Doctor of Nursing Practice (DNP) Final Projects
The Doctor of Nursing Practice Final Projects collection contains the completed works of students from the DNP Program at Arizona State University's College of Nursing and Health Innovation. These projects are the culminating product of the curricula and demonstrate clinical scholarship.
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- Creators: Crawford, Daniel
- Creators: Bain, Ilyssa D.
- Creators: Cowart, Amanda
Method: This project aimed to provide an evidence-based education for intake nurses to understand prevalence of PTSD and to use a screening tool Primary Care PTSD for DSM-5 (PC-PTSD-5) in a non-VA behavioral health facility.
Setting: The project site was a civilian behavioral health facility located in West Phoenix Metropolitan area. The behavioral health facility serves mental health and substance abuse needs. Project implementation focused on the intake department.
Measures: Sociodemographic data, PTSD diagnosis criteria, prevalence and PC-PTDSD-5 screening tool knowledge collected from pre and posttest evaluation. Patients’ charts for those admitted 6-week before and 6-week after the education to calculate numbers of screening tools completed by nurses at intake assessment.
Data analysis: Descriptive statistics was used to describe the sample and key measures; the Wilcoxon Signed Rank Test was used to examine differences between pre-test and post-test scores. Cohen’s effect size was used to estimate clinical significance.
Results: A total of 23 intake nurses (87.0% female, 65.2% 20-39 years old, 52.2% Caucasian, 95.6% reported having 0-10 years of experience, 56.5% completed Associate’s degree) received the education. For PTSD-related knowledge, the pre-test score (Mdn = 6.00) was significantly lower than the post-test score (Mdn = 10.00; Z= -4.23, p < .001), suggesting an increase of PTSD knowledge among nurses after the education. Regarding the diagnosis, the percentage of patients who were diagnosed with PTSD increased from (0.02% to 20% after the education).
Discussion: An evidence-based education aimed at enhancing intake nurses’ knowledge, confidence and skills implementing a brief and no-cost PTSD screening tool showed positive results, including an increase of PTSD diagnosis. The implementation of this screening tool in a civilian primary mental health care facility was feasible and helped patients connect to PTSD treatment in a timely fashion. Continued use of paper version of screening tool will be maintained at facility as an intermediary solution until final approval through parent company is received to implement into electronic medical records.
Methods: A multifaceted intervention was utilized that included educational sessions for providers, adjustments to the electronic health record (EHR), access to toolkits, and workflow changes. Pediatric patients aged 5-18 years and diagnosed with asthma (N = 173) were evaluated using a pre-post design. Provider adherence to key components of clinical practice guidelines were assessed prior to implementation, and a three and six months post-implementation. Data was analyzed using descriptive statists and the Friedman’s ANOVA by rank.
Results: Provider education, EHR adjustments, provider toolkits, and changes to office workflow improved provider adherence to key aspects of asthma clinical practice guidelines. A significant difference was found between the pre and post implementation groups (p < .01).
Conclusion: Increased adherence to clinical practice guidelines leads to fewer complications and an overall improved quality of life. Continuing provider education is critical to sustained adherence.
Background:
Asthma is one of the most common pediatric diseases, affecting 6.3 million U.S. children in 2014, that can result in negative health outcomes if not managed correctly due to it's chronic and complex nature requiring frequent and close management (NHLBI, 2007). The National Heart, Lung, and Blood Institute's (NHLBI) Guidelines for the Diagnosis and Management of Asthma will be implemented into practice to determine the health outcomes of patients before and after guideline implementation.
Methods:
Inclusion criteria includes patients 5-18 years with a history of asthma, recurrent albuterol use, or intermittent symptoms of airflow obstruction. Data will be collected through EHR data reports at pre implementation, 3 months, and 6 months post implementation and will be analyzed using SPSS. Descriptive statistics, paired t-tests, and a Friedman's ANOVA will be conducted to analyze data.
Results:
A Friedman ANOVA was conducted comparing the outcome variables six months priot to the practice change, at three months post implementation, and at six months post implementation. A significant difference was found (x2(15) = 216.62, p<.05). The implementation of the practice change significantly affected the outcome variables.
Conclusions:
In general, the implementation of a practice change to use evidence based NHLBI ERP-3 Asthma Guidelines, along with staff and provider education sessions and creation of standardized assessment and documentation tools resulted in positive changes in the outcomes variables. Findings from this study along with the literature of implementing evidence based asthma guidelines supports similar practice change implementations in other pediatric primary care clinics.
Background:
Approximately 1 in 5 U.S. school-aged children are obese. There are many known health complications associated with obesity including premature death. Family-based obesity interventions that promote healthy lifestyle habits are effective at enabling children to make changes needed to avoid long-term health complications associated with obesity. The purpose of this evidence-based practice intervention was to evaluate the effectiveness of a family-based obesity intervention on familial lifestyle behaviors related to nutrition, physical activity, and screen time.
Methods:
Two overweight-obese children (according to CDC criteria) ages 8-12 years old visiting a pediatric primary care clinic in a suburban neighborhood located in the southwest region were recruited to participate in this evidence-based practice intervention based on inclusion and exclusion criteria. Familial lifestyle behaviors were assessed using the Family Health Behavior Scale (FHBS) prior to receiving an educational intervention addressing nutritional, physical activity, and screen time recommendations and again after following these recommendation for 6-weeks. Additionally, scheduled follow-up phone calls were made every 3 or 6-weeks addressing any parental questions that surfaced. Data was insufficient for statistical analysis, however, anecdotal recommendations for future implementation of this intervention resulted.
Results:
Of the two patients who participated, pre- and post-intervention data was only attainable from one patient. That patient did have improved scores within each of the 4 FHBS subscales (parent behaviors, physical activity, mealtime routines, and child behaviors). Overall, 11 of the 27 behaviors assessed improved, 12 behaviors resulted in no change, and 4 behaviors worsened. Recommendations related to a more successful implementation of this intervention in the future include improved provider participation (buy-in), utilization of broader inclusion criteria, consideration of the implementation time-frame, and application of the Health Belief Model for addressing existing barriers for each patient prior to implementing the intervention.
Conclusions:
In order to determine the effectiveness of this intervention a larger sample size and completed post-intervention data are needed. The small sample size and lack of post-intervention data inhibits proper data analyzation and significance from being determined.