Leveraging Simulated Annealing for Patient Appointment Management
Finding a suitable way to optimize the scheduling madness
Patient appointment management is a complex puzzle which is very common in healthcare industry. There are various factors like doctors’ availability, duration of appointment, emergency or urgency, and preferred time slots which need to be considered.
The process optimization tasks involve finding a set of problem parameter values that corresponds to minimization of some cost value. There are class optimization algorithms called “Heuristic Optimization” to solve real-world complex nonlinear optimization problems.
Many of them are inspired actually by natural processes. Simulated Annealing is one such algorithm. We can use this algorithm to find optimum patient appointments in a doctor’s office. The implementation is part of my Python package called “Arotau”. It contains various heuristic optimization algorithms implementation.
What is Simulated Annealing?
Simulated Annealing (SA) algorithm is inspired by the physical annealing process. As we know, in physical annealing, some material is heated up and then gradually cooled down and during that time the material is shaped into the desired form. There is temperature parameter in SA. As the algorithm iterates temperature is gradually brought down. At high temperatures more exploration is allowed to enable escape from local minima. Exploration is encouraged by probabilistically accepting worse solutions. At low temperature, worse solutions are less likely to be accepted promoting exploration.
Like any heuristic optimization, SA does not guarantee optimum solution. It’s sub optimal, but computational resource usage is much less than brute force approach, which is often not even feasible because of combinatorial explosion.
Learn More About Simulated Annealing and It’s Application Here: Patient Appointment Management Optimization with Simulated Annealing
Conclusion
SA is a simple heuristic optimization solution for solving complex optimization problems. In the article referred, we can see how it can be applied to optimize doctor’s office patient appointments. It can be used to solve various optimization problems including hyper parameter tuning for machine learning models.