Luo Li, Luo Le, Zhang Xinli, He Xiaoli
Business School, Sichuan University, No. 29, Wangjiang Road, Chengdu, Sichuan, 610064, China.
Outpatient Department, West China Hospital, Sichuan University, Chengdu, Sichuan, 610064, China.
BMC Health Serv Res. 2017 Jul 10;17(1):469. doi: 10.1186/s12913-017-2407-9.
Accurate forecasting of hospital outpatient visits is beneficial for the reasonable planning and allocation of healthcare resource to meet the medical demands. In terms of the multiple attributes of daily outpatient visits, such as randomness, cyclicity and trend, time series methods, ARIMA, can be a good choice for outpatient visits forecasting. On the other hand, the hospital outpatient visits are also affected by the doctors' scheduling and the effects are not pure random. Thinking about the impure specialty, this paper presents a new forecasting model that takes cyclicity and the day of the week effect into consideration.
We formulate a seasonal ARIMA (SARIMA) model on a daily time series and then a single exponential smoothing (SES) model on the day of the week time series, and finally establish a combinatorial model by modifying them. The models are applied to 1 year of daily visits data of urban outpatients in two internal medicine departments of a large hospital in Chengdu, for forecasting the daily outpatient visits about 1 week ahead.
The proposed model is applied to forecast the cross-sectional data for 7 consecutive days of daily outpatient visits over an 8-weeks period based on 43 weeks of observation data during 1 year. The results show that the two single traditional models and the combinatorial model are simplicity of implementation and low computational intensiveness, whilst being appropriate for short-term forecast horizons. Furthermore, the combinatorial model can capture the comprehensive features of the time series data better.
Combinatorial model can achieve better prediction performance than the single model, with lower residuals variance and small mean of residual errors which needs to be optimized deeply on the next research step.
准确预测医院门诊量有利于合理规划和分配医疗资源以满足医疗需求。就每日门诊量的多重属性而言，如随机性、周期性和趋势性，时间序列方法（ARIMA）是门诊量预测的一个不错选择。另一方面，医院门诊量也受医生排班影响，且这种影响并非纯粹随机。考虑到这种不纯粹的特性，本文提出一种新的预测模型，该模型考虑了周期性和星期效应。
我们先在每日时间序列上构建季节性自回归积分滑动平均（SARIMA）模型，然后在星期时间序列上构建单指数平滑（SES）模型，最后通过对它们进行修正建立组合模型。将这些模型应用于成都某大型医院两个内科的1年城市门诊每日就诊数据，以提前约1周预测每日门诊量。
基于1年中43周的观测数据，将所提出的模型应用于预测8周内连续7天的每日门诊量横断面数据。结果表明，两种单一传统模型和组合模型实施简单、计算强度低，适用于短期预测范围。此外，组合模型能更好地捕捉时间序列数据的综合特征。
组合模型比单一模型能实现更好的预测性能，具有更低的残差方差和更小的平均残差误差，在下一步研究中需要深入优化。
