To obtain more precise results on the selected healthcare problem, the student altered the PICOT question from a foreground to a background one. Thus, the PICOT question is, “In pregnant African American women (P), will an educational program (I) compared to no education (C) increase the rate of breastfeeding (O) over three months (T)?” The best method for evaluating the effect of the intervention would be to utilize a t-test, which would enable the author to compare and contrast the findings pertaining to the intervention and referent groups. Therefore, it will be necessary to recruit pregnant women in the study, using such eligibility criteria as second- or third-trimester singleton pregnancy and the absence of premature birth risks. Other factors to consider will be the absence of conditions excluding or complicating breastfeeding and belonging to the African American race.
The Evaluation Method and Its Meaning
The selected evaluation method is both the most suitable and reliable one. Such tests allow identifying the statistical significance (or the lack of it) in the data analyzed. Statistical Package for the Social Sciences (SPSS) will be utilized to run the test and assess the results received. Scholars employ this approach is frequently both in general and specifically in studying breastfeeding intervention effects. Overall, researchers commonly use statistical tests to evaluate the effect of breastfeeding education effects. Bonuck et al. (2014) have employed a statistical test to check the impact of an intervention on breastfeeding intensity and duration.
Giglia, Cox, Zhao, and Binns (2015) have investigated the influence of an Internet intervention on the breastfeeding rate increase with the help of the chi-squared test. The study by Wu, Hu, McCoy, and Efird (2014) has involved the analysis of variance (ANOVA) F-test to analyze the effect of a breastfeeding self-efficacy intervention on women’s decisions regarding the issue. Thus, one can justify the use of a t-test by positive examples from other studies since scholars report statistical tests to offer the most accurate results on the evidence-based problem.
The numbers in the test will mean the rate of change in women’s attitudes toward breastfeeding after the intervention. The student expects that in the intervention group, the rate will be higher than in the referent group. The minimal significance level of the test will be 0.05. If the difference between the two groups is 0.05 or higher, the intervention will be considered as successful. Following the study by Howell, Bodnar-Deren, Balbierz, Parides, and Bickell (2014), the independent variable of the present paper will be the intervention status (positive or negative). The dependent variable will be the educational intervention.
The Significance of the Result
The result will matter because the current level of breastfeeding among the target population group is too low to be sufficient. As Jones, Power, Queenan, and Schulkin (2015) have found, minority women face more barriers to breastfeeding than the general population. The lack of proper education on the matter is one of such obstacles, so the results of an educational intervention will indicate whether and to what extent such approaches can help mitigate the problem.
Benchmarks, NDNQI Data, and KPI Metrics
The Centers for Disease Control and Prevention have set the benchmarks for breastfeeding. Specifically, the American Academy of Pediatrics advises breastfeeding as the exclusive approach to feeding infants for the first six months (“Breastfeeding report card,” 2018). Another benchmark is the combination of breastfeeding with complementary foods from the sixth to the twelfth months. The National Database for Nursing Quality Indicators (NDNQI) views education on and assistance with breastfeeding as one of the core nursing competencies (“NDNQI: A Press Ganey solution,” 2016). Also, the NDNQI data indicate that breastfeeding is one of the risk factors of newborn falls and drops (The Joint Commission, 2018). Thus, these data show that some of the most esteemed healthcare institutions evaluate the significance of breastfeeding highly.
To evaluate the clinical practice change, the author will utilize several KPI metrics. First of all, the study will evaluate patient satisfaction and patient experience rates. These measures will help to assess the effectiveness of the intervention. Additionally, they will promote the understanding of patients’ satisfaction levels. The student will exploit the first metric chosen for evaluation only once, after the intervention. It is possible to make use of a questionnaire at this point to find out patients’ perceptions about their participation in the project. Those content with the results will constitute the high-satisfied group, whereas those not content with the outcomes will form the low-satisfied group.
The overall state of wellbeing, as well as physical and mental health of participants and their newborn children, will serve as an indicator of patient satisfaction. The more patients report positive effects of the intervention, the more useful the project will be. This KPI metric is rather crucial since the very essence of any healthcare-related process is the improvement of people’s health.
Secondly, the study will compare operation costs before and after the intervention. It is no secret that hospitals sometimes have to spend too much money on longer patient stay or readmissions. The lack of breastfeeding leads to the deterioration of children’s health. As a result, much financial support at the moment applies to deal with the health problems of newborns from African American families (Johnson, Kirk, Rosenblum, & Muzik, 2015). To evaluate the effectiveness of the intervention, the student will record operation costs related to healthcare programs involving newborns before and after the project. The author expects that after the intervention, operation costs of the hospital will become lower, and the healthcare facility will be able to save money on some urgent needs.
Finally, it will be necessary to evaluate the operational workload to assess whether it has decreased after the intervention. As well as the previous measure, operation costs, the operational workload is a crucial factor impacting the functioning of the hospital. The fewer women choose to adhere to breastfeeding practices, the worse immune systems their babies have. The more babies become ill, the more personnel is required to look after them. Frequently, hospitals experience nursing shortages, which affects the operations in the facility badly. The evaluation of operational workload before and after the intervention will allow tracing whether the project has enhanced the workload and alleviated the healthcare professionals’ burnout. The identified KPI measures will make it possible to assess whether the project has been successful.
References
Bonuck, K., Stuebe, A., Barnett, J., Labbok, M. H., Fletcher, J., & Bernstein, P. S. (2014). Effect of primary care intervention on breastfeeding duration and intensity. American Journal of Public Health, 104(S1), S119–S127. Web.
Breastfeeding report card. (2018). Web.
Giglia, R., Cox, K., Zhao, Y., & Binns, C. W. (2015). Exclusive breastfeeding increased by an internet intervention. Breastfeeding Medicine, 10(1), 20–25. Web.
Howell, E. A., Bodnar-Deren, S., Balbierz, A., Parides, M., & Bickell, N. (2014). An intervention to extend breastfeeding among black and Latina mothers after delivery. American Journal of Obstetrics and Gynecology, 210(3), 239.e1–239.e5. Web.
Johnson, A., Kirk, R., Rosenblum, K. L., & Muzik, M. (2015). Enhancing breastfeeding rates among African American women: A systematic review of current psychosocial interventions. Breastfeeding Medicine, 10(1), 45–62. Web.
The Joint Commission. (2018). Preventing newborn falls and drops. Web.
Jones, K. M., Power, M. L., Queenan, J. T., & Schulkin, J. (2015). Racial and ethnic disparities in breastfeeding. Breastfeeding Medicine, 10(4), 186–196. Web.
NDNQI: A Press Ganey solution. (2016). 2016 NDNQI RN survey with practice environment scale. Web.
Wu, D. S., Hu, J., McCoy, T. P., & Efird, J. T. (2014). The effects of a breastfeeding self-efficacy intervention on short-term breastfeeding outcomes among primiparous mothers in Wuhan, China. Journal of Advanced Nursing, 70(8), 1867–1879. Web.