Poisson Regression models to reduce Waiting Time for Hospital Service. Case Study: Nakhonpathom Hospital Thailand.
The growing population has led to increase in waiting time and overcrowding in the hospital service, a Poisson regression model has been developed to analyze the time series of count data. In finding a Poisson regression model, parameters are estimated and goodness-of-fit is utilized to carefully extract the best model to fit the count data. The marginal effect is the basis function which can be used in the Poisson regression model. This study attempted to analyze actual operations of a hospital and proposed modifications in the system to reduce waiting times for the patients, which should lead to an improved view of the quality of service provided. To develop a Poisson regression analysis model for the above situation, we need to define a model for the expected number of patients for hospital services cases. Here, two underlying variables are of interest, “waiting time” and “hospital services”. Since “waiting time” have been categorized seven groups. The variable “hospital services” which contains four categorizes (No welfare (NW), Reimbursement to employer (RE), Social Security Service (SS) and 30 baht for welfare health service (Gold cards (30W)). As a result, significant levels of causal variables are not expected to be identical for each model. We find that 30 baht for welfare health service (Gold cards (30W)) category has a higher rate of increase in the average waiting time. The marginal effect is a basis function that can be used in the Poisson regression. It allows into arrive at better predictions of hospital service and rehabilitation decision making.
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