Phillip May 16, 2024 No Comments

MHA FPX 5017 Assessment 4 Presenting Statistical Results for Decision Making

MHA FPX 5017 Assessment 4 Presenting Statistical Results for Decision Making Name Capella university MHA-FPX 5017 Data Analysis for Health Care Decisions Prof. Name Date Presenting Statistical Results for Decision Making Introduction A coherent and efficient presentation of evidence-based data collection is crucial when communicating with healthcare administrators. Healthcare researchers employ multiple regression analyses to evaluate the strength of the relationship between a dependent variable and several predictor variables. Given the dynamic nature of healthcare, comprehending and presenting data is imperative for identifying trends, whether positive or negative. Regression analysis is an effective statistical method for analyzing medical data, enabling the identification and characterization of relationships among multiple factors. However, if decision-makers fail to grasp the results of data analysis, its utility is compromised. The process of data analysis commences with understanding the problem, goals, and intended actions. Consequently, the analysis yields evidence to either support or refute the hypothesized idea (Davenport, 2014). Regression Method The multiple regression equation is represented as y = a + b1x1 + b2x2 + … + bkxk, where x1, x2, …, xk denote the k independent variables (e.g., age, risk, satisfaction), and y (cost) represents the dependent variable. Multiple regression analysis allows for the explicit control of numerous other factors influencing the dependent variable simultaneously. Through regression analysis, one or more independent variables are compared to a dependent variable, and based on a linear combination of predictors, a predicted value is computed for the criterion. 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 illustrated in Fig. 1, several statistics are employed to evaluate the fit of a regression model, indicating how well it aligns with the data. Multiple R The correlation coefficient, multiple R, measures the strength of the linear relationship between the predictor variable and the response variable. A multiple R of 1 signifies a perfect linear relationship, while a multiple R of 0 suggests no linear relationship whatsoever (Kraus et al., 2021). R Squared The coefficient of determination, also known as r2, signifies the variance explained by a predictor variable, representing the proportion of variance in the response variable. An r2 of 1 indicates that the regression predictions perfectly match the data. The r2 value of 11.3% implies that the response variable can be entirely explained by the predictor variable (Kraus et al., 2021; Shipe et al., 2019). ANOVA In Figure 2, ANOVA, the F statistic p-value, located at the bottom of the table, is crucial for determining the overall significance of the regression model. If the p-value is less than the significance level (usually .05), there is sufficient evidence to conclude that the regression model fits the data better than the model without predictor variables. Thus, the predictor variables enhance the model’s fit (Kraus et al., 2021; Shipe et al., 2019). In Figure 3, coefficient estimates, standard errors, p-values, and confidence intervals for each term in the regression model are presented. Each term receives a coefficient estimate, standard error estimate, t-statistic, p-value, and confidence interval (Shipe et al., 2019). Conclusion According to the multiple regression results, the variables considered account for 11.31% of the variance, indicating that changing costs would cause an 11.31% increase. Healthcare professionals continually seek ways 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. https://web-s-ebscohostcom.library.capella.edu/ehost/pdfviewer/pdfviewer?vid=2&sid=3d6a776e-ccaa-4746-a332-24bafb60e468%40redis 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. https://doi.org/10.1155/2021/5580431 MHA FPX 5017 Assessment 4 Presenting Statistical Results for Decision Making Palmer, P. B., & O’Connell, D. G. (2009). Regression analysis for prediction: Understanding the process. Cardiopulmonary Physical Therapy Journal, 20(3), 23–26. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2845248/ 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. https://doi.org/10.21037/jtd.2019.01.25

Phillip May 16, 2024 No Comments

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.

Phillip May 16, 2024 No Comments

MHA FPX 5017 Assessment 2 Hypothesis Testing for Differences Between Groups

MHA FPX 5017 Assessment 2 Hypothesis Testing for Differences Between Groups Name Capella university MHA-FPX 5017 Data Analysis for Health Care Decisions Prof. Name Date Hypothesis Testing for Differences Between Groups Populations of individuals undergo analysis and testing utilizing hypothesis testing within inferential statistics, aiding in comparing datasets and facilitating conclusive decision-making. Two types of hypotheses, null and alternative, frame research questions, with one positing truth. The null hypothesis proposes no significant difference in data compared side by side, while the alternative hypothesis suggests substantial differences within the dataset (Hacker & Hatemi-J, 2022). The directive entails comparing the productivity levels of clinics one and two using the methods of null and alternative hypotheses. In this context, the null hypothesis (H0) suggests no difference in productivity between the two clinics, while the alternative hypothesis (Ha) supports differences in productivity. Expressed as equations: [H_0: \text{Clinic 1} = \text{Clinic 2}] [H_a: \text{Clinic 1} \neq \text{Clinic 2}] MHA FPX 5017 Assessment 2 Hypothesis Testing for Differences Between Groups 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). 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 – 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. 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. http://library.capella.edu/login?qurl=https%3A%2F %2Fwww.proquest.com%2Fscholarly-journals%2Fevaluating-measuring-exactlydistances-between%2Fdocview%2F2867617355%2Fse-2%3Faccountid%3D27965 MHA FPX 5017 Assessment 2 Hypothesis Testing for Differences Between Groups 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. https://doi.org/10.3390/econometrics5010005 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. https://doi.org/10.1108/JES-09-2020-0469

Phillip May 16, 2024 No Comments

MHA FPX 5017 Assessment 1 Nursing Home Data Analysis

MHA FPX 5017 Assessment 1 Nursing Home Data Analysis Name Capella university MHA-FPX 5017 Data Analysis for Health Care Decisions Prof. Name Date Introduction The administration of a local nursing home is conducting an evaluation of the current department manager and the facility’s performance spanning the last 70 months. The assessment entails a comprehensive review of utilization rates, satisfaction levels, and readmission rates utilizing descriptive statistical tables and histograms. The primary objectives of the nursing administration include achieving higher utilization rates, greater satisfaction among residents, and reducing readmission rates. Additionally, insights gleaned from the data analysis will inform decisions regarding the retention of the current department manager. Data and Statistics To facilitate a thorough performance evaluation, three descriptive statistics tables have been devised, delineating utilization, satisfaction, and readmission rates over the past 70 months. These tables highlight measures of central tendency (mean, median, and mode) as well as dispersion (variance, range, and standard deviation). The utilization of descriptive statistics aims to optimize information dissemination while minimizing data loss (Frey, 2018). In addition to tabular representation, histograms have been constructed to visually depict 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 offer 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 delineate the findings from each descriptive statistical table and histogram pertaining to utilization rates, satisfaction levels, and readmission rates. Utilization Rates Nursing homes in the United States have evolved from predominantly long-stay facilities to establishments catering to a substantial number of short-stay patients (Applebaum, Mehdizadeh, & Berish, 2020). The current aim is to decrease utilization rates, thereby enhancing reimbursement rates. Analysis indicates an average length of stay per month of 68 days. In comparison, the U.S. average length of stay was considerably higher in 2014 and 2015, at 178 and 180 days, respectively (Statista Research Department, 2016). Notably, the range of length of stay spans 96.05 days, signifying significant variability among patients. Over the 70-month period, the majority of patients had a length of stay ranging from 61 to 80 days, with only a limited duration where stays were 40 days or less. Reducing the length of stay holds implications for nursing home practices and quality monitoring (Applebaum et al., 2020). Patient Satisfaction Scores Enhancing the quality of resident care remains a pertinent objective within nursing home administration (Plaku-Alakbarova et al., 2018). Analysis reveals that, on average, 49% of patients expressed satisfaction with their care. However, satisfaction levels were consistently below 40% for 31 months, with only 14 months recording 100% satisfaction. There exists a projected correlation between employee job satisfaction and patient satisfaction, with implications for resident outcomes (Plaku-Alakbarova et al., 2018). Addressing employee satisfaction and re-evaluating policies may yield improvements in patient satisfaction rates. Readmission Rates Mitigating preventable readmissions is crucial 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%. Recommendation The primary objectives of the nursing home administration encompass 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 MHA FPX 5017 Assessment 1 Nursing Home Data Analysis 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/

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