Healthcare relies on descriptive statistics, particularly in nursing. These provide vital instruments for summarizing and interpreting healthcare data. This paper, therefore, discusses the importance of descriptive statistics in nursing with three examples in practice and how evidence-based practice (EBP) is implemented using them.
Significance Of Descriptive Statistics
Descriptive statistics is one of the most basic principles in data analysis; it helps summarize and simplify complex datasets. Descriptive statistics is the first step in analyzing data. Summarizing data characteristics is essential for understanding and interpreting information. These include measures such as the mean, median mode, and range, which provide insight into a dataset’s central tendency and dispersion. The paramount importance of descriptive statistics is seen in their ability to summarize a given data set (Kaliyadan & Kulkarni, 2019). This means that rather than go through each data point, analysts can use summary statistics to get a general idea of the dataset. This kind of summarization is beneficial when working with large sets where it would be impractical to examine each point manually.
Moreover, graphs like histograms, box plots, and scatter plots can be made through descriptive statistics. These visual aids help analysts see trends and patterns in data and quickly identify outliers to make more informed decisions and insights about them. Descriptive statistics also assess data quality by identifying outliers and anomalies within datasets (Kaliyadan & Kulkarni, 2019). Finding these irregularities is significant for maintaining the quality of any information therein so that other analyses can be done more accurately.
Application in Nursing and Evidence-Based Support
According to Duff et al., (2020) descriptive statistics can be used to summarize the demographic characteristics of patients in a healthcare setting. This includes age, gender, ethnicity, socioeconomic status, and comorbidities. Knowing patient demographics is essential for customizing care to specific populations. For example, several reports have shown that healthcare outcomes can vary significantly between age groups or among ethnic groups. These disparities can be identified by healthcare providers analyzing data on demography using descriptive statistics and interventions formulated as a result (Duff et al., 2020). EBP studies often involve collecting and analyzing demographic data to guide evidence-based care delivery.
Nurses often record medication administration and patient adherence. Dosages, frequencies, and patient compliance rates can be summarized using descriptive statistics. EBP Support: Research needs to focus on medication management and adherence for better outcomes amongst patients. It has been established that medication adherence is a significant factor in controlling chronic diseases such as diabetes and hypertension (Macido, 2019). To identify areas where interventions are necessary and evaluate the efficacy of strategies promoting adherence, nurses can use descriptive statistics to monitor medication administration and adherence rates. EBP suggests applying data-driven methods to enhance drug compliance and patient safety.
One is to utilize descriptive statistics to assess clinical outcomes such as infection rates, readmission rates, and patient satisfaction scores. Evidence-based practice is essential from the point of monitoring and evaluating clinical outcomes so that there are better healthcare services. For instance, a hospital can use surveys to collect patient satisfaction data, which can be analyzed using descriptive statistics (Shuman et al., 2019). By doing this, medical professionals can identify areas in patient care processes that need improvements by identifying areas of dissatisfaction. Descriptive statistics are often used in research studies to give the baseline clinical outcome information before interventions that work as evidence for quality improvement initiatives.
To conclude, descriptive statistics are essential in nursing because they help summarize and assess data quality. They are used in demographic analysis, medication management, and clinical outcome evaluation to support Evidence-Based Practice for improved healthcare delivery.
References
Duff, J., Cullen, L., Hanrahan, K., & Steelman, V. (2020). Determinants of an evidence-based practice environment: an interpretive description. Implementation science communications, 1(1), 1-9. https://implementationsciencecomms.biomedcentral.com/articles/10.1186/s43058-020-00070-0
Kaliyadan, F., & Kulkarni, V. (2019). Types of variables, descriptive statistics, and sample size. Indian Dermatology Online Journal, 10(1), 82. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362742/
Macido, A. (2019). A Nurse-Led Inpatient Diabetes Self-Management Education and Support Program to Improve Patient Knowledge and Treatment Adherence. Journal of Health Education Teaching, 10(1), 1–10. https://eric.ed.gov/?id=EJ1236325
Shuman, C. J., Powers, K., Banaszak‐Holl, J., & Titler, M. G. (2019). Unit leadership and climates for evidence‐based practice implementation in acute care: a cross‐sectional descriptive study. Journal of Nursing Scholarship, 51(1), 114-124. https://sigmapubs.onlinelibrary.wiley.com/doi/abs/10.1111/jnu.12452
write