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, which could result in improved patient experiences. The histogram unequivocally shows that over roughly 32 months, patient satisfaction ratings surpassed 55, indicating a typically favourable patient reaction during those times. Patients, on the other hand, expressed modest levels of satisfaction over the course of 38 months, with scores below 55.
In order to determine if greater levels of patient satisfaction are linked to fewer readmissions or whether dissatisfaction is linked to higher rates of patients returning to care, the relationship between patient satisfaction and readmission rates can be thoroughly investigated for every month.
MHA FPX 5107 Assessment 1
In the end, readmissions were low for about 25 months, indicating times when healthcare outcomes were consistent and few patients needed to be readmitted. Nonetheless, readmissions peaked around 21 months later, which may indicate an increase in patient health issues or a lack of proper discharge planning at that time. A more stable level of patient returns for care was indicated by readmissions, which during a 24-month period dropped within a moderate range between 0.089 and 0.130.
The histogram data, which is divided into several ranges and shows the varying readmission rates over different months, can be used to spot patterns or trends associated with particular periods of time or circumstances. By analysing these variations, efforts to enhance patient care and cut down on needless hospitalisations can be informed and factors impacting readmission rates can be identified (Silverwood et al., n.d.).
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