MHA FPX 5017 Assessment 3 Predicting an Outcome Using Regression Models
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MHA FPX 5017 Assessment 3 Predicting an Outcome Using Regression Models

MHA FPX 5017 Assessment 3 Predicting an Outcome Using Regression Models

Name

Capella university

MHA-FPX 5017 Data Analysis for Health Care Decisions

Prof. Name

Date

Regression Models in Modern Decision Making

The significance of statistics in contemporary decision-making processes empowers managers with greater confidence in navigating uncertainties amidst the abundance of available data. This confidence enables managers to make informed decisions and provide stable leadership to their staff, thus enhancing organizational effectiveness. Various regression models have garnered attention from modern scholars due to their ability to synthesize information, formulate meaningful variables, construct actual models, and analyze the appropriateness of these models in accommodating collected data (Casson & Farmer, 2014). This analysis aims to predict the required reimbursement amount for the subsequent year based on a dataset comprising hospital costs, patient ages, risk factors, and satisfaction scores from the previous year.

Significance Testing and Effect Size of Regression Coefficients

Statistical methodologies play a crucial role in organizational decision-making processes. Employing diverse regression analysis methods to establish an equation that effectively captures the statistical correlation between a response variable and one or more predictor variables is imperative (SCSUEcon, 2011). The p-value assumes significance in determining the effect size of the coefficient in a regression equation, as it allows for the testing of the null hypothesis. A low p-value (<0.05) signifies the rejection of the null hypothesis, indicating a significant advancement in several regression models and changes observed in the response variable concerning variations in predictor values (Sullivan & Feinn, 2012).

MHA FPX 5017 Assessment 3 Predicting an Outcome Using Regression Models

Regression Modeling for Predictive Analysis

In predicting the reimbursement amount, a regression model incorporating age, risk, and satisfaction datasets reveals an explanatory variance of 11% (Gaalan et al., 2019). It’s important to note that not all independent variables contribute equally to this variance; rather, each variable’s percentile contribution must be considered to understand the model’s fitness accurately. The multiple regression model demonstrates statistical significance, with F(3,181) = 7.69, P < .001, and R2 = .11.

Statistical Results and Decision Making

Utilizing data from the provided dataset, multiple regression equations can support healthcare decisions regarding predicted reimbursement costs for individual patients. The reimbursement cost for each patient can be calculated using the equation: y = 6652.176 + 107.036(age) + 153.557(risk) – 9.195*(satisfaction). Examples of predicted reimbursement costs for specific patients from rows 13, 20, and 44 are presented below.

Conclusion

To optimize healthcare reimbursement costs, it may be prudent to exclude the satisfaction variable from predictive models, as it appears incongruent with other predictor variables. However, employing various regression models remains essential for making informed decisions and aligning with long-term organizational goals. Despite potential regulatory adjustments, healthcare organizations can leverage regression analysis to navigate uncertainties and plan for future reimbursement costs effectively.

Reference

Casson, R. J., & Farmer, L. D. M. (2014). Understanding and checking the assumptions of linear regression: A primer for medical researchers. Clinical & Experimental Ophthalmology, 42(6), 590–596.

Gaalan, K., Kunaviktikul, W., Akkadechanunt, T., Wichaikhum, O. A., & Turale, S. (2019). Factors predicting quality of nursing care among nurses in tertiary care hospitals in Mongolia. International Nursing Review, 72(5), 53-68.

IntroToIS BYU. (2016). Creating a multiple linear regression predictive model in Excel [Video] | Transcript. Retrieved from YouTube.com.

MHA FPX 5017 Assessment 3 Predicting an Outcome Using Regression Models

Schneider, A., Hommel, G., & Blettner, M. (2010). Linear regression analysis: part 14 of a series on evaluation of scientific publications. Deutsches Arzteblatt international, 107(44), 776–782.

SCSUEcon. (2011). Linear regression in Excel [Video] | Transcript. Retrieved from YouTube.com.

Sullivan, G. M., & Feinn, R. (2012). Using effect size-or why the P-value is not enough. Journal of graduate medical education, 4(3), 279–282.