A Decision-Making tool based on historical data for service time prediction in outpatient scheduling

Background: Appointment scheduling in outpatient settings typically uses simple classification rules to assign patients to long or short appointment slots, based on the anticipated duration of the patient-physician consultation, i.e., the service time. For example, new patients are assigned longer appointment slots, and return patients are assigned shorter slots. While these rules are convenient, they fail to account for the significant variability in service time of outpatient visits. Methods: We present a data-mining approach that allows practices to predict service time based on patient characteristics and several other clinical attributes. This approach provides a decision-support tool that helps practices determine the length of time to allocate to a patient's appointment. Specifically, we use a neural network to accurately estimate service time for each patient based on his/her characteristics. The neural network is trained using eight years of real appointment data (2010 to 2018) from a local outpatient clinic. We compare the performance of the neural network predictions against commonly used classification rules, using a randomly sampled test dataset and a statistical test. Results: Our results suggest that outpatient practices can refine their current practices by adopting a data-driven approach to determining slot lengths for appointments. The average absolute difference and the standard deviation of differences between the neural network predictions and the actual service times in practice (case study) are 5.7 min and 4.0 min, respectively. These two measures are significantly lower than the same comparison with the common classification rule (new patient versus return patient) at the clinic; i.e. average time and standard deviations are 14.3 min and 8.2 min, respectively. Conclusion: Neural network modeling can capture the effect of processes in a medical facility and create individualized predictions of patient service time with more accuracy.