Scheduling Optimization in Ophthalmology using Multi-Objective Integer Models

Abstract

This paper studies a scheduling problem in which patients request appointments at specific future days within a specialty definite time window. This research is inspired by a study of Ophthalmology scheduling practices at the Clinic Hospital at Santiago de Cuba (Cuba). In this hospital, patients do not know in advance neither the proximate time at which they will be seen by the doctor nor the total amount of time to be spent at the Hospital. In this article, an optimal distribution of patients during the designing scheduling process has been performed. The optimality condition obeys to two different goals: to give the appointments as soon as possible and to minimize the time that a patient would spend at the hospital to complete a protocol. We formulate this problem as a Multi-Objective Integer Problem (MOIP) and compare the performance of the resulting MOIP policies with traditional practices decision rules for the diagnosis and treatment of ophthalmology diseases. We show that this method outperforms traditional methods by far. Specifically, it reduces the total diagnosis time for cataract, cornea and glaucoma diseases by 70% on average or, in other words, by roughly two hours, relative to the standard approach. Arguably, this translates into improved patient satisfaction and efficiency in the use of resources in health services.

This paper studies a scheduling problem in which patients request appointments at specific future days within a specialty definite time window. This research is inspired by a study of Ophthalmology scheduling practices at the Clinic Hospital at Santiago de Cuba (Cuba). In this hospital, patients do not know in advance neither the proximate time at which they will be seen by the doctor nor the total amount of time to be spent at the Hospital. In this article, an optimal distribution of patients during the designing scheduling process has been performed. The optimality condition obeys to two different goals: to give the appointments as soon as possible and to minimize the time that a patient would spend at the hospital to complete a protocol. We formulate this problem as a Multi-Objective Integer Problem (MOIP) and compare the performance of the resulting MOIP policies with traditional practices decision rules for the diagnosis and treatment of ophthalmology diseases. We show that this method outperforms traditional methods by far. Specifically, it reduces the total diagnosis time for cataract, cornea and glaucoma diseases by 70% on average or, in other words, by roughly two hours, relative to the standard approach. Arguably, this translates into improved patient satisfaction and efficiency in the use of resources in health services.