Data analyitics is a process that involves examining pattersn in healthcare and analysing the trends for decision making. In preventive care, data analysis helps healthcare organziations to predict trends of disease and institute measures to prevent reoccurrence and prevent adverse events. Data analytics is important in lowering the cost of healthcare and boosting business intelligence. The purpose of this paper is to review hospital data on readmission rates and interpret the results.
Case Review and Data Analysis
The data shows that respiratory readmissions constitute a large part of total readmission procedures compared to pneumonia or non-influenza cases. Respiratory readmissions accounted for 6.51% of the total 3.9 million readmissions done from the Diagnosis-Related Group. Pneumonia or non-influenza cases of readmissions were less than respiratory since they accounted for 2.18%. In the Principal Procedure Group respiratory readmissions were 243,375 which was 6.23% of the total. The results indicate fewer readmission cases for respiratory diseases. According to Alshabanat (2022), COPD or asthma are major respiratory diseases leading to hospital readmissions. He noted that respiratory diseases contributed significantly to unplanned readmissions due to COPD resulting in additional costs(Alshabanat (2022). The hospital care readmissions were 181,372 and 25,682 patients for acute as well as other inpatient respectively. The total number of days was 979,408.80 which was 4.66% and 97,077.96 which was 0.66% of the total. The hospital care readmissions results also indicate that both acute and other inpatient issues form a significant portion of cases. This is in line with the results of the study conducted by Zuckerman et al. (2015) XX and published in JAMA Internal Medicine which showed hospital readmissions were a persistent problem for acute as well as non-acute conditions.
Statistically Significant Test
A statistically significant test is defined as one where the probability of obtaining an observed result due to chance alone (p-value) falls below a predefined cutoff, typically 0.5. Therefore a statistically significant result implies that the observed data may not be due to chance variation alone and there exists some true relationship or difference between variables under study. Therefore this allows medical researchers to assess whether the outcomes they have found are significant and not just a result of chance.
Rationale
The chi-square test is one of the statistical tests that can be used with this data set for the hospital. The chi-square test is an association measure between two categorical variables. In this instance, the chi-square test will be used to verify whether there is a significant relationship between respiratory readmissions and Pneumonia/Non-Influenza.
Therefore it is possible to compare the observed frequencies of respiratory readmissions and pneumonia/non-influenza readmissions with the expected corresponding not associated ones. If the observed frequencies deviate from their expected p-value by a significant amount there will be an association between the type of readmission and in total number of readmissions.
We can also perform a t-test to determine the means of the total readmissions between respiratory diseases and pneumonia/non-influenza cases. The t-test is employed to establish whether there are any significant differences between the means of two independent groups. The average number of respiratory readmissions is compared with pneumonia/non-influenza readmissions to determine if there is an important difference between these two types of conditions. T-tests will help establish if respiratory readmissions are significantly above or below pneumonia/non-influenza readmission.
Accepted Confidence Intervals
In healthcare research, the most accepted confidence intervals are from 95% to 100% (King et al.,2022). The given intervals show the range in which a true population parameter might lie. For instance, a 95% confidence interval indicates that if the study is conducted repeatedly ninety-five of such intervals would contain true population parameter Alshabanat (2022) . They enable healthcare professionals to make more intelligent decisions about patient management or policy implementation. Confidence intervals also allow the comparison of different studies or populations, as they reflect confidence in estimates.
Organizing, prioritizing and reporting statistical results ensures transparency as well as reproducibility of research. Therefore researchers can validate the results using statistical reports hence the important information is not missed or misrepresented. The prioritization of statistical results helps healthcare researchers concentrate on the major findings in data analysis or between relationships. Reporting helps healthcare professionals and policymakers draw their inferences from the most relevant outcomes that are of paramount importance for guiding decisions.
Conclusion
Healthcare data is important in making decisions and imporving healthcare practices. Data is obtained from previous trends, and analysed accurately to predict future trends and identify areas of intervention. Proper data analysis depends on having statistically significant tests at set confidence intervals that ensure data validity and generalization.
References
Alshabanat. (2022). Impact of a Chronic Obstructive Pulmonary Disease (COPD) comprehensive case management program on hospital length of stay and readmission rates (Doctoral dissertation, University of British Columbia) [Doctoral dissertation]. https://open.library.ubc.ca/soa/cIRcle/collections/ubctheses/24/items/1.0417424
King, B., Ohyama, M., Kwon, O., Zlotogorski, A., Ko, J., Mesinkovska, N. A., Stanley, S., & Sinclair, R. (2022). Two phase 3 trials of baricitinib for alopecia Areata. New England Journal of Medicine, 386(18), 1687-1699. https://doi.org/10.1056/nejmoa2110343
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