A dynamic approach for outpatient scheduling

Abstract

Aims: Patient no-show is a recurrent problem in medical centers and, in conjunction with cancellation of appointments, often results in loss of productivity and excessive patient time to appointment. The purpose of this study was to develop a dynamic procedure for scheduling patients within an outpatient clinic where patients are expected to have multiple appointments, such as physical therapy, occupational therapy, primary care, and dentistry.

Methods: This retrospective study involved the year 2014 de-identified patient records from an outpatient clinic affiliated with a large university hospital. A number of patient characteristics, appointment data, and historical attendance records were examined to determine whether they significantly impacted patients who missed scheduled appointments (no-shows). Patient attendance behaviors over multiple appointments were examined to determine whether their no-show and cancellation patterns differed from one appointment to the next. Decision tree analysis was applied to those predictors that significantly correlated with patient attendance behavior to assess the likelihood of a patient no-show. A sample dynamic appointment scheduling procedure that utilized different overbooking strategies for different appointment numbers was then developed. Computer simulation was used to assess the effectiveness of the dynamic procedure versus two other methods consisting of randomly assigned and uniformly assigned appointments.

Results: The dynamic scheduling procedure resulted in increased scheduling efficiency through overbooking but with less than 5% risk of appointment conflicts (i.e. two patients showing at the same time), equating to approximately 0.16 conflicts per clinician per day. It also increased clinic utilization by about 6.7%. It consistently outperformed the other two methods with respect to the percentage of appointment conflicts.

Limitations: The study is limited with respect to potential clinician cost increase resulting from possible appointment conflicts. A second limitation is that patients experiencing appointment conflicts might not wait for treatment, resulting in potential loss of revenue. A third limitation is that the model does not take into account patient satisfaction, nor the ethics of overbooking patients.

Conclusions: A dynamic appointment scheduling procedure was developed using actual patient characteristics. The procedure resulted in creation of more efficient appointment schedules thereby increasing the clinic utilization.

Keywords: Decision analysis; healthcare; outpatient scheduling; overbooking; predictive modeling.