目的:在移动平均趋势剔除法、最小二乘法两种趋势剔除法中找出一种能较好反映某医院门诊量季节变化规律的方法。方法:根据该医院2005~2008年各月门诊数据,运用移动平均趋势剔除法、最小二乘法分别计算该医院门诊患者的季节指数(seasonal index)和各月预测值,并对预测结果进行平均绝对偏差(MAD)、平均平方误差(MSE)、平均预测误差(AFE)、平均绝对百分误差(MAPE)分析。同时,判断实际值与预测值的容许区间的关系。结果:移动平均趋势剔除法和最小二乘法预测值的MAD、MSE、AFE、MAPE分别为766.94,888236.8542,-0.23,5.478249.8%和739.0196,802281.2,0.125,5.259-453%。移动平均趋势剔除法有4个实际值落在容许区间之外,最小二乘法有2个。结论:最小二乘法能够更好反映出该院门诊量季节变化的规律,是预测的最佳选择方案。
ObjectiveTo analyze epidemic characteristics of multidrug-resistant organism (MDRO) in Neurosurgical Intensive Care Unit (NSICU), and to analyze the status of infection and colonization, in order to provide reference for constituting intervention measures. MethodsPatients who stayed in NSICU during January 2014 to April 2015 were actively monitored for the MDRO situation. ResultsA total of 218 MDRO pathogens were isolated from 159 patients, and 42 cases were healthcare-associated infections (HAI) among 159 patients. The Acinetobacter baumannii was the most common one in the isolated acinetobacter. Colonization rate was positively correlated with the incidence of HAI. From January to December, there was a significantly increase in the colonization rate, but not in the incidence of HAI. ConclusionThe main MDRO situation is colonization in NSICU. The obvious seasonal variation makes the HAI risk at different levels. So it is necessary that full-time and part-time HAI control staff be on alert, issue timely risk warning, and strengthen risk management. The Acinetobacter baumannii has become the number one target for HAI prevention and control in NSICU, so their apparent seasonal distribution is worthy of more attention, and strict implementation of HAI prevention and control measures should be carried out.
ObjectiveTo acquire the flow law of outpatient and emergency visits in a large general hospital. MethodsBy sampling monthly amount of outpatient and emergency from January 2005 to December 2013 of a large general hospital in Guangzhou, the trend of the time series was analyzed and calculated the seasonal index of the amount of hospital outpatient and emergency visits with the use of long-term trends method. ResultThe flow law of patients in the hospital outpatient and emergency was significantly affected by seasonal factors, and different month had its own variation characters. The seasonal indexes were the highest in March, July, August, November and December (seasonal index >105%), while the lowest in January, February, October (seasonal index <95%). ConclusionBased on analysis of the outpatient and emergency visits and causes with hospitals, decision makers and hospitals should make reasonable allocation of medical resources and provide evidence for the scientific decisions of hospital management. Thus, ensure the safety of patients.
Objective To explore the predicted precision of discharged patients number using curve estimation combined with trend-season model. Methods Curve estimation and trend-season model were both applied, and the quarterly number of discharged patients of 363 hospital from 2009 to 2015 was collected and analyzed in order to predict discharged patients in 2016. Relative error between predicted value and actual number was also calculated. Results An optimal quadratic regression equation Yt=3 006.050 1+202.350 8×t–3.544 4×t2 was established (Coefficient of determination R2=0.927, P<0.001), and a total of 23 462 discharged patients were predicted based on this equation combined with trend-season model, with a relative error of 1.79% compared to the actual number. Conclusion The curve estimation combined with trend-season model is a convenient and visual tool for predicting analysis. It has a high predicted accuracy in predicting the number of hospital discharged patients or outpatients, which can provide a reference basis for hospital operation and management.
ObjectiveTo establish a forecasting model for inpatient cases of pediatric limb fractures and predict the trend of its variation.MethodsAccording to inpatient cases of pediatric limb fractures from January 2013 to December 2018, this paper analyzed its characteristics and established the seasonal auto-regressive integrated moving average (SARIMA) model to make a short-term quantitative forecast.ResultsA total of 4 451 patients, involving 2 861 males and 1 590 females were included. The ratio of males to females was 1.8 to 1, and the average age was 5.655. There was a significant difference in age distribution between males and females (χ2=44.363, P<0.001). The inpatient cases of pediatric limb fractures were recorded monthly, with predominant peak annually, from April to June and September to October, respectively. Using the data of the training set from January 2013 to May 2018, a SARIMA model of SARIMA (0,1,1)(0,1,1)12 model (white noise test, P>0.05) was identified to make short-term forecast for the prediction set from June 2018 to November 2018, with RMSE=8.110, MAPE=9.386, and the relative error between the predicted value and the actual value ranged from 1.61% to 8.06%.ConclusionsCompared with the actual cases, the SARIMA model fits well with good short-term prediction accuracy, and it can help provide reliable data support for a scientific forecast for the inpatient cases of pediatric limb fractures.