AI-driven techniques for enhanced efficiency in psychiatry clinical scheduling
Authors:   , Kasaie Sharifi Seyed Alireza 
Published: March 2024

[EMBARGOED UNTIL 12/1/2024] In recent times, psychiatry clinics are constantly facing late patient arrivals. Patient unpunctuality significantly affects the use of resources and patient waiting times, among other quality indicators. Consequences of tolerating early and late arrivals include disruption of the timetable and lengthier wait times for incoming patients, which may lead to patient dissatisfaction and physician fatigue. Considering these problems, psychiatric clinics strive to improve their services. Therefore, this research suggests three phases for enhancing psychiatric services using predictive and prescriptive analytics. In the first phase, four machine learning models, including multinomial logistic regression, decision tree, random forest, and artificial neural network, are developed to predict patient arrival patterns and aid efficient scheduling accurately. These models are analyzed using the explainable synthetic intelligence approach and the Shapley additive explanations model, promoting comprehension and trust in our algorithmic results. The results show that the random forest algorithm can predict the patient's unpunctuality with an accuracy of over 85 percent. Also, the travel distance, appointment lead time, patient's age, Body Mass Index (BMI), and specific mental diagnoses are significant factors affecting the patient's unpunctuality. In the second phase, new appointment systems are developed to effectively distribute patient needs during the clinic session to enhance resource utilization and patient satisfaction. The proposed appointment systems can address the patient's unpunctuality, preference, and availability. To do this, 21 different scheduling rules (3 benchmark rules + 18 proposed rules) have been assessed regarding their average total cost under different scenarios. The HSBGDM-1 rule emerged as balanced for clinic operations, proficiently managing physician time but occasionally causing overtime variations. Increased patient delays often heightened physician idle times, especially under IBVST and VBFST rules. Hybrid rules, like the HSBGDM series, adapted well, enhancing patient wait times and managing unscheduled patients. Conversely, REPDM scheduling might extend waits, affecting patient satisfaction. Systems focusing on new appointments can increase physician idle times due to unpredictability. While accommodating unscheduled patients boosts service quality, it may also cause disruptions. In the final phase, a genetic algorithm is utilized to determine the optimal patient-to- physician ratio for minimizing the average total cost in a psychiatric clinic across varied scenarios. The sensitivity analysis revealed a profound understanding of how different patient-to-physician ratios affect outcomes across diverse situations, particularly regarding unpunctuality rates. Notably, an 80:2 ratio demonstrated consistent effectiveness in most scenarios, suggesting its suitability as a benchmark for healthcare scheduling. However, specific unpunctuality rates influenced optimal ratios differently, highlighting a potential non-linear relationship between unpunctuality and ideal proportions.