Finding evidence-based relationships among variables is an important tool for any healthcare administration leader. On a daily basis, healthcare administration leaders may want to see what variables are correlated so that they can implement quality improvement.
This week, you think of scenarios where building and interpreting regression models would be useful for healthcare administration leaders. You might consider building off of your Week 4 Discussion.
For example, Jenna, a healthcare administration leader, determined last week that patient satisfaction scores had fallen from the mean of 87. She wants to know why. She believes that it may have something to do with patient waiting time and time spent with the doctor. Thus, her dependent variable (y) is patient satisfaction and the independent variables are waiting time (x1) and time spent with the doctor (x2). She can evaluate the relationship between these two variables using correlation; bivariate scatterplots for y vs. x1 and y vs. x2; and regression techniques.
For this Discussion, think about a healthcare scenario where multiple regression might be useful in your organization or one with which you are familiar. Consider what your dependent and independent variables might be for conducting a multiple regression analysis. Build a small example, and run the regression analysis.
Post a description of the dependent and independent variables you will use for your multiple regression analysis, and then explain your regression model in terms of your dependent and independent variables. Explain how you might measure your variables. Be specific and provide examples.
Expert Solution Preview
Introduction:
In healthcare administration, finding evidence-based relationships among variables is crucial for implementing quality improvement. Building and interpreting regression models can be a useful tool for healthcare administration leaders. This discussion will focus on a healthcare scenario where multiple regression might be useful, and the dependent and independent variables required for conducting a multiple regression analysis.
Answer:
The healthcare scenario where multiple regression might be useful is predicting readmission rates of diabetic patients. The dependent variable will be the readmission rate, and the independent variables will include patient demographics such as age, sex, race, ethnicity, insurance, and length of stay. Additional independent variables could include patient comorbidities like hypertension, hyperlipidemia, cardiovascular disease, and other chronic illnesses.
To measure the dependent variable, the readmission rate, we can conduct a retrospective study by analyzing discharge data and linking it with readmission data. We can calculate the readmission rate by dividing the number of readmissions by the total number of discharges for diabetic patients. To measure the independent variables, we can collect demographic data and comorbidities through electronic health records and patient surveys.
The regression model for predicting readmission rates will use the dependent variable (readmission rate) and multiple independent variables (patient demographics and comorbidities). The regression equation will help predict the relationship between the independent variables and the dependent variable. It will be useful in identifying factors that impact the readmission rate of diabetic patients. The regression model can also help develop targeted interventions to improve patient outcomes and reduce readmission rates.
