PROJECT TITLE :

Optimal Appointment Rule Design in an Outpatient Department

ABSTRACT:

Appointment systems are used by hospitals to manage patient access. The duration of the booking window, block capacity, and block service time are all important factors in achieving efficient and timely access to healthcare. We apply a renewal process model to evaluate interday appointment planning and propose improved appointment rules for hospitals, particularly those with low or insufficient resources, in this work. To determine the steady-state distribution, we show an embedded Markov chain. We suggest three performance indicators for our evaluation, namely slot utilization, appointment success rate, and patient waiting time, to balance the waiting time and probability of healthcare access. The impact of each appointment rule parameter is then investigated numerically. Extending the booking window does not considerably lessen system congestion, according to qualitative findings, and a limited appointment block is a better design for highly in-demand clinicians. Our concept and method are used to create an ideal appointment rule for a real hospital in Beijing, China. The enhanced appointment rule is practical and useful for hospital management' decision-making. Practitioners, take note: An appointment system with a short booking window is a practical way to cut down on wait time. Potential patients, on the other hand, will be denied entrance to healthcare systems, particularly those with a high arrival rate. We aim to assess and build an improved appointment rule that includes booking window duration, block capacity, and block service time to balance the tradeoff between reducing waiting time and boosting healthcare access probability. Extending the booking window does not considerably lessen system congestion, according to sensitivity analysis, and a narrowed appointment block is a better design for highly in-demand clinicians. We create an ideal appointment rule for a real Chinese hospital using our model. The results demonstrate that depending on the conditions, improvement can be large (more than 60%, for example).