Tutorial in medical decision modeling incorporating waiting lines and queues using discrete event simulation

Area of Science:

• Health economics
• Operations research
• Health services research

Background:

• Decision-analytic models in healthcare often overlook delays and resource limitations.
• Waiting times significantly impact treatment outcomes and costs, yet are frequently unconsidered.

Purpose of the Study:

• To introduce queuing theory and simulation techniques for health care decision-analytic modeling.
• To demonstrate how to incorporate waiting times and resource constraints into models.

Main Methods:

• Queuing theory provides mathematical analysis of waiting systems.
• Discrete event simulation is employed for complex systems with resource capacities and queues.
• Percutaneous coronary intervention for coronary artery disease is used as a case study.

Main Results:

• Queuing theory offers performance measures for waiting systems.
• Simulation allows explicit modeling of resource capacities and patient queues.
• Integrating queuing concepts enhances the realism of decision-analytic models.

Conclusions:

• Understanding queuing theory is crucial for accurate health care modeling.
• Simulation techniques effectively incorporate waiting times and resource limitations.
• This approach leads to more robust and realistic health care decision-making.