MHA FPX5107 Assessment 1 Descriptive Statistics and Data Visualization

MHA FPX5107 Assessment 1 Descriptive Statistics and Data Visualization: Analyzing negative aspects within a healthcare system is a crucial process. This assessment involves the evaluation of a healthcare institution’s overall performance through the use of statistical analysis and visual representations (Hariri & Bowers, 2019). The collected data includes information on monthly healthcare service utilization, patient satisfaction, and monthly readmission rates. The assessment employs the Microsoft Office Excel Analysis Tool Pack to conduct the statistical analysis, and the findings are then interpreted. Furthermore, the assessment discusses the implications of these interpretations in the context of existing literature.

MHA FPX5107 Assessment 1 Descriptive Statistics Test

The healthcare data analysis incorporates descriptive statistical tests to assess two key aspects: central tendency and data dispersion. To evaluate central tendency, the mean, median, and mode have been chosen for all three variables (utilization, satisfaction, and readmissions). Conversely, variance and standard deviation have been employed to gauge the extent of data dispersion (Feroze & Kulkarni, 2019).

Table Descriptive Statistics

Histogram for Data Visualization

Creating distinct categories and subdivisions within a sizable dataset is essential for efficient analysis. Histograms serve as valuable tools for visually representing these categories and divisions within the dataset (like MHA FPX5107 Assessment 1 Descriptive Statistics and Data Visualization). In this context, three histograms have been generated to delineate specific categories and enhance our comprehension of the prevailing conditions related to healthcare service utilization, patient satisfaction, and readmission rates. As an example, the histogram illustrating the utilization of healthcare services over the past 70 months is presented below:

MHA FPX5107 Assessment 1 Descriptive Statistics and Data Visualization

The analysis reveals that hospital utilization was notably low for six out of 70 months. On the other hand, there were 29 months with consistently high utilization, where the patient count ranged between 57 and 75. Additionally, there were 21 months marked by exceptionally high activity, with patient counts and healthcare service utilization exceeding 75. The patients treated during these busy months have been assessed for their satisfaction levels as follows:

Descriptive Statistics and Data Visualization

During the months when healthcare utilization was predominantly low, the patient satisfaction rate was notably high. The histogram clearly illustrates that patient satisfaction ratings exceeded 55 in approximately 32 months, while in 38 months, patients reported moderate satisfaction levels, which were less than 55. The correlation between patient satisfaction and readmission rates can be comprehensively examined across all months, as demonstrated in the histogram below:

MHA FPX5107 Assessment 1

At the end readmissions were minimal in approximately 25 months, reaching their peak in about 21 months. Over 24 months, readmissions fell within a moderate range between 0.089 and 0.130. The histogram data highlights the fluctuating levels of readmissions across various months, categorized by different ranges.

Data Interpretation and Summary

MHA FPX5107 Assessment 1 Descriptive Statistics and Data Visualization tests have been conducted on the provided dataset to assess several key parameters, including the mean, mode, median, standard deviation, variance, and data range (Mishra et al., 2019). The analysis reveals that the average service utilization at the healthcare institution over the 70 months is approximately 69.822, signifying a relatively typical level. Notably, the data related to utilization displays a high standard deviation of 21.639, indicating considerable variability. This dispersion of values is notably greater than the mean, highlighting the diversity of utilization data, which lacks a specific pattern. The range of the data is also considerably high at 96.045, further underscoring the substantial dispersion without a discernible trend. This analysis suggests that while patient count remains robust at the hospital, there is a need to implement strategies aimed at enhancing the quality of care, including nurse-to-patient ratio considerations and the optimization of wait times.

MHA FPX5107 Assessment 1 Descriptive Statistics and Data Visualization

Furthermore, the mean patient satisfaction level, as indicated by the descriptive statistics, is approximately 49.357, with a significant standard deviation of 30.912. The data exhibits wide dispersion around the mean, with a lengthy spread. The data range for patient satisfaction is remarkably high at 97, suggesting considerable diversity in patients’ opinions regarding the quality of care provided. While the healthcare institution may maintain consistent care standards, patient satisfaction varies widely, with some reporting high levels of satisfaction and others expressing significantly lower levels. Research indicates that non-care-related factors significantly influence patient satisfaction levels. Therefore, it is advisable for the healthcare organization to focus on improving patient wait times through effective patient management systems to further enhance patient satisfaction (Mishra et al., 2019).

Similarly, the data related to patient readmissions has undergone a descriptive statistical evaluation, revealing a very low readmission rate given the volume of patients who have utilized care services (Jennifer Diamond Acosta & Brooks, 2021). The analysis establishes that the mean readmission rate is 0.106, indicating a minimal number of patients returning for readmission. Moreover, the standard deviation statistics (0.048) demonstrate low data dispersion, implying the effectiveness of the readmission rate. According to research findings, low readmission rates are indicative of a healthcare institution’s delivery of high-quality care. To sustain this level of care, the institution should focus on maintaining effective care procedures, ensuring nurse competence, and optimizing the utilization of associated technologies.

Conclusion

The statistical analysis of the dataset revealed that the hospital’s readmission rates were consistently low, while patient satisfaction levels fell within the moderate range. The analysis involved the application of descriptive statistics to the data, which allowed for the assessment of both central tendency and data dispersion. The findings indicated that there is potential to enhance patient satisfaction with the quality of care provided by implementing technologies such as patient management systems and optimizing wait times.