Prediction of Daily Blood Sampling Room Visits Based on ARIMA and SES Model.

Xinli Zhang,Fei Xiong,Le Luo,Yu Yu

doi:10.1155/2020/1720134

Abstract

This paper is aimed at establishing a combined prediction model to predict the demand for medical care in terms of daily visits in an outpatient blood sampling room, which provides a basis for rational arrangement of human resources and planning. On the basis of analyzing the comprehensive characteristics of the randomness, periodicity, trend, and day-of-the-week effects of the daily number of blood collections in the hospital, we firstly established an autoregressive integrated moving average model (ARIMA) model to capture the periodicity, volatility, and trend, and secondly, we constructed a simple exponential smoothing (SES) model considering the day-of-the-week effect. Finally, a combined prediction model of the residual correction is established based on the prediction results of the two models. The models are applied to data from 60 weeks of daily visits in the outpatient blood sampling room of a large hospital in Chengdu, for forecasting the daily number of blood collections about 1 week ahead. The result shows that the MAPE of the combined model is the smallest overall, of which the improvement during the weekend is obvious, indicating that the prediction error of extreme value is significantly reduced. The ARIMA model can extract the seasonal and nonseasonal components of the time series, and the SES model can capture the overall trend and the influence of regular changes in the time series, while the combined prediction model, taking into account the comprehensive characteristics of the time series data, has better fitting prediction accuracy than a single model. The new model can well realize the short-to-medium-term prediction of the daily number of blood collections one week in advance.

Highlights

In the Chinese medical context, the outpatient blood sampling room is the key node for clinical cases from diagnosis to the step of treatment [1]
In Lu et al.’s study, five hybrid algorithm combing models were assessed and the results showed that the GM-artificial neural networks (ANNs) model provided the most precise prediction in forecasting the incidence of occupational diseases in China [28]
The results show that the method can improve the prediction accuracy of the number of blood collections, and the model has the ability to predict the daily blood collection demand, which is helpful for decision makers to more effectively allocate outpatient blood collection resources and save costs

Summary

Introduction

In the Chinese medical context, the outpatient blood sampling room is the key node for clinical cases from diagnosis to the step of treatment [1]. With an increasing demand on the quality of medical services, improving the ability to predict the demand for outpatient blood collection medical services and forecasting the level of demand (patient flow) in advance (days, weeks) have a practical significance, especially for the allocation of medical resources and the satisfaction of high-quality needs. As a hospital management decision support system, the outpatient service demand prediction system improves the overall efficiency of the outpatient department, thereby improving work efficiency and patient satisfaction [3]. Existing research has focused on outpatient flow [4, 5] and other related departments like internal medicine [6, 7], cancer [8], anxiety disorder [3], diarrhea [9], and so on; the prediction of outpatient blood sampling room visits was ignored.

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Methods

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Discussion

Conclusion