Essay Sample Hybrid Simulation Framework for Healthcare Data Integration: A Multi-Methodology Approach

Overview of Framework Development

Framework development entails the development of a conceptual structure that can be utilized to address or solve complex issues. The current data integration climate within healthcare facilities demands a reduction in overheads and a switch to temporary staff or/and third party organizations. Health systems stakeholders tend to disagree on the issues such as the desirability of health system outputs. Such disagreements are often cognitive because the opinions of these stakeholders are moulded by their incomparable experiences. Simulation and modeling can therefore assist in cognitive exploration as well as policy options.

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Conceptual Modeling

The dynamic and complex nature of health problems challenges the policy and decision-making processes in hospitals. Therefore, the problem characteristics that need modeling are policy development and decision making. The use of a multi-methodology approach of both the agent-based and systems dynamics models can help achieve the desired results (Mingers and Brocklesby, 1997; Zolfagharian, Romme and Walrave, 2018). The complex systems can be modeled using hybrid simulation to attain more realistic results. The Systems Dynamics (SD) is based on the principle that structure defines behavior (De La Torre et al., 2019). The use of SD will permit modeling of several individual sub-parts within the healthcare system that interacts with one another. The model is associated with higher-level problems (Wang and Cheong, 2005) and utilizes a holistic perspective to study system structures. The model entails the systems thinking process which includes nonlinearity, complexity and feedback. The SD model’s purpose will be to allow the exploration of different healthcare policy options rather than yielding the predicted numerical figures. On the other hand, the Agent-Based Model (ABM) focuses on agent interactions (Farsi et al., 2019). Most studies define an agent as a self-contained program that controls its decision-making and acts based on its perception of the environment. In ABM, the properties of these agents entail cooperative, autonomous and learning (Nguyen, Howick and Megiddo, 2020). The system complexity emerges from the varied interactions between agents. Similar to SD the dynamic processes in ABM are simulated repeatedly. The objective of the simulation is to track the agents’ interactions in the artificial surrounding and understand how global patterns emerge.

Model Development

The complexity in policymaking demands simulation by both the SD and ABM. ABM is applicable in decision making among stakeholders and SD in policy formulation hence the use of the hybrid SD-ABM simulation. the hybrid ABM-SD for the healthcare system will be developed based on the model development process described by Nasirzadeh, Khanzadi and Mir (2018); Wu et al. (2019). The process of policy formation needs the input (decisions, views) of stakeholders which implies that SD needs information from ABM. Therefore, information flow will be from ABM to SD and vice versa. This hybrid SD-ABM simulation belongs to the sequential class because there is an interaction between ABM and SD, however, the interaction is not mutual. There is a mutual flow of information between ABM and SD. ABM calculates the perceptions, decisions, and experiences of stakeholders while considering their environment, interactions and unique differences. This information will then make up the input for the SD model. These decisions and views are the interface variables which will be sent to the SD as input.

Experimenting and Testing

In order to evaluate the performance of the hybrid SD-ABM simulation framework, the model will be implemented in a healthcare facility transforming into the use of electronic health records. One of the significant factors influencing this process is the development of a successful policy to guide the transition process. The policy formulation process is affected by the stakeholders’ decisions and views of the current system, as well as the complicated interrelated structure of influencing factors such as their experiences, unique differences, the interactions among them and their perceptions of the health system. The hybrid SD-ABM model could therefore be useful to account for these factors. The heterogeneity of different agents in this case stakeholders will be modelled using ABM (Mustafee and Powell, 2018) while their complex interrelated structure will be modelled using the SD (Powell and Mustafee, 2017) to achieve the desired results.

References

De La Torre, G., Gruchmann, T., Kamath, V., Melkonyan, A., & Krumme, K. (2019). A System Dynamics-Based Simulation Model to Analyze Consumers’ Behavior Based on Participatory Systems Mapping–A “Last Mile” Perspective. In Innovative Logistics Services and Sustainable Lifestyles (pp. 165-194). Springer, Cham.

Farsi, M., Erkoyuncu, J. A., Steenstra, D., & Roy, R. (July 01, 2019). A modular hybrid simulation framework for complex manufacturing system design. Simulation Modelling Practice and Theory, 94, 14-30.

In White, L., In Kunc, M., In Burger, K., & In Malpass, J. (2020). Behavioral operational research: A capabilities approach.

Mingers, J., & Brocklesby, J. (1997). Multimethodology: Towards a framework for mixing methodologies. Omega, 25(5), 489-509.

Mustafee, N., & Powell, J. H. (2018, December). From hybrid simulation to hybrid systems modelling. In 2018 Winter Simulation Conference (WSC) (pp. 1430-1439). IEEE.

Nasirzadeh, F., Khanzadi, M., & Mir, M. (January 01, 2018). A hybrid simulation framework for modelling construction projects using agent-based modelling and system dynamics: an application to model construction workers' safety behavior. International Journal of Construction Management, 18, 2, 132-143.

Nguyen, L. K. N., Howick, S., & Megiddo, I. (2020, March). A hybrid simulation modelling framework for combining system dynamics and agent-based models. In Operational Research Society Simulation Workshop 2020.

Powell, J. H., & Mustafee, N. (2017). Widening requirements capture with soft methods: an investigation of hybrid M&S studies in health care. Journal of the Operational Research Society, 68(10), 1211-1222.

Wang, W., & Cheong, F. (2005). A Framework for the System Dynamics (SD) Modeling of the Mobile Commerce Market 1.

Wu, C., Chen, C., Jiang, R., Wu, P., Xu, B., & Wang, J. (2019). Understanding laborers’ behavioral diversities in multinational construction projects using integrated simulation approach. Engineering, Construction and Architectural Management.

Zolfagharian, M., Romme, A. G. L., & Walrave, B. (2018). Why, when, and how to combine system dynamics with other methods: Towards an evidence-based framework. Journal of Simulation, 12(2), 98-114.