Risk-Adjusted Healthcare Staffing Policy During the Pandemic – Modeled with Simulation Software

The use of agent-based modeling (ABM) has gained popularity in healthcare operations and policy management and particularly, in the infectious disease prevention and control domain. ABM approach has provided additional insights with more realistic models for disease spread and assessment of various healthcare service operations. This modeling technique helps simulate the actions of various autonomous individuals including physicians, nurses, and patients, jointly with the interactions among them.

In this research, using agent-based capabilities of the simulation software provided the flexibility to consider each anesthesiologist as a unique agent. Each agent had specific parameters and attributes and interacts with other anesthesiologists working in the same hospital.

Additionally, this allowed the flexibility to model each hospital as an agent with further segregation into groups within each hospital. Moreover, the capability to track the current state (in terms of Susceptible-Exposed-Infected-Recovered, or SEIR) of each physician made this the best option to model the rapidly spreading COVID-19.

In pursuit of the best healthcare staffing policy to limit the infection spread within the anesthesia department, the researchers considered three different staff scheduling policies:

  • Inter-Hospital Mixing (Baseline policy)
  • Inter-Group Mixing
  • No Mixing

The proposed healthcare policy simulation model could help the hospital administrators adjust staffing models based on population prevalence and hospital prevalence of disease. Also, it could help to better manage elective surgical volume increases.


Healthcare policy modeling with simulation software

Healthcare policy modeling with simulation software

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