Prediction of Obstetric Patient Flow and Horizontal Allocation of Medical Resources Based on Time Series Analysis - Li, Hua; Mu, Dongmei; Wang, Ping; Li, Yin; Wang, Dongxuan (Frontiers in Public Health, 2021)
Objective: in view of the ever-changing flow of obstetric patients in the hospital, how the government and hospital management plan and allocate medical resources has become an important problem to solve urgently at present. Methods: the historical patient flow data from January 1, 2018 to February 29, 2020 in the Department of obstetrics of the first hospital of Jilin University in China was used as the training set. In order to analyze and predict the monthly patient flow, the data were classified and summarized to obtain the monthly patient flow. The TS function in R software is used to generate the time series of monthly patient flow; the decomposition function is used to decompose the time series data to obtain the random, trend and seasonal variation rules of the data; the HoltWinter model in the forecast package is used to predict the time series; ACF, PACF, Ljung-Box and residual histogram are used to verify the accuracy of the prediction model, and the results show that the prediction model has reached the optimal level. Results: the analysis and prediction results obtained from the prediction model showed that the obstetric inpatient flow was not a pure random process, and the patient flow was not only accompanied by the random patient flow, but also showed a trend change and seasonal change rule. The trend change law is increasing year by year. The seasonal variation law is that the patient flow presents the trough in the first and fourth quarters of each year, and reaches the peak in the second and third quarters. The distribution of random flow is approximately normal. Conclusion: in this paper, the time series analysis model is used to analyze and predict the obstetric patient flow, and the trend and seasonal variation of the obstetric patient flow are obtained, and the patient flow in each month in the next year is predicted. On this basis, combined with the trend and seasonal changes of obstetric patient flow, a more reasonable and fair horizontal allocation scheme of medical resources is proposed combined with the prediction of patient flow.