On the evening of the 22nd of May 2017, a terrorist denoted an improvised explosive device in the foyerof the Manchester Arena as concert goers, children and adults emerged, killing 23 people (including the attacker). Paediatric Mass Casualty Incidents (MCI) are rare in the context of an individual clinician orinstitution, but children are often involved when MCI occur.1 A paediatric MCI should provide anopportunity to explore optimal human and organisational performance, to apply that learning to improvefuture patient outcomes. Resilience dened as “the intrinsic ability of a system to adjust its functioning prior to, during, or following changes and disturbances so that it can sustain required operations, evenafter a major mishap or in the presence of continuous stress”, is an essential prerequisite of a MajorTrauma Centre (MTC). A MTC is a complex socio-technical healthcare system designed to respondeffectively to a myriad of clinical scenarios, within which healthcare staff work adaptively to providepatient care.
In the immediate aftermath of the Manchester Arena Attack the nearby paediatric MTCdemonstrated both resilient elements and a series of adaptations to improve patient outcomes during theMCI. During the initial response to the attack twenty-two children aged between eight to fteen years and veparents presented with blast injuries to the paediatric MTC. One child died in the Paediatric EmergencyDepartment (PED), fourteen children were admitted, four going directly to the operating theatres and six tothe Paediatric Intensive Care Unit (PICU).MCI involving children are rare events. However, learning from such experiences, is a fundamental element of resilience. A lack of in-depth learning after events, severely hampers the capability to respondto future MCIs that may present to a UK MTC. Modelling is one way of learning, with a model being aformal system that can be used to express or represent the “objects and their relationships in the world”that are being investigated.7Functional Resonance Analytical Methodology (FRAM) facilitates the modelling of complex adaptive systems.
With condence developed in the model, actual timings during the MI were compared with thoseproduced by the model using expected timings for functions. These expected Work As Imagined ndingswere Function Process Time (Tp) the time it took for a function to go from input to output, the WAIFunction Output Lag Time (To) the time it took to move from one function ending to starting anotherfunction and WAI Total Time of Functions (Tt) the total time for functions in the system.
These expectedtimings were constructed on discussion with subject matter experts, for example discussion with seniorPED nurse regarding how many minutes it takes to triage a severely injured child. The exception was thefunction “To stabilise in Resus” which was theoretically derived from a series of simulated resuscitations suggesting an average resuscitation time of thirty minutes for trauma patients published previously.14 Atthe time of the Arena Attack the hospital did not have an electronic patient record, the reliable Work AsDone (WAD) data was taken from actual timings to commence CT scanning and times of entering andleaving theatre from theatre software. Mean WAD Function Start Times and Function Process Times arepresented. Table 2 shows the expected mean timings produced by the model of the MCI and timingsrecorded during the MI for the rst eight patients, three of whom went to theatre.
Publication
A Functional Resonance Analytical Methodology exploration of the essential functions of a paediatric major trauma centre responding to a mass casualty incident
February 2024 DOI: 10.21203/rs.3.rs-3937622/v1