MHA FPX 5017 Assessment 3 Predicting an Outcome Using Regression Models

Student Name

Capella University

MHA-FPX 5017 Data Analysis for Health Care Decisions

Prof. Name

Date

Regression Models in Contemporary Decision-Making

The role of statistics in modern decision-making processes empowers managers to navigate uncertainties amidst abundant data. Confidence derived from statistical analysis enables informed decisions, enhancing organizational effectiveness. Modern scholars emphasize regression models for synthesizing information, formulating variables, constructing models, and analyzing data appropriateness (Casson & Farmer, 2014). This study aims to predict next year’s reimbursement based on hospital costs, patient ages, risk factors, and satisfaction scores from the previous year.

Significance Testing and Effect Size of Regression Coefficients

Statistical methodologies are integral to organizational decisions. Employing regression analysis methods to establish equations capturing the correlation between variables is imperative (SCSUEcon, 2011). The p-value is crucial in determining coefficient effect size, indicating significance and changes in response variables based on predictor values (Sullivan & Feinn, 2012).

Regression Modeling for Predictive Analysis

A regression model incorporating age, risk, and satisfaction datasets predicts reimbursement with 11% variance (Gaalan et al., 2019). Not all variables contribute equally; understanding each variable’s contribution is essential. The model shows significance, F(3,181) = 7.69, P < .001, and R2 = .11.

Statistical Results and Decision Making

Using dataset regression equations, healthcare decisions regarding predicted reimbursement for individual patients can be supported. Example calculations for specific patients from rows 13, 20, and 44 are provided.

MHA FPX 5017 Assessment 3 Predicting an Outcome Using Regression Models

Conclusion

Optimizing healthcare reimbursement costs may involve excluding the satisfaction variable from predictive models, as it appears incongruent. However, leveraging regression analysis remains crucial for informed decisions aligned with organizational goals. Despite potential regulatory adjustments, regression analysis helps navigate uncertainties and plan reimbursement 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.

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.

MHA FPX 5017 Assessment 3 Predicting an Outcome Using Regression Models

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.