Yi-Fen Liu, Jun-Fang Liao and Jacob Jou
The purpose of this paper is to explore healthcare waiting time and the negative and positive effects (i.e. the dual effects) it has on outpatient satisfaction.
Abstract
Purpose
The purpose of this paper is to explore healthcare waiting time and the negative and positive effects (i.e. the dual effects) it has on outpatient satisfaction.
Design/methodology/approach
Self-administered surveys with 334 outpatients and follow-up interviews with 20 outpatients in three large hospitals in Taiwan were conducted to collect data.
Findings
Quantitative surveys demonstrated that perceived waiting time correlated with satisfaction negatively first but then positively. Satisfaction also correlated with doctor reputation and patient sociability. Follow-up qualitative interviews further revealed that, for some patients, waiting contributed positively to patient evaluations through signaling better healthcare quality and facilitating social interaction.
Originality/value
This research demonstrated the possibility that waiting might have positive effects on healthcare satisfaction. It also identified variables that could produce greater positive perceptions during hospital waiting and underlying mechanisms that could explain how the positive effects work. This research may potentially help hospitals with a better understanding of how they can improve patients’ waiting experiences and increase satisfaction.
Details
Keywords
A. Garg, K. Tai and M.M. Savalani
The empirical modelling of major rapid prototyping (RP) processes such as fused deposition modelling (FDM), selective laser sintering (SLS) and stereolithography (SL) has…
Abstract
Purpose
The empirical modelling of major rapid prototyping (RP) processes such as fused deposition modelling (FDM), selective laser sintering (SLS) and stereolithography (SL) has attracted the attention of researchers in view of their contribution to the overall cost of the product. Empirical modelling techniques such as artificial neural network (ANN) and regression analysis have been paid considerable attention. In this paper, a powerful modelling technique using genetic programming (GP) for modelling the FDM process is introduced and the issues related to the empirical modelling of RP processes are discussed. The present work aims to investigate the performance of various potential empirical modelling techniques so that the choice of an appropriate modelling technique for a given RP process can be made. The paper aims to discuss these issues.
Design/methodology/approach
Apart from the study of applications of empirical modelling techniques on RP processes, a multigene GP is applied to predict the compressive strength of a FDM part based on five given input process parameters. The parameter setting for GP is determined using trial and experimental runs. The performance of the GP model is compared to those of neural networks and regression analysis.
Findings
The GP approach provides a model in the form of a mathematical equation reflecting the relationship between the compressive strength and five given input parameters. The performance of ANN is found to be better than those of GP and regression, showing the effectiveness of ANN in predicting the performance characteristics of the FDM part. The GP is able to identify the significant input parameters that comply with those of an earlier study. The distinct advantages of GP as compared to ANN and regression are highlighted. Several vital issues related to the empirical modelling of RP processes are also highlighted in the end.
Originality/value
For the first time, a review of the application of empirical modelling techniques on RP processes is undertaken and a new GP method for modelling the FDM process is introduced. The performance of potential empirical modelling techniques for modelling RP processes is evaluated. This is an important step in modernising the era of empirical modelling of RP processes.