Evidence-Based Practice in Nursing: Diabetes and DNP Roles

Introduction to Evidence-Based Practice in Nursing

Evidence-based practice (EBP) is the method of gathering, synthesizing, and applying research findings to improve clinical practice, the work environment, and patient outcomes. According to the American Nurses Association (ANA), medical treatments should represent functional, systematic decisions grounded in EBP research. Applying the EBP approach to clinical practice helps deliver the highest quality and most cost-effective patient care possible. This paper discusses the essential qualities of EBP as they relate to nursing research, with particular reference to culturally tailored diabetes education aimed at reducing HbA1c levels among Americans of Asian descent with type 2 diabetes.

Meta-Analysis and Research Translation

Meta-analysis provides the foundation for evidence-based practice because its outcomes can be used to establish new best-practice recommendations or to resolve conflicts among competing treatments. In many healthcare situations, it is common to find numerous primary studies that have attempted to answer similar questions; however, varying populations and settings, along with inconsistent results, make it difficult to derive a comprehensive understanding of outcomes that can be applied in clinical practice. Meta-analysis — the statistical method used in systematic reviews — offers a solution to this problem. A systematic review combines an appropriate research framework to clarify what is known and unknown about the potential benefits and harms of treatments, devices, and other healthcare interventions, drawing together outcomes from multiple primary studies, generally randomized controlled trials (RCTs) (Holly & Slyer, 2013). In this way, the overall effectiveness of a specific healthcare intervention can be established, which helps inform its use within the healthcare system.

Meta-analysis has been used to examine the effectiveness of a culturally tailored diabetes educative intervention (CTDEI) on glycemic management in ethnic minorities with type 2 diabetes. The research process involved searching databases including PubMed, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Education Resources Information Center (ERIC), PsycINFO, and ProQuest for randomized controlled trials. A meta-analysis was conducted to evaluate the impact of diabetes education on glycemic management, measured by glycosylated hemoglobin (HbA1c) values, in ethnic minority populations with type 2 diabetes. Researchers determined the effect size (ES) based on HbA1c change from baseline to follow-up between treatment and control groups (Nam et al., 2012). According to this meta-analysis, CTDEI was found to be effective for improving glycemic management among ethnic minorities. The magnitude of the effect varied depending on the intervention setting, baseline HbA1c level, and the timing of HbA1c measurement.

Comparative Effectiveness Research in Diabetes Education

Comparative effectiveness research is important in translating research into practice. Ehrmann et al. (2016) employed a comparative effectiveness research design to evaluate the RCT-verified efficacy of the diabetes education program for type 1 diabetes (PRIMAS) against the effectiveness observed in an implementation trial (IT) conducted within routine care settings. The study included 75 individuals with type 1 diabetes who received PRIMAS via an RCT, while 179 individuals were observed within an implementation trial. Baseline characteristics and treatment outcomes at a six-month follow-up — including changes in HbA1c, hypoglycemia episodes, and diabetes-related distress — were compared across both groups.

The results demonstrated that the clinical conditions used as indicators for participation in diabetes education, such as suboptimal glycemic management or elevated diabetes-related distress, were considerably more prevalent within the RCT cohort than in the implementation trial cohort. Findings related to glycemic management may have been influenced by the additional RCT requirement of HbA1c greater than 7.5 percent. When both cohorts were combined, diabetes education with PRIMAS produced a 0.36 percentage-point reduction in HbA1c. Comparing HbA1c reductions across the RCT and implementation trial revealed no significant differences between settings; the mean difference was only 0.01 percent. The 95-percent confidence interval for this difference did not exceed the 0.4-percent threshold commonly used to establish medical non-inferiority for improvements in glycemic management.

The effectiveness of PRIMAS in routine care settings was comparable to the efficacy demonstrated in the RCT. Clinical improvement was independent of the setting in which PRIMAS was evaluated. The PRIMAS training program for type 1 diabetes can therefore be delivered in routine care settings without a loss of effectiveness relative to its original assessment in an RCT. However, the study did not provide explicit evidence regarding whether its findings were formally translated into widespread clinical practice.

Statistical Significance Versus Clinical Significance

While many studies focus on statistical significance, clinicians and healthcare professionals should also concentrate on clinically meaningful improvements. A research outcome can be statistically significant without being clinically significant, and vice versa. Unfortunately, clinical significance is not always clearly defined or understood, and many research consumers incorrectly equate statistically significant results with clinical importance. Clinically relevant changes in outcomes are recognized — sometimes interchangeably — by terms such as "minimal clinically important differences (MCID)," "clinically meaningful differences (CMD)," and "minimally important changes (MIC)" (Page, 2014).

Generally, these terms all refer to the smallest change in an outcome score considered "important" or "worthwhile" by the clinician or the patient, or that would prompt a change in patient management. Changes in outcomes that exceed these minimum thresholds are considered clinically relevant. It is important to recognize that both harmful and beneficial changes may result from treatment; therefore, the term "clinically essential changes" should be used to identify both minimum and beneficial differences, as well as harmful modifications.

Several researchers have established MCID, MIC, and CMD values for various outcome measures. It is essential to determine clinical importance within a patient population with comparable diagnoses and pain levels. For example, a clinically meaningful change in pain among patients with shoulder joint pain differs between those with intact rotator cuffs and those with a ruptured rotator cuff (Page, 2014). Patients with severe pain or higher pain intensity may require a smaller change in pain for that change to be considered clinically significant.

Interpretation of clinical research should not rely exclusively on the presence or absence of statistically significant differences. Given the heterogeneity of patient samples, small sample sizes, and limitations in hypothesis testing, practitioners should also consider scientifically relevant measures such as effect size, clinically meaningful differences, confidence intervals, and magnitude-based inferences.

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