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/

Phillip April 1, 2024 No Comments

PSYC FPX 4600 Assessment 3 Data Analysis and Interpretation

PSYC FPX 4600 Assessment 3 Data Analysis and Interpretation Name Capella University PSYC FPX 4600 Research Methods in Psychology Prof. Name Date Data Analysis and Interpretation In the data analysis and interpretation section, statistical or numerical methods are employed to test the data. For this research, the ANOVA single-factor test will be utilized to analyze and interpret the data for hypothesis results. The analysis will adhere to statistical APA style, which is widely used for reporting research findings (Kyonka et al., 2019). Interpretation of Statistical Findings According to the statistical findings derived from the ANOVA results, ethnicity among communities or students does not significantly impact grades and educational performance (Hoijtink et al., 2019). Neither students nor professors perceive any differences among students based on cultural background, gender, color, or race. This study paves the way for future researchers to explore other discriminatory factors and assess their effects on students’ academic records. The provided table illustrates the statistical findings concerning independent and dependent variables. ANOVA: SOURCE OF VARIATION SS df MS F P-Value F-crit BETWEEN GROUPS 132.2473 16 8.265458 7.250311 3.15E-15 1.664263 WITHIN GROUPS 556.3269 488 1.140014 TOTAL 688.5743 504 PSYC FPX 4600 Assessment 3 Data Analysis and Interpretation The one-way ANOVA indicates that the impact of ethnicity on students’ grades is negligible, with p = 3.15. A statistical significance of 688.5 is found, refuting the hypothesis. The ANOVA single-factor test is applied to obtain mean values of the data and to calculate variance, determining the similarity between dependent and independent variables. ANOVA yields p-values greater than 0.05, indicating dominance of the null hypothesis. The obtained p-value of 3.15 is significantly higher than 0.05, suggesting that ethnicity has no significant impact on grades and academic performance. Most respondents consider ethnicity a secondary factor affecting students’ grades and educational performance. Additionally, the high value for degrees of freedom (df) of the Single ANOVA test refutes the hypothesis. Results from ANOVA suggest that factors such as personal abilities, family values, and financial status may significantly influence students’ grades and academic performance, highlighting the educational sector’s progress in overcoming racial discrimination. Demographic Statistics For statistical analysis, it is necessary to consider demographic factors such as race, age, and gender (Petritis & PhD, 2018). Demographic results may vary due to socioeconomic factors such as education and social status (Mishra et al., 2019). In the statistical analysis using ANOVA single factor, demographic questions are included to ascertain respondents’ qualifications, gender, work experience, and age. Respondents aged 15-55 are included in the study with a 10-year scale. Thirty responses are collected to obtain real-time data for the hypothesis. Google Forms is utilized to collect data, and the responses are recorded in Excel. Consequently, the results confirm that ethnicity among communities or students has no significant impact on grades and educational performance (Hoijtink et al., 2019). References Hoijtink, H., Mulder, J., van Lissa, C., & Gu, X. (2019). A tutorial on testing hypotheses using the Bayes factor. Psychological Methods, 24(5), 539–556. https://doi.org/10.1037/met0000201 Kyonka, E. G. E., Mitchell, S. H., & Bizo, L. A. (2019). Beyond inference by eye: Statistical and graphing practices in JEAB, 1992-2017. Journal of the Experimental Analysis of Behavior, 111(2), 155–165. https://doi.org/10.1002/jeab.509 PSYC FPX 4600 Assessment 3 Data Analysis and Interpretation Mishra, P., Singh, U., Pandey, C., Mishra, P., & Pandey, G. (2019). Application of student’s t-test, analysis of variance, and covariance. Annals of Cardiac Anaesthesia, 22(4), 407. https://doi.org/10.4103/aca.aca_94_19 Petritis, B., & PhD. (2018, November 20). t-test & ANOVA (Analysis of Variance). RayBiotech.com. https://www.raybiotech.com/learning-center/t-test-anova/

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