation. This resulted in significant differences in THC when this was computed from the different methods of calculation.
Conclusions:Surge capacity is dependent on the method of measurement. Each method has its inherent deficiencies. Until more reliable methodologies are developed, there is a benefit to analyze surge capacity using several methods rather than just one. Emergency committee members should be aware of the importance of critical resources when looking to the hospital capacity to respond to an MCI, and to the possibility to effectively increase it with a good preparedness plan. Since hospital capacity during real events is not static but dynamic, largely depending on occupation of the available resources, it is important that the regional command center and the hospitals receiving casualties constantly communicate on specific agreed upon critical resources, in order for the regional command center to timely evaluate the overall regional capacity and guarantee the appropriate distribution of the patients.
,Faccincani R ,Della Corte F ,Sesana G ,Stucchi R ,Weinstein E ,Ashkenazi I .Ingrassia P Hospital Surge Capacity during Expo 2015 in Milano, Italy . Prehosp Disaster Med.2018 ;33 (5 ):459 –465 .
Disaster Metrics: Evaluation of de Boer's Disaster Severity Scale (DSS) Applied to Earthquakes
- Jamil D. Bayram, Shawki Zuabi, Caitlin M. McCord, Raphael A.G. Sherak, Edberdt B. Hsu, Gabor D. Kelen
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- Journal:
- Prehospital and Disaster Medicine / Volume 30 / Issue 1 / February 2015
- Published online by Cambridge University Press:
- 29 December 2014, pp. 22-27
- Print publication:
- February 2015
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Introduction
Quantitative measurement of the medical severity following multiple-casualty events (MCEs) is an important goal in disaster medicine. In 1990, de Boer proposed a 13-point, 7-parameter scale called the Disaster Severity Scale (DSS). Parameters include cause, duration, radius, number of casualties, nature of injuries, rescue time, and effect on surrounding community.
HypothesisThis study aimed to examine the reliability and dimensionality (number of salient themes) of de Boer's DSS scale through its application to 144 discrete earthquake events.
MethodsA search for earthquake events was conducted via National Oceanic and Atmospheric Administration (NOAA) and US Geological Survey (USGS) databases. Two experts in the field of disaster medicine independently reviewed and assigned scores for parameters that had no data readily available (nature of injuries, rescue time, and effect on surrounding community), and differences were reconciled via consensus. Principle Component Analysis was performed using SPSS Statistics for Windows Version 22.0 (IBM Corp; Armonk, New York USA) to evaluate the reliability and dimensionality of the DSS.
ResultsA total of 144 individual earthquakes from 2003 through 2013 were identified and scored. Of 13 points possible, the mean score was 6.04, the mode = 5, minimum = 4, maximum = 11, and standard deviation = 2.23. Three parameters in the DSS had zero variance (ie, the parameter received the same score in all 144 earthquakes). Because of the zero contribution to variance, these three parameters (cause, duration, and radius) were removed to run the statistical analysis. Cronbach's alpha score, a coefficient of internal consistency, for the remaining four parameters was found to be robust at 0.89. Principle Component Analysis showed uni-dimensional characteristics with only one component having an eigenvalue greater than one at 3.17. The 4-parameter DSS, however, suffered from restriction of scoring range on both parameter and scale levels.
ConclusionJan de Boer's DSS in its 7-parameter format fails to hold statistically in a dataset of 144 earthquakes subjected to analysis. A modified 4-parameter scale was found to quantitatively assess medical severity more directly, but remains flawed due to range restriction on both individual parameter and scale levels. Further research is needed in the field of disaster metrics to develop a scale that is reliable in its complete set of parameters, capable of better fine discrimination, and uni-dimensional in measurement of the medical severity of MCEs.
. ,Bayram JD ,Zuabi S ,McCord CM ,Sherak RAG ,Hsu EB .Kelen GD Disaster Metrics: Evaluation of de Boer's Disaster Severity Scale (DSS) Applied to Earthquakes . Prehosp Disaster Med.2015 ;30 (1 ):1 -6
Disaster Metrics: Quantitative Benchmarking of Hospital Surge Capacity in Trauma-Related Multiple Casualty Events
- Jamil D. Bayram, Shawki Zuabi, Italo Subbarao
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- Journal:
- Disaster Medicine and Public Health Preparedness / Volume 5 / Issue 2 / June 2011
- Published online by Cambridge University Press:
- 08 April 2013, pp. 117-124
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Objectives: Hospital surge capacity in multiple casualty events (MCE) is the core of hospital medical response, and an integral part of the total medical capacity of the community affected. To date, however, there has been no consensus regarding the definition or quantification of hospital surge capacity. The first objective of this study was to quantitatively benchmark the various components of hospital surge capacity pertaining to the care of critically and moderately injured patients in trauma-related MCE. The second objective was to illustrate the applications of those quantitative parameters in local, regional, national, and international disaster planning; in the distribution of patients to various hospitals by prehospital medical services; and in the decision-making process for ambulance diversion.
Methods: A 2-step approach was adopted in the methodology of this study. First, an extensive literature search was performed, followed by mathematical modeling. Quantitative studies on hospital surge capacity for trauma injuries were used as the framework for our model. The North Atlantic Treaty Organization triage categories (T1-T4) were used in the modeling process for simplicity purposes.
Results: Hospital Acute Care Surge Capacity (HACSC) was defined as the maximum number of critical (T1) and moderate (T2) casualties a hospital can adequately care for per hour, after recruiting all possible additional medical assets. HACSC was modeled to be equal to the number of emergency department beds (#EDB), divided by the emergency department time (EDT); HACSC = #EDB/EDT. In trauma-related MCE, the EDT was quantitatively benchmarked to be 2.5 (hours). Because most of the critical and moderate casualties arrive at hospitals within a 6-hour period requiring admission (by definition), the hospital bed surge capacity must match the HACSC at 6 hours to ensure coordinated care, and it was mathematically benchmarked to be 18% of the staffed hospital bed capacity.
Conclusions: Defining and quantitatively benchmarking the different components of hospital surge capacity is vital to hospital preparedness in MCE. Prospective studies of our mathematical model are needed to verify its applicability, generalizability, and validity.
(Disaster Med Public Health Preparedness. 2011;5:117–124)
Disaster Metrics: Quantitative Estimation of the Number of Ambulances Required in Trauma-Related Multiple Casualty Events
- Jamil D. Bayram, Shawki Zuabi, Mazen J. El Sayed
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- Journal:
- Prehospital and Disaster Medicine / Volume 27 / Issue 5 / October 2012
- Published online by Cambridge University Press:
- 21 August 2012, pp. 445-451
- Print publication:
- October 2012
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Introduction
Estimating the number of ambulances needed in trauma-related Multiple Casualty Events (MCEs) is a challenging task.
Hypothesis/ProblemEmergency medical services (EMS) regions in the United States have varying “best practices” for the required number of ambulances in MCE, none of which is based on metric criteria. The objective of this study was to estimate the number of ambulances required to respond to the scene of trauma-related MCE in order to initiate treatment and complete the transport of critical (T1) and moderate (T2) patients. The proposed model takes into consideration the different transport times and capacities of receiving hospitals, the time interval from injury occurrence, the number of patients per ambulance, and the pre-designated time frame allowed from injury until the transfer care of T1 and T2 patients.
MethodsThe main theoretical framework for this model was based on prehospital time intervals described in the literature and used by EMS systems to evaluate operational and patient care issues. The North Atlantic Treaty Organization (NATO) triage categories (T1-T4) were used for simplicity.
ResultsThe minimum number of ambulances required to respond to the scene of an MCE was modeled as being primarily dependent on the number of critical patients (T1) present at the scene any particular time. A robust quantitative model was also proposed to dynamically estimate the number of ambulances needed at any time during an MCE to treat, transport and transfer the care of T1 and T2 patients.
ConclusionA new quantitative model for estimation of the number of ambulances needed during the prehospital response in trauma-related multiple casualty events has been proposed. Prospective studies of this model are needed to examine its validity and applicability.
,Bayram JD ,Zuabi S .El Sayed MJ Disaster Metrics: Quantitative Estimation of the Number of Ambulances Required in Trauma-Related Multiple Casualty Events . Prehosp Disaster Med.2012 ;27 (5 ):1 -7 .
Disaster Metrics: A Proposed Quantitative Model for Benchmarking Prehospital Medical Response in Trauma-Related Multiple Casualty Events
- Jamil D. Bayram, Shawki Zuabi
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- Journal:
- Prehospital and Disaster Medicine / Volume 27 / Issue 2 / April 2012
- Published online by Cambridge University Press:
- 17 May 2012, pp. 123-129
- Print publication:
- April 2012
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Introduction
Quantitative benchmarking of trauma-related prehospital response for Multiple Casualty Events (MCE) is complicated by major difficulties due to the simultaneous occurrences of multiple prehospital activities.
Hypothesis/ProblemAttempts to quantify the various components of prehospital medical response in MCE have fallen short of a comprehensive model. The objective of this study was to model the principal parameters necessary to quantitatively benchmark the prehospital medical response in trauma-related MCE.
MethodsA two-step approach was adopted for the methodology of this study: an extensive literature search was performed, followed by prehospital system quantitative modeling. Studies on prehospital medical response to trauma injuries were used as the framework for the proposed model. The North Atlantic Treaty Organization (NATO) triage categories (T1-T4) were used for the study.
ResultsTwo parameters, the Injury to Patient Contact Interval (IPCI) and Injury to Hospital Interval (IHI), were identified and proposed as the principal determinants of the medical prehospital response in trauma-related MCE. IHI is the time interval from the occurrence of injury to the completion of transfer of care of critical (T1) and moderate (T2) patients. The IHI for each casualty is compared to the Maximum Time Allowed described in the literature (golden hour for T1 and Friedrich's time for T2). In addition, the medical rescue factor (R) was identified as the overall indicator for the prehospital medical performance for T1 and T2, and a numerical value of one (R = 1) was proposed to be the quantitative benchmark.
ConclusionA new quantitative model for benchmarking prehospital response to MCE in trauma-related MCE is proposed. Prospective studies of this model are needed to validate its applicability.
Bayram J, Zuabi S. Disaster metrics: a proposed quantitative model for benchmarking prehospital medical response in trauma-related multiple casualty events. Prehosp Disaster Med. 2012;27(2):-7.
Disaster Metrics: Quantification of Acute Medical Disasters in Trauma-Related Multiple Casualty Events through Modeling of the Acute Medical Severity Index
- Jamil D. Bayram, Shawki Zuabi
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- Journal:
- Prehospital and Disaster Medicine / Volume 27 / Issue 2 / April 2012
- Published online by Cambridge University Press:
- 17 May 2012, pp. 130-135
- Print publication:
- April 2012
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Introduction
The interaction between the acute medical consequences of a Multiple Casualty Event (MCE) and the total medical capacity of the community affected determines if the event amounts to an acute medical disaster.
Hypothesis/ProblemThere is a need for a comprehensive quantitative model in MCE that would account for both prehospital and hospital-based acute medical systems, leading to the quantification of acute medical disasters. Such a proposed model needs to be flexible enough in its application to accommodate a priori estimation as part of the decision-making process and a posteriori evaluation for total quality management purposes.
MethodsThe concept proposed by de Boer et al in 1989, along with the disaster metrics quantitative models proposed by Bayram et al on hospital surge capacity and prehospital medical response, were used as theoretical frameworks for a new comprehensive model, taking into account both prehospital and hospital systems, in order to quantify acute medical disasters.
ResultsA quantitative model called the Acute Medical Severity Index (AMSI) was developed. AMSI is the proportion of the Acute Medical Burden (AMB) resulting from the event, compared to the Total Medical Capacity (TMC) of the community affected; AMSI = AMB/TMC. In this model, AMB is defined as the sum of critical (T1) and moderate (T2) casualties caused by the event, while TMC is a function of the Total Hospital Capacity (THC) and the medical rescue factor (R) accounting for the hospital-based and prehospital medical systems, respectively. Qualitatively, the authors define acute medical disaster as “a state after any type of Multiple Casualty Event where the Acute Medical Burden (AMB) exceeds the Total Medical Capacity (TMC) of the community affected.” Quantitatively, an acute medical disaster has an AMSI value of more than one (AMB / TMC > 1). An acute medical incident has an AMSI value of less than one, without the need for medical surge. An acute medical emergency has an AMSI value of less than one with utilization of surge capacity (prehospital or hospital-based). An acute medical crisis has an AMSI value between 0.9 and 1, approaching the threshold for an actual medical disaster.
ConclusionA novel quantitative taxonomy in MCE has been proposed by modeling the Acute Medical Severity Index (AMSI). This model accounts for both hospital and prehospital systems, and quantifies acute medical disasters. Prospective applications of various components of this model are encouraged to further verify its applicability and validity.
Bayram JD, Zuabi S. Disaster metrics: quantification of acute medical disasters in trauma-related multiple casualty events through modeling of the Acute Medical Severity Index. Prehosp Disaster Med. 2012;27(2):1-6.