“…Results from the 7000 runs were obtained and analyzed. Reasonableness of the designed representative urban hospital and general modeling approach was assessed 10 under routine conditions. Initial MCI model run results were discussed with emergency managers, 11 and the model was further tuned to incorporate elements that were not initially identified as key.…”
Section: Resultsmentioning
confidence: 99%
“…For certain MCIs, it may be important to include decontamination units, burn units, or other specialized services. In this model, patient care paths are a function of patient medical needs and MCI-based operational strategies.Figure 2(A) Hospital Patient Flow Model 10 . (B) ED in Routine Conditions 10 .…”
Section: Methods: Hospital MCI Modelingmentioning
confidence: 99%
“…The modeling approach extends the work that assesses a hospital operating in routine circumstances to MCIs. 10 This approach explicitly recognizes that the performance of the whole hospital depends on the functionality of its units and the specific needs and care paths of the patients. Since services share resources in hospitals, shortfalls in one unit can negatively affect services provided in another unit.…”
Section: Introductionmentioning
confidence: 99%
“…A discrete event simulation modeling approach that uses an underlying queueing network conceptualization of the hospital’s critical units was taken. The modeling approach extends the work that assesses a hospital operating in routine circumstances to MCIs 10 . This approach explicitly recognizes that the performance of the whole hospital depends on the functionality of its units and the specific needs and care paths of the patients.…”
Section: Introductionmentioning
confidence: 99%
“…Summary of Assumptions Compared Under Routine10 and MCI Conditions ED, emergency department; ICU, intensive care unit; OR, operating room; PACU, postanesthetic care unit; RN, registered nurse; SICU, surgical intensive care unit; TAT, turnaround time.…”
Mass casualty incidents are a concern in many urban areas. A community's ability to cope with such events depends on the capacities and capabilities of its hospitals for handling a sudden surge in demand of patients with resource-intensive and specialized medical needs. This paper uses a whole-hospital simulation model to replicate medical staff, resources, and space for the purpose of investigating hospital responsiveness to mass casualty incidents. It provides details of probable demand patterns of different mass casualty incident types in terms of patient categories and arrival patterns, and accounts for related transient system behavior over the response period. Using the layout of a typical urban hospital, it investigates a hospital's capacity and capability to handle mass casualty incidents of various sizes with various characteristics, and assesses the effectiveness of designed demand management and capacity-expansion strategies. Average performance improvements gained through capacity-expansion strategies are quantified and best response actions are identified. Capacity-expansion strategies were found to have superadditive benefits when combined. In fact, an acceptable service level could be achieved by implementing only 2 to 3 of the 9 studied enhancement strategies. (Disaster Med Public Health Preparedness. 2018;page 1 of 13).
“…Results from the 7000 runs were obtained and analyzed. Reasonableness of the designed representative urban hospital and general modeling approach was assessed 10 under routine conditions. Initial MCI model run results were discussed with emergency managers, 11 and the model was further tuned to incorporate elements that were not initially identified as key.…”
Section: Resultsmentioning
confidence: 99%
“…For certain MCIs, it may be important to include decontamination units, burn units, or other specialized services. In this model, patient care paths are a function of patient medical needs and MCI-based operational strategies.Figure 2(A) Hospital Patient Flow Model 10 . (B) ED in Routine Conditions 10 .…”
Section: Methods: Hospital MCI Modelingmentioning
confidence: 99%
“…The modeling approach extends the work that assesses a hospital operating in routine circumstances to MCIs. 10 This approach explicitly recognizes that the performance of the whole hospital depends on the functionality of its units and the specific needs and care paths of the patients. Since services share resources in hospitals, shortfalls in one unit can negatively affect services provided in another unit.…”
Section: Introductionmentioning
confidence: 99%
“…A discrete event simulation modeling approach that uses an underlying queueing network conceptualization of the hospital’s critical units was taken. The modeling approach extends the work that assesses a hospital operating in routine circumstances to MCIs 10 . This approach explicitly recognizes that the performance of the whole hospital depends on the functionality of its units and the specific needs and care paths of the patients.…”
Section: Introductionmentioning
confidence: 99%
“…Summary of Assumptions Compared Under Routine10 and MCI Conditions ED, emergency department; ICU, intensive care unit; OR, operating room; PACU, postanesthetic care unit; RN, registered nurse; SICU, surgical intensive care unit; TAT, turnaround time.…”
Mass casualty incidents are a concern in many urban areas. A community's ability to cope with such events depends on the capacities and capabilities of its hospitals for handling a sudden surge in demand of patients with resource-intensive and specialized medical needs. This paper uses a whole-hospital simulation model to replicate medical staff, resources, and space for the purpose of investigating hospital responsiveness to mass casualty incidents. It provides details of probable demand patterns of different mass casualty incident types in terms of patient categories and arrival patterns, and accounts for related transient system behavior over the response period. Using the layout of a typical urban hospital, it investigates a hospital's capacity and capability to handle mass casualty incidents of various sizes with various characteristics, and assesses the effectiveness of designed demand management and capacity-expansion strategies. Average performance improvements gained through capacity-expansion strategies are quantified and best response actions are identified. Capacity-expansion strategies were found to have superadditive benefits when combined. In fact, an acceptable service level could be achieved by implementing only 2 to 3 of the 9 studied enhancement strategies. (Disaster Med Public Health Preparedness. 2018;page 1 of 13).
Disasters and other emergency events have complex effects on human systems, particularly if the events are severe or prolonged. When these types of events happen in rural communities, the resources of the local public health, healthcare, and emergency response organizations can be quickly depleted or overwhelmed. Planning for emergencies can help to mitigate their impact. Model-based systems engineering (MBSE) methods, including computer simulations, can provide insight on how best to prepare for these events and to explore the effects of varying approaches and resource utilization. To best apply these methods for improving disaster management in rural settings, a synthesis of the current body of evidence in this field is needed. The objective of this scoping review was to provide a descriptive overview of the application of computer simulation based on MBSE approaches to disaster preparedness and response for rural healthcare systems. Six studies met inclusion criteria, and varied in terms of MBSE method used, healthcare setting, and disaster type and context considered. We identified a gap in the research regarding the application of MBSE approaches to support rural healthcare disaster preparedness planning efforts. Model-based systems engineering and systems thinking, therefore, represent novel methods for developing tools and computational simulations that could assist rural communities better prepare for disasters.
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