Abstract:

With the renewed concern about health care quality, there is a need for improved surveillance tools and focus on special age groups. While Structural Equation Model (SEM) is an important tool for surveillance, prediction and measuring intervention impact, the need to focus on reduction in bias necessitates the modification of SEM with Sequential Structural Equation Model (SSEM) to study some specific groups of health care delivery. This study formulated a model on patient satisfaction and compliance to treatment using SSEM of two stopping times with some exogenous, endogenous and mediating variables that generally influence health care delivery. SSEM modelling of Patient Satisfaction (PS) and Compliance to Treatment (CT) involves four latent variables (factors) and some manifest (dependent) variables. Statistical Packages for Social Sciences (SPSS) and Linear Structural Relationship (LISREL) 8.80 were adopted for the analysis. The study established that the fitted indices for the second stopping time meet the threshold rules in all cases when various fitting indices were used, and the fitted model result revealed an insignificant influence of PE on HS [R2 =0.012, F = 3.199, P >.05]. This indicates that PE contributed insignificantly to HS. Therefore, this study concluded that the procedure of sequential stopping time for hypothesised relationship showed that SSEM is useful in the drive towards quality patient health care and satisfaction. Hence, this confirmed that demographic variables are significant to patient experience.