The FRAM approach has identified how a paediatric trauma system functioned in response to the Manchester Arena Attack. The observed and reported resilient response of the paediatric hospital3 is directly related to the system’s ability to monitor, anticipate, learn, and respond6, both during and after the mass casualty incident. The functions identified in the model and how these functions are interconnected by couplings, provide key insights into the behaviour of the system. The function “to be surgical commander”, can be observed to provide the resilience potential to monitor and anticipate throughout the system during the incident. This key function was itself a spontaneous adaptation to practice by a single surgeon, not detailed in the major incident plan, in advance of the incident. In practice the function was achieved by having a senior surgeon on the “shopfloor,” directly observing how care was being provided, as opposed to being sited in a command centre, within the reach of couplings from this function evident. A practical sequelae of FRAM modelling of the response is that this function has now been established in the major incident plan for a group of hospitals that includes the paediatric hospital studied. A second key function can be identified with many outputs, that of “to review as part of a multi-disciplinary team”. In addition to monitoring patients and ensuring holistic care to children and families in the ward setting, this function also enhanced resilience by capturing learning during the incident, in terms of extent of potential injuries, occult injuries to hunt for, understanding of potential human cross contamination due to shrapnel and other factors.4 Noticeable by its absence in the model is a function to anticipate the number of incoming patients, which could have been achieved by establishing close contact with the scene of the incident. Review of all interview transcripts highlighted the lack of communication from the scene into the hospital.
A further key function is that of “to decide the patient destination”, this enhanced the response resilience potential of the system, in terms of damage control surgery, further damage control resuscitation or normal critical care / ward care. Further examination of how and where this function was achieved is warranted; particularly when one considers the one-way system of resuscitation-radiology-decision-making to Theatres or PICU, where it was imperative that patients did not return to the resuscitation bay after leaving for radiology.
The modelling has also highlighted the central and rate-limiting role of CT scanning has on the response of the system during the major incident. Due to the high likelihood of blast injuries, a high number of children were CT scanned. For the most seriously injured this was after approximately thirty minutes of resuscitation (including intubation, ventilation, and sedation) prior to CT in one of the three staffed resuscitation bays initially available. The model highlights the key function of “to perform CT scan”, particularly when the hospital has only one scanner available. If, unlike on the night of the event, more than three patients arrived contemporaneously each requiring resuscitation, then a second function of “to re-triage for CT during resuscitation” would be required to ensure the finite resource of the CT scanner was not targeted to the wrong patients during the one-way resuscitation flow described above. This understanding of how variability within the system which alone or in combinations may impact care provision is key to allow an after-action review of resilience. In this way the FRAM model is providing visual evidence akin to the descriptive evidence of a verbal, reflection on-action review experiential learning framework, which is well established in the debriefing literature.18 Such a review of the major incident using the FRAM model in this way, allows further development of the above-described resilience potentials in advance of any future major incidents. Which in turn can facilitate re-design of a Major Incident Plan (MIP), as has happened at the study site.
Analysis of the expected mean process timings from FRAM model and mean actual process timings of the MCI (Table 2) also provided some other valuable insights for future MIP development. The model predicted that on average a patient would arrive in the CT scanner every 37 minutes. The actual average time was 38 minutes from arrival to commencing scanning, providing some construct validity to the modelling process. However, based on a damage control operative time of sixty minutes, analysis of the model highlights that actual times in theatre were more than twice this. It has been recognised that improvements in damage control timing is required, with a second anaesthetist in theatre now monitoring this in the current MIP. Comparison of the model and actual timings also shows a significant overshoot on predicted timings for post-operative PICU bed availability after the end of surgery, which is also being optimised.
Having an established, verified and internally validated a FRAM model of a MCI response, one can then advance further. It should then be possible to provide potential insights on the factors that promote resilience in healthcare systems exposed to extreme perturbations, that are key to policy makers and health care managers as part of risk and vulnerability analyses.20,21. Each FRAM function is in essence a mathematical equation describing the requirements to pass from its input stage to its output stage.9 With each function being described by its own aspects (Timings, Controls, Preconditions required, and Resources used up)8, variability can be introduced qualitatively into these aspects of each function in the model, for example too early, too late or on time, or precise, imprecise or not at all8. The FRAM model directly allows the visualisation of such downstream consequences of upstream changes in the system. One can also introduce variability deliberately or observe the consequence of spontaneous adaptations to practice in the management of specific scenarios.14 This Structured What-If FRAM approach has been utilised to observe the impact of approximate adjustments or adaptations to practice in trauma care on established key performance indicators.14 As such one can introduce functions directly into this model and theoretically test and visualise the impact of the change to system.
However, with the FRAM model in essence a cloud of inter-connected mathematical equations, it is now possible to explore variability quantitatively by inputting numerical “Metadata“ instead of the above qualitative data for the aspects of each function in the system. This numerical data can be inputted from clinical incidents or from developed fictional data specifically designed to stress test the system. An example of how this can be achieved is by cycling the FRAM model until finite resources for example available operating theatres, staff or PICU beds are used up. With this meta-data approach, it becomes possible to use the FRAM model to predict downstream impacts of upstream variability .21 Moreover, one can start to stress the system and observe how it responds in different scenarios.21 It is also now possible to explore the resilience of the system further utilising metadata with the resilience potentials used directly in the system in the form of functions “to respond”, “to monitor”, “to anticipate” and “to learn” as described by Nomoto21, and then running consecutive iterations of the model to mathematically observe how the data variability may directly impact the system’s ability to maintain its resilience.21 This approach will constitute the next stage of our research utilising the simplified WAI model (Fig. 3) to explore how the resilience of a paediatric major trauma system responds to differing conditions.
Where previously a “factory physics” approach of calculating how long a function takes and how many resources are used up, for example in a tabletop exercise, now the cycling FRAM model can potentially explore these scenarios. With a verified mathematical model, a series of Emergency Preparedness Resilience and Readiness (EPRR) scenarios could be explored, visualised and findings summated and kept. This represents an important step forward in EPRR analyses, as this stepwise approach can be applied to any healthcare system and any perturbations to such system.