The Association Between Patient Satisfaction and Inpatient Admissions Among Teaching and Nonteaching Hospitals
The author of the paper "The Association Between Patient Satisfaction and Inpatient Admissions Among Teaching and Nonteaching Hospitals" employs Spearman's Rank Correlation to assess and analyze the outcomes of inpatient admissions. The method employed is quite effective in determining the potential relationship between the variables under consideration. Spearman's Rank Correlation is a type of nonparametric technique used to measure the rank correlation between two variables that can only be described by using a monotonic function. Typically, the Pearson's rank correction is proportional to the Spearman's correlation (Iman & Conover, 1982). In the article, the Spearman's Correlation values exist between +1 and -1. Values close to +1 indicate a strong correlation between the variables while values close to -1 indicate a weak correlation. A correlation of 0 means there does not exist any relationship between the variables. Therefore, when the technique was used in this research, the following outcome was obtained.
The Variables Used for the Study and the Research Question
The variables used for the study included admission in the combined hospitals and patient satisfaction. The individual patient satisfaction rates were first compared with the admission to combined teaching and non-teaching hospitals (Messina et al., 2009). In this regard, the research was examining the three variable to understand the relationships between them. Eventually, it was found that the variables used answered the research question (Messina et al., 2009). They gave the exact kind of relationship that is existing between the level of patient satisfaction and their rate of admission in teaching hospitals. Thus, the outcome has shown that it gave the full answer to the question by stating whether the relationship was weak, strong or did not exist.
Negative Spearman Rank Correlation
In the first assessment, it was realized that there was a negative Spearman Rank correlation between patient satisfaction and the combination of hospitals to which they were admitted. In this case, the result presented a negative correlation that clarified that high patient satisfaction in hospitals only correlates well with low volumes of patients. This result means that doctors attend to patients well when they are few in the non-teaching hospitals (Messina et al., 2009). However, when they are congested, they do not receive quality service because the facility does not have adequate student doctors.
Positive Correlation in Teaching Hospitals
The Spearman's rank correlation between patient satisfaction and the admission volume in a teaching hospital showed a strong positive correlation. In this regard, the value of the correlation was r = 0.581. This value indicates that there is a strong positive correlation between the services offered at the teaching hospital (Messina et al., 2009). An increase in the number of patients does not negatively affect the services offered to the patients (Iman & Conover, 1982). On the other hand, the level of satisfaction and patient's admission to the non-teaching hospitals showed a - 0.097 Spearman's rank correlation coefficient (Messina et al., 2009). The coefficient in this analysis is - 0.097, meaning that the two have a negative relationship. Indeed, the methods used to test the relationship between the variables are realistic because they have given an accurate view of the ideal situation on the ground. Therefore, Spearman's rank correlation is appropriately used.
Conclusion
In conclusion, Spearman's rank correlation is used to give an accurate form of the relationship that exists between the variables. The variables were chosen based on the research question. When the variables were finally evaluated, the generated coefficients that were used to examine if the relationship existing between them were positive or negative. Moreover, the Spearman's Rank Correlation was appropriately used because it contains the exact values of the coefficients that were tabulated.
References
Iman, R. L., & Conover, W. J. (1982). A distribution-free approach to inducing rank correlation among input variables. Communications in Statistics-Simulation and Computation, 11(3), 311-334.
Messina, D. J., Scotti, D. J., Driscoll, A. E., Ganey, R., & Zipp, G. P. (2009). The relationship between patient satisfaction and inpatient admissions across teaching and nonteaching hospitals. Journal of healthcare management, 54(3), 177-189.
