Wednesday, May 6, 2020
Critical Appraisal Study of Cohort Study-Free-Samples for Students
Question: Critical appraisal on a cohort study using JBI critical assessment tool on cohort study. Answer: Introduction: Evidence based practice can be defined as any interdisciplinary approach medical practice that serves by integrating three most available research evidence on different treatments and its effect, clinical expertise and judgment, and most importantly client preferences and values while designing planning and implementing a particular care strategy. There are various benefits to evidence-based practice into the care, such as better care outcomes, enhanced patient safety, and improved living quality in the Healthcare facility (LoBiondo-Wood Haber, 2017). Evidence based practice has contributed in obliterating a lot of Healthcare complexities such as medication errors. Medication errors are any avoidable event that can cause inappropriate medication administration leading to reversible or irreversible harm to the patients (Ghaleb et al., 2010). Evidence based practice can effectively obliterate medication errors and provide a much safer care environment to the patients (Melnyk Fineout- Overholt, 2011). This assignment will attempt to critically appraise a cohort study on how evidence based practice can prevent adverse drug events in neonatal Intensive Care units. A cohort study is an investigative design of medical research and emphasizes on establishing links between factors and their outcome in the healthcare scenario. Hence it can be mentioned that a cohort study will be an accident tool in order to serve the purpose of critical appraisal study. Critical analysis: The critical analysis for this assignment will follow the JBI critical appraisal tool. The paper on the review by Morris et al. discusses the effectiveness of a barcode medication administration system (BCMA) as a mode of evidence-based practice that has the potential to reduce preventable adverse drug events in neonatal Intensive Care Unit (NICU) (Morriss et al., 2009). The very first question which focuses on the two groups selected in the study and whether they are similar and recruited from the same population. It has to be mentioned that the two groups that was elected in the study design was from the same population of a 36 bed NICU setting. The population is divided by 50%, half of the population where exposed to the BCMA system right away while the latter half was exposed later along the study. The grouping to be effective the two groups selected for the study has to be as similar as possible in all manners except for the exposure and it can be mentioned that for the study th e authors have selected a particular Care Unit where the two different groups and a comparison for only dissembler in terms of the exposure to the BCMA a system. The next question focuses on exposure measurement which is a critical criterion of an authentic and reliable cohort study. Here the exposure measurement was by a structured daily audit of each of the subjects, paper and electronic medical record to identify the preventable ADEs. However there are no detailed description of the particular exposure measurement technique implemented for the both of the select example Groups for the study. For the next question, the nature of exposure measurement is more or less accurate as the audit procedure included the use of triggers to enhance the identification of any adverse drug event. It has to be mentioned that it the current measurement of the exposure was enough to decide the occurrence of preventable ADEs in presence and absence of BCMA system. There is no requirement for a past e xposure measurement for the study hence the exposure measurement for the study can be considered valid. As the inter observer exposure measurement reliability was statistically adjusted in the study it can be mentioned that the reliability of the exposure measurement is also valid (Song Chung, 2010). The 4th question of the critical appraisal tool focuses on confounding factors. It has to be mentioned that for the study there when 958 study subjects. There are many confounding factors and the segregation of the factors into the two groups into BCMA and No-BCMA have been represented in the table 1. The confounding factors present included personal characteristics of the patients, total Administration doses, and the nursing capacity. It has to be mentioned that for any research study the presence of confounding factors have the ability to influence the direction of the study results, and for any cohort study to be high quality the potential confounders has to be measured and identified whenever possible (Song Chung., 2010). As the research study had identified and measures the number of confounders in between two groups this criteria can be considered met. Fifth question addresses whether the research study has employed any strategy to deal with the confounding factors. Although t he authors have mentioned that the confounding factors like nursing capacity were addressed in the article however no detailed strategy was explained in the study. In the fifth question it can be said that the participants were free of the outcome of the interest at the beginning of the study. The Neonatal Intensive Care Unit (NICU) of University of Iowa Childrens Hospital recruited patients in an unbiased manner. The chance of the patient to be admitted in the section 1 (with equipped with the barcode medication administration [BCMA]) is independent to those of section 2 with no BMCA. However, the nurses who were the main candidate to organize the process of BMCA were given training in relation to BMCA and were then assisted by super-users while operating the patients. Thus there might be a chance of bias as nurses after obtaining the training might be aware of the outcome and thereby increasing the chance of bias (Hopkins Batterham, 2016). The study further claimed that they failed to conduct a blinded trial. According to Morriss et al. (2009), the investigators who made the final designation were blinded about the potential of the Adverse D rug Event (ADE) and then they adjusted the analyses for the subject along with the environmental differences that might act as the confounders. The outcome was measured in a valid manner because it utilized a comparison mode of measuring the outcome in systematic review (Shamseer et al., 2015). The paper here employed a structure of daily audit of each subjects paper and electronic medical record for 24 hours to spot the medication errors and the rate of occurrences that were significant to or preventable in relation to adverse drug events. All the medication orders were critically reviewed against the latest and the continuing orders and are then compared with the paper or with the electronic Medication administration record (MAR). The audit procedures were also used to enhance the identification of the Averse drug event (ADE). The follow-up time was 50 weeks that is significant enough to tally the medication error in accordance with the BCMA. Within 50-week (12 months approx) the medication errors and other potential and preventable ADV were detected in a structure manner for a daily audit. This goes in sync with the study conducted by de Arajo Lobo et al., in the year 2014. In this study, they conducted an observational cross-sectional study for 8 months to detect medication error. The entire sample size was strictly followed up with zero drop-out. There were total 958 study subjects with 856 unique patients. Initially all the patients were studied for 19 consecutive weeks in the absence of BCMA system. After an interval of 4-week (when no data was collected) and BCMA installation was done) study period of 9 consecutive weeks and then 3 consecutive weeks were conducted with the BCMA trained nurse. The said approached conducted in this study goes with the approach prosed by Sedgwick in the year 2012. The strategies that are used for the complete follow were not also effective. According to Morriss et al. (2009), the study conducted in the neonatal unit limit ed the generalized results. This is because the hospitals that are already equipped with computer prescribed order entry (COPE) or other clinical services that have lower rates of preventable ADE may fail to experience as great at the relative value. Appropriate statistical analysis was performed via using Kruskal-Wallis test in order to calculate the p score. Based on the statistical analysis, it was observed that there was significant change in the preventable ADEs. According to Acar and Sun (2013), Kruskal-Wallis test is the best ever test used to calculate the significant statistical results via eliminating the uncertainty of the study. Conclusion: Thus from discussion it can be concluded that the results obtained from the study conducted by Morriss et al., 2009 showed that the BCMA system help in the reduction of the risk of the targeted preventable ADE via controlling the number of the medication doses, subject and the day. This study thus will help in the reduction of the fatal risk of the neonatal care unit via reducing the harm coming from the medication error. It will also help in the advancement of the disease process via including technological based approaches in order to mitigate the error. The study also showed that the computer prescriber order entry along with the clinical decision support software helps in reducing the threat coming from the ADE. The study also highlighted the same results is applicable to diverse group of people include across the genders and even among the twins. However, the capacity of the nurse group and their proper training should be taken into consideration. References: Acar, E. F., Sun, L. (2013). A generalized KruskalWallis test incorporating group uncertainty with application to genetic association studies.Biometrics,69(2), 427-435. de Arajo Lobo, M. G. A., Pinheiro, S. M. B., Castro, J. G. D., Moment, V. G., Pranchevicius, M. C. S. (2013). Adverse drug reaction monitoring: support for pharmacovigilance at a tertiary care hospital in Northern Brazil.BMC Pharmacology and Toxicology,14(1), 5. Ghaleb, M. A., Barber, N., Franklin, B. D., Wong, I. C. K. (2010). The incidence and nature of prescribing and medication administration errors in paediatric inpatients.Archives of Disease in Childhood, adc158485. Hopkins, W. G., Batterham, A. M. (2016). Error rates, decisive outcomes and publication bias with several inferential methods.Sports medicine,46(10), 1563-1573. Lisby, M., Nielsen, L. P., Brock, B., Mainz, J. (2010). How are medication errors defined? A systematic literature review of definitions and characteristics.International Journal for Quality in Health Care,22(6), 507-518. LoBiondo-Wood, G., Haber, J. (2017).Nursing Research-E-Book: Methods and Critical Appraisal for Evidence-Based Practice. Elsevier Health Sciences. Melnyk, B. M., Fineout-Overholt, E. (Eds.). (2011).Evidence-based practice in nursing healthcare: A guide to best practice. Lippincott Williams Wilkins. Morriss, F. H., Abramowitz, P. W., Nelson, S. P., Milavetz, G., Michael, S. L., Gordon, S. N., ... Cook, E. F. (2009). Effectiveness of a barcode medication administration system in reducing preventable adverse drug events in a neonatal intensive care unit: a prospective cohort study.The Journal of pediatrics,154(3), 363-368. Morriss, F. H., Abramowitz, P. W., Nelson, S. P., Milavetz, G., Michael, S. L., Gordon, S. N., ... Cook, E. F. (2009). Effectiveness of a barcode medication administration system in reducing preventable adverse drug events in a neonatal intensive care unit: a prospective cohort study.The Journal of pediatrics,154(3), 363-368. Sedgwick, P. (2012). Observational study design.Bmj,345, e5856. Shamseer, L., Moher, D., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., ... Stewart, L. A. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation.Bmj,349, g7647. Song, J. W., Chung, K. C. (2010). Observational studies: cohort and case-control studies.Plastic and reconstructive surgery,126(6), 2234
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