|dc.description.abstract||Contemporary society is becoming increasingly reliant on critical infrastructure (CI), which contributes to the wellbeing of its populations. One of the CI sectors that most strongly supports society is healthcare, with hospitals a critical healthcare infrastructure that contributes to society by providing numerous healthcare services, especially when dealing with the effects of disastrous events. Any disruption in hospitals’ performance—either by physical damage or functional disruption—can have negative consequences on their effective response to disastrous events, which can worsen the outcomes of the emergency situation. Adverse events can affect the overall performance of healthcare systems, directly or indirectly, by compromising the lifeline infrastructure network, with cascading and amplifying effects from malfunctions and failures throughout the CI network. Thus, improving the resilience of hospitals’ functional performance (HFP) is critical to minimise interruption to hospital services.
This study’s review of the literature highlights the need to approach the issue of HFP resilience from both static and dynamic perspectives to provide a comprehensive understanding of the subject matter. Thus, this research aimed to achieve the following objectives:
- identify the performance metrics through which the resilience of hospitals’ performance can be evaluated
- explore the existing interdependencies and complex interactions between the Resilience metrics related to hospitals’ internal performance, and the performance metrics related to hospitals’ external CI and stakeholders
- develop a model to explore the interactions between hospitals’ critical functions when operating under surge procedures and analyse the effects of functional variability on HFP resilience.
The outcome of this review of the relevant literature was the development of a functional resilience index (FRI) for evaluating hospitals’ resilience in the face of disruptive events. Following this, this study narrowed the FRI, to a list of metrics and their relative sub-metrics to those elements that a group of subject matter experts (SMEs) collectively believed contribute the most to HFP resilience. Finally, SMEs were interviewed to identify the pairwise relationships among the list of metrics and their relative sub-metrics. Based on their view, the data was prepared for being used by structural modelling techniques (interpretive structural modelling [ISM] and Matrice d’Impacts Croisés Multiplication Appliquée á un Classement or ‘Cross-Impact Matrix Multiplication Applied to Classification’ [MICMAC]). ISM provided a hierarchy within which resilience metrics were classified into different brackets, based on the significance of their influence on HFP. Use of MICMAC highlighted the direct and indirect influences of the chosen metrics and identified their influence on overall HFP resilience. Hence, by using structural analysis techniques, the extent of the relative influence and dependence of internal and external resilience metrics were highlighted and their overall effect on systems was identified.
To address the third objective from a dynamic perspective, functional resonance analysis method (FRAM) was deployed to provide a macro analysis of the interactions among hospital system functions under surge conditions. The use of FRAM as the modelling technique helped address the extent of system adaptability to change and explore the hidden effects of different functions on overall system performance. The modelling involved identification of surge functions and the required conditions for functions generating their intended outcomes.
In the next step, this study identified the pathway through which functional variabilities may propagate throughout the system. To achieve this objective, application of FRAM was integrated with application of the resilience analysis matrix to analyse HFP. The results identified 23 couplings in 153 interactions between 29 functions that had the potential to affect overall HFP. The approach of this research revealed how managing the variability of certain interactions can enhance the efficiency and effectiveness of HFP in dealing with disruptive events. Finally, a number of instantiations were developed to study HFP under different surge scenarios. For scenario analysis, in this study, FRAM was integrated with Monte Carlo simulation to identify the likelihood of hospital functions varying and generating functional resonance.
The outcome of studying HFP with respect to complexity theory was the development of a five-tiered hierarchy to classify the criticality of the couplings between different functions. This hierarchy helped uncover which functions are sensitive to certain types of variability in their input aspects. By discovering the dynamics between the system functions and the way they behave in different scenarios, decision makers can make appropriate decisions to monitor, mitigate and manage those functional variabilities and functional resonance. In this study, it was suggested that, by exploring the interactions among internal and external resilience indicators (which represent the outcomes of different resilience practices and strategies), decision/strategy makers can target those sensitive functions and establish benchmarks to dampen the functional variabilities or isolate the functions to manage functional resonance.
In summary, by analysing HFP from static and dynamic methods, this study provides a unique perspective into the topic of hospital functional resilience when dealing with surge conditions. This approach provides a better understanding of the way in which hospitals’ performance can deviate under stress and deal with uncertain conditions, and the way that implementation and improvement of various strategies and practices can effectively improve HFP.||