Exploration of Bayesian Statistic Model for Nurse Call Change after Surgery among Adjustment Methods

Abstract

Analysis of nurse call is important to evaluate nursing management. However, the nurse call data include wide variabilities of individual patients, which sometime blind true differences between two groups, and massive zero calls, which distort true distribution of nurse calls. These problems may decrease analysis performance. Moreover, in medical statistics, to detect true difference related to interventions, the outcome values are often adjusted by fundamental parameters of the body characteristics such as age, sex and body weight. Thus, the adjustment mechanism is also important for nurse call analysis. We proposed a new statistic model to analyze nurse call data based on Bayesian statistics. The model includes hierarchical Bayes model, zero inflated Poisson (ZIP) distribution and linear combination of adjustment parameters. The nurse call dataset in an orthopedics ward was prepared, and the nurse calls of the patient who had undergone orthopedics surgery and the other surgery were compared. Introduction of hierarchical Bayes and ZIP improved fitness of the model to the dataset. However, the adjustment with age and sex was not so effective for model improvement. Therefore, our model including hierarchical Bayes and ZIP may be effective for nurse call analysis.