As the first point of contact between patients and health service providers, appointments systems are important determinants of overall patient satisfaction. They can increase the utilization of resources, match workload to available capacity, and smooth the flow of patients. Yet, important policy decisions concerning the management of appointment systems are made without the benefit of decision-support tools. For example, some service providers are adopting a same-day appointments system. Under this scheme, physicians are encouraged to clear their backlog and to attempt to see all those patients who call for appointments the same day. While such a system has been successful in preliminary implementation at several large clinics, its long-term success is not guaranteed. More importantly, there is not enough research in the literature to provide an understanding of its success factors, conditions under which it may falter, and how it could be monitored and improved. The overall goal of this research project is to fill this gap by developing a suite of analytical tools that can be used to set policy parameters. Specifically, the hypotheses that will be addressed in this project are as follows: 1. a physician whose work schedule is less irregular, who has a more stable work schedule, should have a smaller target number of open slots; 2. more homogeneous work-day schedules (with fewer non-standard appointment lengths) are better suited for same-day appointments; 3. there exists a threshold ratio of demand to capacity above which the ability to successfully implement a same-day appointment system is seriously jeopardized; 4. mathematical models can help identify optimal decision rules governing timing of open slots, release of protected slots, and double-booking. The project goals will be achieved via a multi-pronged approach. Data from a clinic that has implemented a same-day appointments system will be used to test the above hypotheses, and to estimate parameter values. At the same time, mathematical- and computer-simulation models will be developed to evaluate the robustness of the existing management practices and to identify optimal decision rules. This study will impact practice in two ways: 1) it will allow policy makers to use a scientific approach to set policy parameters; and 2) it will provide a framework for building of the next generation of computer-assisted appointment systems that allow patients to set appointments via the Internet.
