A SIMULATION STUDY OF INTERVENTIONS TO REDUCE APPOINTMENT LEAD-TIME AND PATIENT NO-SHOW RATE Ronald E. Giachetti Dept. of Industrial & Systems Engineering 10555 W. Flagler Street Miami, FL 33174, U.S.A. ABSTRACT A problem in health care is the lengthy waiting time for patients to receive an appointment. Long appointment de- lays cause patient dissatisfaction with the health care clin- ic and also has clinical ramifications. Long appointment delays are also found to increase patient no-shows, which further wastes medical resources and leads to a decrease in clinical care. A model of the health care clinic is built to understand the casual relationships in the system con- tributing to the problem. The model is used to investigate two possible policies. A policy of eliminating multiple appointment types can be effective in reducing appoint- ment delay and as a consequent no-shows. Using data from several clinics, our study also suggests that an effec- tive policy is to segregate habitual no-show patients and double-book them whenever they make appointments. This policy is equally effective as general overbooking without penalizing the entire patient population. 1 INTRODUCTION Many clinics suffer from two related problems. First, is the long waiting time, which we will call appointment de- lay, until the next available appointment. Appointment delay is part of what the Institute of Medicine calls time- liness, and was identified as a primary area needing im- provement (Institute_of_Medicine 1996). Second, is the high incident of no-shows in many clinics. No-shows are patients who do not appear for their scheduled appoint- ment, thus wasting resources of the clinic by denying the opportunity of using that appointment slot for other pa- tients. To deal with these problems clinics try various inter- ventions. In some cases the interventions work, in other cases they fail. What is lacking in the literature is a full and clear understanding of the relationships between pa- tient demand, patient behavior, clinic capacity, and clini- cal policies. Many of the components have been studied individually, but unless models are built of the entire sys- tem practitioners will fail to see how their decisions affect other system aspects. Moreover, most analytical and si- mulation models treat patient behavior as unaffected by clinical policies. The lack of representing human behav- ior in response to system policy decisions misses signifi- cant feedback influencing overall performance. In this paper, system dynamics simulation is used to model the feedback between clinic interventions and pa- tient demand behavior. We draw upon known behaviors in queueing theory as well as what has been determined from empirical studies of clinical practices. We develop a model that relates patient demand, patient behavior, clinic capacity, and clinic policies to understand how clinical interventions can influence patient behavior. The result- ing model is used to evaluate various interventions under some basic assumptions of clinic operations. An analysis of the policies is presented, which is followed by conclu- sions and suggestions for future research. 2 LITERATURE REVIEW In the UK and Europe the waiting list for patients to see health care providers is a political and social issue that has received wide attention from the research community. Clearly, the waiting list is a queue, and some studies have analyzed it from this perspective (Worthington 1987). Wolstenholme (1993) uses system dynamics to show how the waiting list grows due to the implementation of a na- tional policy change. Coyle (1984) creates a model to show how admissions policies and treatment durations can cause the waiting list to grow by recycling patients. Van Ackere and Smith (1999) analyze policies and their effect on the waiting list at the national level in the UK; González-Busto and García (1999) do a similar study for Spain. These studies underline the need to consider feed- back between policy decisions and their affect on system performance, in this case the length of the waiting list. In the US, where there is no national health care sys- tem, there are far fewer studies of waiting for appoint- ments, hereafter called appointment delay. There is more 1463 978-1-4244-2708-6/08/$25.00 ©2008 IEEE Proceedings of the 2008 Winter Simulation Conference S. J. Mason, R. R. Hill, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds.