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MHA-FPX5017

MHA FPX 5017 Assessment 4 Presenting Statistical Results for Decision Making

MHA, MHA-FPX5017 /

MHA FPX 5017 Assessment 4 Presenting Statistical Results for Decision Making MHA FPX 5017 Assessment 4 Presenting Statistical Results for Decision Making Student Name Capella University MHA-FPX 5017 Data Analysis for Health Care Decisions Prof. Name Date Presenting Statistical Results for Decision Making Introduction A well-structured and effective presentation of evidence-based data is crucial for effective communication with healthcare administrators. Within healthcare research, multiple regression analyses play a vital role in assessing the relationship between a dependent variable and several predictor variables. Understanding and presenting data is essential in identifying trends within the dynamic healthcare landscape, whether positive or negative. Regression analysis is a robust statistical method for analyzing medical data, enabling the identification and characterization of relationships among multiple factors. However, the utility of data analysis diminishes if decision-makers struggle to comprehend the results. The data analysis process begins with a clear understanding of the problem, goals, and intended actions, with the analysis providing evidence either to support or refute the hypothesized idea (Davenport, 2014). Regression Method The multiple regression equation, represented as ( y = a + b_1x_1 + b_2x_2 + … + b_kx_k ), where ( x_1, x_2, …, x_k ) represent the independent variables (e.g., age, risk, satisfaction), and ( y ) (cost) represents the dependent variable. Multiple regression analysis allows for the explicit control of numerous factors influencing the dependent variable simultaneously. This method compares one or more independent variables to a dependent variable, computing a predicted value for the criterion based on a linear combination of predictors. Regression analysis serves two primary purposes in science: prediction, including classification, and explanation (Palmer & O’Connell, 2009). MHA FPX 5017 Assessment 4 Presenting Statistical Results for Decision Making Regression Statistics As depicted in Table 1, several statistics are utilized to assess the fit of a regression model, indicating its alignment with the data. Table 1: Regression Statistics Statistic Description Multiple R Measures the strength of the linear relationship between the predictor and response variables. R Squared Signifies the variance explained by the predictor variable, representing the proportion of variance in the response variable. ANOVA Determines the overall significance of the regression model. Multiple R The correlation coefficient, multiple R, quantifies the strength of the linear relationship between the predictor variable and the response variable. A multiple R of 1 indicates a perfect linear relationship, while a multiple R of 0 suggests no linear relationship (Kraus et al., 2021). R Squared The coefficient of determination, or ( r^2 ), indicates the proportion of variance in the response variable explained by the predictor variable. An ( r^2 ) of 1 suggests perfect alignment between regression predictions and data. An ( r^2 ) value of 11.3% implies that the response variable’s variance can be entirely explained by the predictor variable (Kraus et al., 2021; Shipe et al., 2019). ANOVA ANOVA, as shown in Table 2, utilizes the F statistic p-value to determine the overall significance of the regression model. If the p-value is less than the significance level (usually .05), it indicates that the regression model fits the data better than the model without predictor variables, thus enhancing the model’s fit (Kraus et al., 2021; Shipe et al., 2019). Conclusion According to the multiple regression results, the variables considered account for 11.31% of the variance, indicating that altering costs would result in an 11.31% increase. Healthcare professionals continuously seek methods to reduce costs while maintaining high-quality care for their patients. The model’s significant impacts, below 0.05, warrant consideration in decision-making (Shipe et al., 2019). References Davenport, T. H. (2014). A Predictive Analytics Primer. Harvard Business Review Digital Articles, 2–4. [Link] Kraus, D., Oettinger, F., Kiefer, J., Bannasch, H., Stark, G. B., & Simunovic, F. (2021). Efficacy and Cost-Benefit Analysis of Magnetic Resonance Imaging in the Follow-Up of Soft Tissue Sarcomas of the Extremities and Trunk. Journal of Oncology, 2021. [DOI Link] Palmer, P. B., & O’Connell, D. G. (2009). Regression analysis for prediction: Understanding the process. Cardiopulmonary Physical Therapy Journal, 20(3), 23–26. [Link] MHA FPX 5017 Assessment 4 Presenting Statistical Results for Decision Making Shipe, M. E., Deppen, S. A., Farjah, F., & Grogan, E. L. (2019). Developing prediction models for clinical use using logistic regression: An overview. Journal of Thoracic Disease, 11(S4), S579–S584. [DOI Link] Download Free Sample Get Capella University Free MHA Samples MHA FPX 5010 MHA FPX 5020 MHA FPX 5042 MHA FPX 5040 MHA FPX 5016 MHA FPX 5012 MHA FPX 5014 MHA FPX 5017 Get Free Samples of any Class/Assignment

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MHA FPX 5017 Assessment 3 Predicting an Outcome Using Regression Models

MHA, MHA-FPX5017 /

MHA FPX 5017 Assessment 3 Predicting an Outcome Using Regression Models 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. Download Free Sample Get Capella University Free MHA Samples MHA FPX 5010 MHA FPX 5020 MHA FPX 5042 MHA FPX 5040 MHA FPX 5016 MHA FPX 5012 MHA FPX 5014 MHA FPX 5017 Get Free Samples of any Class/Assignment

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MHA FPX 5017 Assessment 2 Hypothesis Testing for Differences Between Groups

MHA, MHA-FPX5017 /

MHA FPX 5017 Assessment 2 Hypothesis Testing for Differences Between Groups MHA FPX 5017 Assessment 2 Hypothesis Testing for Differences Between Groups Student Name Capella University MHA-FPX 5017 Data Analysis for Health Care Decisions Prof. Name Date Hypothesis Testing for Group Differences In inferential statistics, populations of individuals are subject to analysis and testing using hypothesis testing, which aids in comparing datasets and facilitating conclusive decision-making. This process revolves around two types of hypotheses: null and alternative. The null hypothesis posits no significant difference in data compared side by side, while the alternative hypothesis suggests substantial differences within the dataset (Hacker & Hatemi-J, 2022). Research Scenario The comparison of productivity levels between two clinics, Clinic 1 and Clinic 2, is conducted using null and alternative hypotheses. The null hypothesis (H0) proposes no difference in productivity between the two clinics, while the alternative hypothesis (Ha) supports differences in productivity. These hypotheses can be expressed as equations: [H_0: text{Clinic 1} = text{Clinic 2}] [H_a: text{Clinic 1} neq text{Clinic 2}]. Selection of Statistical Tests The determination of a normal distribution between the samples guides the choice of tests. A symmetric distribution ensures symmetrical data presentation, while the current asymmetric appearance signifies unequal variances, favoring the Wilcoxon Signed-Rank test (Chang & Perron, 2017). Two-Sample t-tests Results Both samples possess a sufficient sample size (n = 100) warranting an independent t-test for estimating the normal distribution. Presented below are two independent t-tests, one assuming equal variances and the other assuming unequal variances. Table 1: Two-Sample t-test Assuming Equal Variances   Clinic 1 Clinic 2 Mean 124.32 145.03 Variance 2188.543 1582.514 Observations 100 100 Pooled Variance 1885.529 – Hypothesized Mean Difference 0 – df 198 – t Stat -3.37247 – P(T<=t) one-tail 0.000448 – t Critical one-tail 1.65258 – P(T<=t) two-tail 0.000896 – t Critical two-tail 1.972017 – Table 2: Two-Sample t-test Assuming Unequal Variances   Clinic 1 Clinic 2 Mean 124.32 145.03 Variance 2188.543 1582.514 Observations 100 100 Pooled Variance 1885.529 – Hypothesized Mean Difference 0 – df 193 – t Stat -3.37247 – P(T<=t) one-tail 0.00045 – t Critical one-tail 1.652787 – P(T<=t) two-tail 0.0009 – t Critical two-tail 1.972332 – Interpretation and Recommendation Clinic 2 exhibits a higher mean than Clinic 1 in both scenarios, indicating better performance. With p-values less than the significance level (α = 0.05), the null hypothesis is rejected. Consequently, Clinic 1’s patient visit ratios differ from Clinic 2’s based on the data. MHA FPX 5017 Assessment 2 Hypothesis Testing for Differences Between Groups Recommendation According to the data, Clinic 2 appears to outperform Clinic 1, albeit with a relatively close performance. Remedial actions for underperforming clinics involve analyzing clinical workflows, scheduling and booking software, staff education, billing, and coding practices. A comprehensive analysis identifies deficient areas, enabling administrators to formulate data-driven recommendations for enhancing clinic performance (Aspalter, 2023). References Aspalter, C. (2023). Evaluating and Measuring Exactly the Distances between Aggregate Health Performances: A Global Health Data and Welfare Regime Analysis. Social Development Issues, 45(1), 1-36. Link Chang, S. Y., & Perron, P. (2017). Fractional Unit Root Tests Allowing for a Structural Change in Trend under Both the Null and Alternative Hypotheses. Econometrics, 5(1), 5. DOI MHA FPX 5017 Assessment 2 Hypothesis Testing for Differences Between Groups Hacker, R. S., & Hatemi-J, A. (2022). Model selection in time series analysis: using information criteria as an alternative to hypothesis testing. Journal of Economic Studies, 49(6), 1055-1075. DOI Download Free Sample Get Capella University Free MHA Samples MHA FPX 5010 MHA FPX 5020 MHA FPX 5042 MHA FPX 5040 MHA FPX 5016 MHA FPX 5012 MHA FPX 5014 MHA FPX 5017 Get Free Samples of any Class/Assignment

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MHA FPX 5017 Assessment 1 Nursing Home Data Analysis

MHA, MHA-FPX5017 /

MHA FPX 5017 Assessment 1 Nursing Home Data Analysis MHA FPX 5017 Assessment 1 Nursing Home Data Analysis Student Name Capella University MHA-FPX 5017 Data Analysis for Health Care Decisions Prof. Name Date Introduction The administration of a local nursing home is undergoing an evaluation of the current department manager and the facility’s performance over the past 70 months. The assessment encompasses a comprehensive review of utilization rates, satisfaction levels, and readmission rates using descriptive statistical tables and histograms. The primary goals of the nursing administration are to achieve higher utilization rates, increase resident satisfaction, and decrease readmission rates. Additionally, insights derived from the data analysis will guide decisions regarding the retention of the current department manager. Data and Statistics To facilitate a comprehensive performance evaluation, three descriptive statistics tables have been developed, outlining utilization, satisfaction, and readmission rates spanning the past 70 months. These tables present measures of central tendency (mean, median, and mode) and dispersion (variance, range, and standard deviation). Utilizing descriptive statistics aims to optimize information dissemination while minimizing data loss (Frey, 2018). In addition to tabular representation, histograms have been created to visually represent utilization, satisfaction, and readmission rates within the nursing home. These graphical representations illustrate the frequency distribution of data points on the y-axis against the respective data intervals on the x-axis. The overarching objective of these histograms is to provide insights into the frequency of utilization, the spectrum of patient satisfaction, and the occurrence of patient readmissions throughout the 70-month period. Results The subsequent sections outline the findings from each descriptive statistical table and histogram concerning utilization rates, satisfaction levels, and readmission rates. Utilization Rates Nursing homes in the United States have transitioned from predominantly long-stay facilities to accommodating a significant number of short-stay patients (Applebaum, Mehdizadeh, & Berish, 2020). The current focus is on decreasing utilization rates to enhance reimbursement rates. Analysis reveals an average length of stay per month of 68 days. In comparison, the U.S. average length of stay was notably higher in 2014 and 2015, at 178 and 180 days, respectively (Statista Research Department, 2016). The range of length of stay spans 96.05 days, indicating significant variability among patients. Over the 70-month period, most patients stayed for 61 to 80 days, with only a limited duration where stays were 40 days or less. Reducing the length of stay has implications for nursing home practices and quality monitoring (Applebaum et al., 2020). Patient Satisfaction Scores Improving the quality of resident care remains a crucial objective within nursing home administration (Plaku-Alakbarova et al., 2018). Analysis shows that, on average, 49% of patients expressed satisfaction with their care. However, satisfaction levels remained consistently below 40% for 31 months, with only 14 months recording 100% satisfaction. There is a projected correlation between employee job satisfaction and patient satisfaction, with implications for resident outcomes (Plaku-Alakbarova et al., 2018). Addressing employee satisfaction and reassessing policies may lead to improvements in patient satisfaction rates. Readmission Rates Mitigating preventable readmissions is essential due to associated adverse events and higher healthcare costs (Mendu et al., 2018). Analysis of readmission rates within 30 days of discharge indicates that 11% of patients were readmitted to the nursing home. The range of readmission rates extends from 1% to 21%, with a significant proportion of readmissions occurring over a 25-month period at 15%. MHA FPX 5017 Assessment 1 Nursing Home Data Analysis Recommendation The primary objectives of the nursing home administration include achieving higher utilization rates, enhancing patient satisfaction, and reducing readmission rates. References Applebaum, R., Mehdizadeh, S., & Berish, D. (2020). It Is Not Your Parents’ Long-Term Services System: Nursing Homes in a Changing World. Journal of Applied Gerontology, 39(8), 898–901. https://doi.org/10.1177/0733464818818050 Frey, B. (2018). The SAGE encyclopedia of educational research, measurement, and evaluation (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc. doi: 10.4135/9781506326139 Mendu, M. L., Michaelidis, C. I., Chu, M. C., Sahota, J., Hauser, L., Fay, E., Smith, A., Huether, M. A., Dobija, J., Yurkofsky, M., Pu, C. T., & Britton, K. (2018). Implementation of a skilled nursing facility readmission review process. BMJ open quality, 7(3), e000245. https://doi.org/10.1136/bmjoq-2017-000245 Plaku-Alakbarova, B., Punnett, L., Gore, R. J., & Procare Research Team (2018). Nursing Home Employee and Resident Satisfaction and Resident Care Outcomes. Safety and health at work, 9(4), 408–415. https://doi.org/10.1016/j.shaw.2017.12.002 MHA FPX 5017 Assessment 1 Nursing Home Data Analysis Statista Research Department (2016). Nursing home average length of stay in United States in 2014 and 2015, by ownership. Retrieved from https://www.statista.com/statistics/323219/average-length-of-stay-in-us-nursing-homes-by-ownership/ Download Free Sample Get Capella University Free MHA Samples MHA FPX 5010 MHA FPX 5020 MHA FPX 5042 MHA FPX 5040 MHA FPX 5016 MHA FPX 5012 MHA FPX 5014 MHA FPX 5017 Get Free Samples of any Class/Assignment

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