MHA FPX 5017 Assessment 3 Predicting an Outcome Using Regression Models MHA-FPX 5017 Data Analysis for Health Care Decisions Regression Models in Modern Decision Making

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

Significance Testing and Effect Size of Regression Coefficients

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

Regression Modeling for Predictive Analysis

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

Statistical Results and Decision Making

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

References

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.