Effects of Rescheduling on Patient No-Show Behavior in Outpatient Clinics

Abstract

We study the effects of rescheduling on no-show behavior in an outpatient appointment system for both new and follow-up patients. Previous literature has primarily focused on new patients and investigated the role of waiting time on no-show probability. We offer a more nuanced understanding of this costly phenomenon. Using comprehensive clinical data, we demonstrate that for follow-up patients, their no-show probability decreases by 10.9 percentage points if their appointments were rescheduled at their own request, but increases by 6.2 percentage points if they were rescheduled by the clinic. New patients, in contrast, are less sensitive to who initiates rescheduling. Their no-show probability decreases by 2.3 percentage points if their appointments were rescheduled at their own request, and increases by 3.2 percentage points—but is statistically insignificant at the 10% level—if they were rescheduled by the clinic. New patients are more concerned about waiting time compared with follow-up patients. For patients whose appointments were not rescheduled, new patients’ no-show probability decreases by 1.3 percentage points if their waiting time is reduced by one week, but the waiting time has a small and statistically insignificant effect on follow-up patients’ no-show probability. Using data-driven simulation, we conduct counterfactual investigation of the impact of allowing active rescheduling on the performance of appointment systems. In particular, allowing the flexibility of patient rescheduling can reduce the overall no-show rate and increase system utilization, but at a cost of increased wait time for new patients. If patients are able to reschedule at least one week in advance, new patients’ wait time is largely reduced, whereas the no-show rate remains the same; this is equivalent to the effect of a 5% increase in the clinic’s capacity.

Optimal scheduling in cloud healthcare system using Q-learning algorithm

“…In medical decision-making modeling, it is an important problem to evaluate patients' preferences for various health states [ 28 ]. Liu et al examined the preferences and choices of patients in appointments of medical service to improve the patient experience by balancing speed and quality of service [ 29 ]. The capacity management problem faced by clinics was to decide which reservation requests to accept to maximize revenue.…”

OM Forum—People-Centric Operations: Achievements and Future Research Directions

“…In addition, how many OM researchers have immersed themselves in HR? This area of the firm is not only the repository of much of the data that many researchers might seek to study but it is also where critical decisions are made about the (Liu, Xie et al 2019) hiring, allocation, and development of people. Critical questions of HR, such as attrition (Emadi and Staats 2020), lend themselves to careful descriptive and prescriptive study.…”

Theoretical and Practical Aspects of Outpatient Clinic Optimization

“…Patient no-shows influence clinic occupancy and financial results [13], and therefore it is important to be able to predict [4,21] and influence [23] this behavior.…”