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The accurate forecast of radiology emergency patient flow is of great importance to optimize appointment scheduling decisions. This study used a multi-model approach to forecast daily radiology emergency patient flow with consideration of different patient sources. We constructed six linear and nonlinear models by considering the lag effects and corresponding time factors. The autoregressive integrated moving average and least absolute shrinkage and selection operator (Lasso) were selected from the category of linear models, whereas linear-and-radial support vector regression models, random forests and adaptive boosting were chosen from the category of nonlinear models. The models were applied to 4-year daily emergency visits data in the radiology department of West China Hospital in Chengdu, China. The mean absolute percentage error of six models ranged from 8.56 to 9.36 percent for emergency department patients, whereas it varied from 10.90 to 14.39 percent for ward patients. The best-performing model for total radiology visits was Lasso, which yielded a mean absolute percentage error of 7.06 percent. The arrival patterns of emergency department and total radiology emergency patient flows could be modeled by linear processes. By contrast, the nonlinear model performed best for ward patient flow. These findings will benefit hospital managers in managing efficient patient flow, thus improving service quality and increasing patient satisfaction.

Nuclear medicine, a subspecialty of radiology, plays an important role in proper diagnosis and timely treatment. Multiple resources, especially short-lived radiopharmaceuticals involved in the process of nuclear medical examination, constitute a unique problem in appointment scheduling. Aiming at achieving scientific and reasonable appointment scheduling in the West China Hospital (WCH), a typical class A tertiary hospital in China, we developed an online appointment scheduling algorithm based on an offline nonlinear integer programming model which considers multiresources allocation, the time window constraints imposed by short-lived radiopharmaceuticals, and the stochastic nature of the patient requests when scheduling patients. A series of experiments are conducted to show the effectiveness of the proposed strategy based on data provided by the WCH. The results show that the examination amount increases by 29.76% compared with the current one with a significant increase in the resource utilization and timely rate. Besides, it also has a high stability for stochastic factors and bears the advantage of convenient and economic operation.