Application of Integer Response Optimization Models for the Healthcare Resources

Abstract

This study investigated the relationship between the number of hospitals and healthcare professionals in the Turkish healthcare system. The study data covers the years 1967-2018. In the study, statistical analysis was performed, and the integer response optimization models were created using the linear regression response optimization models. The Pearson correlation test was applied to show the relationship between decision variables. The percentage of variation of the response variable specified by the linear regression model was calculated as 97.85. The numbers of nurses (p value=0.001, f value=21.36, t value=-4.62), midwives (p value=0.001, f value=24.88, t value=4.99), and OHP (Other health professionals) (p value=0.001, f value=24.16, t value=4.92) were found to be effective on the number of hospitals, doctors and pharmacists were not effective on the number of hospitals with a margin of error of 1.00% or 5.00%. The optimum number of healthcare workers required to provide health services in 1798 hospitals was calculated as 153128 doctors, 165244 nurses, 56351 midwives, 32032 pharmacists, and 177409 OHP. As a result of this study, we confirmed that the Turkish government should be able to provide health services in more hospitals with the existing healthcare system resources.

Testing the estimated patient waiting times of the Random Forest algorithm by SPC

“…Patient waiting time is an important quality-oflife indicator for patients [7]. Long waiting times can cause patients to experience stress, anxiety, and discomfort.…”