| BackgroundCurrently, nurse shortage exists in many countries, and it will be more acute in the coming years. A number of studies showed that there was a correlation between over-workload and nurse health, satisfaction, burnout, and patient outcomes. Chinese Ministry of Health (National Health and Family Planning Commission of People’s Republic of China) enacted "Chinese Nursing Career Development Plan (2011-2015)", required further implementation of hospital nurse human resources allocation standards, made the nursing staff adapt to nursing workload. It was scientifically and reasonably to guide nurse human resources allocation with nursing workload, and got nurse managers attention at home and abroad. Current studies focused more on specialist care areas, such as ICU, orthopedics ward, general surgery ward, pediatrics ward, while less on neurological wardObjectiveThis study aimed to measure the nursing workload in neurological ward. Using time series analysis to dissect the trends of nursing workload, explore the nursing human resources allocation method that met the active nursing model, improve the efficiency use of human resources, expand nursing workforce management mentality and achieve scientific management.MethodsThrough literature searches and observation, we tentatively set the nursing workload measurement indicators, while developed the questionnaire and established the neurological nursing workload measurement indicator through expert meeting. To automatically generate the nursing workload, we improved nursing workload measurement system, and then extracted the daily nursing workload. SPSS19.0 was applied to analysis the data,Time series analysis was used to construct the time series model of daily nursing workload.Results1. We developed neurological nursing workload measurement indicators with 3 levels, the first level included 3 items, the second level included 31 items and the third level included 64 items. Based on patient-centered value, we systematically and normatively described the meaning of indicators.2. The trends of ward A’s workload was upward firstly and downward then, the descending order was:the second quarter, the third quarter, the fourth quarter, the first quarter. There were no differences between them, p>0.01. The trends of ward B’s workload was downward firstly, upward then and downward ultimately, the descending order was:the third quarter, the fourth quarter,the first quarter,the second quarter. There was significantly difference between the second and the third quarter,p<0.01.3. The fit time series model of nursing workload for ward A and ward B were Exponential Smoothing Model, the forecasted nursing workload of ward A were 317.39,316.14,295.94 (Upper Confidence Limits:366.39、 375.95,364.88; Lower Confidence Limits:268.40,256.33,227.00) for the next three days. The forecasted nursing workload of ward B were 450.03,449.38,445.58(Upper Confidence Limits:503.76,512.04,516.06; Lower Confidence Limits:396.30,386.71,375.11) for the next three days.Conclusions1. Nursing workload indicators were the basis for nursing workload studies, establishing specialized nursing workload measurement indicators was the premise of neurological nurse workload measurement, all these can provide references for nursing human resources and performance management.2. Neurological nursing workload in the winter and spring of high cerebro-vascular disease incidence were lower than the summer and fall, while there were no statistical differences between them. The trends of nursing workload can also provide guidance for staffing management.3. Time series analysis can predict the nursing workload based on the short-term forecasting of time series model. It can also evaluate the reasonableness of the existing staffing strategy. |
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