Building Regression Models in Healthcare

Quality improvement in healthcare is one of the significant objectives of many healthcare facilities and stakeholders. Various factors influence service delivery at healthcare facilities, and therefore statistics become handy in determining possible predictions while using these factors as variables. Regression analysis is an appropriate method of coming up with predictive models that can positively influence operations within a healthcare facility. Linear regression uses explanatory variables to predict the outcome of response variables. Multiple regressions are more advanced than simple regression as it allows for the application of several explanatory variables to come up with a better prediction on the outcome of a response variable. This paper highlights the application of multiple regressions in the healthcare scenario and how the method can help achieve desired success. 

An example of multiple regressions in healthcare is the prediction of death rates based on doctor availability, hospital availability, annual income per capita among patients, and population density people per square mile. We can take the death rate as the dependent variable that should be explained by the four independent variables. These are Doctors' availability, hospital availability, income per capita, and population density. Using multiple regressions, predicting the effect of all the four variables on the death rate is possible. This is vital in helping the public healthcare providers decide on how to minimize death rates among residents for a particular town or city. 

In this regression model, I will check on how the increase or decrease in the number of doctors will increase or decrease the number of deaths. From the prediction, healthcare facilities can decide on how to distribute doctors to contain death rates in the location. The availability of hospitals can also affect the treatment of people with chronic illnesses and save lives. Therefore, my prediction model will look at the p-value of hospital variables' availability and suggest a possible outcome on the death rates. The same model will also check the income of people as well as the population density and suggest the impact of these variables on death rates. The model will also help us understand if there exists a linear relationship among these variables regarding the dependent variable death rates. 

After conducting a linear regression using excel, the table below shows the output. 

From the output, one can check the values and see the significance of the model as well as the data presented. From the above date, the model is essential as the significance F is above 0.05 based on the confidence interval of 95%. The model also shows that doctors and hospital availability affect death rates, but income and population density has less effect on the death rate. Using this model is appropriate for healthcare to make vital decisions.