Risk Rate Models and Using Queuing Analysis to Reduce Waiting Time for The Hospital Service. CaseStudy: Samutpakran Hospital

Abstract

The growing population has led to increase in waiting time and overcrowding in the
hospital service, A Risk Rate model has been developed to analyze the time series of count
data. In finding a Risk Rate 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 Risk Rate 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 Risk Rate Models 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” has 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 (30W)). As a result, significant levels of causal variables are not
expected to be identical for each model. We find that30 baht for welfare health service (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 Risk Rate analysis for. It allows into arrive at better
predictions of hospital service and rehabilitation decision making