Descriptive statistical tests are used in healthcare data analysis to evaluate two important factors: data dispersion and central tendency. The mean, median, and mode have been selected for each of the three variables (readmissions, satisfaction, and utilisation) in order to assess central tendency. These metrics offer a basis for comprehending broad patterns in the dataset by shedding light on the typical or average value for every variable.
The degree of data dispersion, on the other hand, has been measured using variance and standard deviation, which aid in determining how dispersed the values are around the centre point. These tests determine whether the data points are widely scattered or closely grouped around the central value by assessing the variability. When combined, these statistical methods offer a thorough analysis of the dataset, pointing out trends and abnormalities (Data Visualization, 2024).
MHA FPX 5107 Assessment 1
These metrics provide a foundation for understanding general trends in the dataset by illuminating the average or typical value for each variable. For each important statistic, including patient satisfaction, utilisation, and readmission rates, measures of central tendency, such as the mean, median, and mode, offer important information about the overall trend or norm. Variance and standard deviation, on the other hand, have been used to quantify the degree of data dispersion.
These metrics help determine how widely distributed the values are around the central point. For instance, a low standard deviation would suggest that the values are more closely clustered around the mean, whereas a large standard deviation would signal that the data is significantly variable. These statistical methods work together to provide a more comprehensive insight (Thijmen van Alphen et al., 2022).
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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. 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. These visual representations allow for a clearer understanding of the distribution of data, highlighting the frequency of various values within each category.
MHA FPX 5107 Assessment 1
By examining the shape and spread of the histograms, analysts can identify patterns, detect outliers, and pinpoint areas that may require further investigation or intervention. Such graphical tools simplify the interpretation of complex data, making it more accessible for decision-making and strategic planning in healthcare settings (Lee et al., 2021). For instance, the following histogram shows the use of medical services throughout the previous 70 months:
According to the data, hospital utilisation was noticeably low for six of the 70 months, pointing to underutilisation periods that might be caused by seasonal variations in demand or operational inefficiencies. On the other hand, 29 months with a patient count ranging from 57 to 75 had continuously high utilisation. These months probably show a consistent need for medical services, maybe as a result of recurring medical requirements or particular seasonal patterns. There were also 21 months with unusually high activity, with over 75 patients and healthcare service utilisation.
Unusual occurrences like disease outbreaks, public health emergencies, or other circumstances that markedly boosted patient flow could be the cause of these peaks. Comprehending these variances is essential for hospital management’s long-term planning and resource allocation (Visualize This, 2024). The following satisfaction ratings have been determined for the patients treated during these hectic months:
The patient satisfaction rate was particularly high in the months when healthcare utilisation was primarily low. This implies that healthcare professionals may have more time or resources to devote to individualised treatment while treating fewer individuals
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