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Modelling complex sociotechnical systems to better understand and optimise their operation has proved a challenge and many methodologies have been put forward to use in what has been called model-based system engineering (MBSE). The appropriateness and availability of the Functional Resonance Analysis Method as a suitable candidate has been tested here, in the context of developing a methodology that can be applied currently, and in an actual real system requiring taking life and death decisions on an informed understanding of the factors and risks involved. The allocation of scarce critical care beds in hospitals is a clear example of where a better understanding of the system and a way of feeding the decision makers with crucial information on numbers and trends could help optimise very challenging responsibilities. By building on recent developments by Hill of Hollnagel’s paradigm shifting approach, this paper has laid out how FRAM system modelling can provide not only the prediction and correction mechanism provided by the new machine learning FRAM facility demonstrated by Nomoto but can add resilience to the critical decision making involved. It puts forward the case that it is certainly worth pursuing and developing further.