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COVID-19

A Novel Decision Support System for Physician Scheduling during Public Health Crises

Strategic Insights

— 1 min read

A recent study has shed new light on the challenges faced by hospitals during public health crises, such as the COVID-19 pandemic. The research, conducted at the University of Adelaide, highlights the importance of effective physician scheduling to ensure the quality of care during such times. The study proposes a novel mixed-integer linear programming (MILP) model to optimize physician scheduling, taking into account the preferences of physicians to improve their satisfaction and productivity.

Key Takeaways:

  • The study was conducted in collaboration with the Center for Sustainable Operations and Resilient Supply Chain at the University of Adelaide.
  • The research focused on developing a decision support system for scheduling physicians during public health crises, such as the COVID-19 pandemic.
  • The proposed MILP model aims to maximize the fairness in the distribution of workload among physicians, considering their preferences and satisfaction.
  • The effectiveness of the proposed model was examined using data from a hospital in Iran during the outbreak of the coronavirus disease (COVID-19).
  • The hospital consisted of 15 regular departments, served by 79 physicians, which was increased to 18 departments during the pandemic.
  • The proposed model considers two indicators for physicians' satisfaction: equitable shifts distribution and physicians' preferences.
  • The study found that considering physicians' preferences significantly affects the physician scheduling, and it can lead to improved productivity and service quality.

Statistics:

  • 79 physicians were serving the 15 regular departments of the hospital before the pandemic.
  • 3 additional departments were added to the hospital to serve COVID-19 patients during the pandemic, increasing the total number of departments to 18.
  • The proposed MILP model was implemented with and without considering physicians' preferences to examine the effect on physician scheduling.
  • The study used data from the hospital in Iran, which has 15 regular departments and 79 physicians.

Sources:

  • Akerkar, S. et al. (2020). "COVID-19: A Global Pandemic." Journal of Clinical Virology, 127, 104585.
  • Kannan, D. et al. (2025). "A Decision Support System for Physician Scheduling During a Public Health Crisis: A Mathematical Programming Model." Annals of Operations Research, 2025.
  • NewsRx (2025). "Research Conducted at University of Adelaide Has Updated Our Knowledge about COVID-19 (A Decision Support System for Physician Scheduling During a Public Health Crisis: A Mathematical Programming Model)." Medical Letter on the CDC & FDA, 166.

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