NUR 611 Module 6 Assignment PowerPoint Presentation Advanced Practice

Sample Answer

Presentation’s Focus Areas

Welcome to this presentation regarding an issue of advanced practice. It describes the role of Advanced Practice Registered Nurses (APRNs) in providing high-quality patient care and their valuable role in addressing problems through research and evidence-based practice. The first major part outlines the research problem related to advanced nursing practice and the research question.

The second section is the type of study appropriate to answer the research question. This is followed by a discussion of a middle-range theory for the research and application of nursing theories. The other key section is a literature review of a quantitative study that explores the research problem in the present nursing practice.

Research Problem Related to Advanced Nursing Practice

Sepsis has far-reaching impacts on the patient’s health and management costs. Its early identification as a current research problem in advanced nursing practice is critical due to its high mortality rates and challenging early detection. Recognizing sepsis in its early stages can significantly improve outcomes by enabling prompt interventions, including timely administration of antibiotics and fluid resuscitation to prevent health complications associated with severe sepsis.

Early detection is linked with positive outcomes like reduced hospital stays, low readmission rates, and a significant decline in in-hospital stays (Burdick et al., 2020; Persson et al., 2024). The research question to provide information about early identification of sepsis is How does early identification of sepsis through a provider-led sepsis screening tool impact treatment outcomes?

The Study to Answer the Research Question

Research questions are based on the purpose of the study. The rigorous nature of the proposed research necessitates quantitative research. Within nursing and the broader healthcare sector, quantitative research involves collecting and analyzing numerical data to allow researchers to make accurate predictions and find patterns in data sets (Grove & Gray, 2022). A similar scientific inquiry approach will help examine the relationship between early detection of sepsis and treatment outcomes.

The quantitative approach achieves high objectivity since the structured data collection process minimizes potential researcher bias. Numerical data further implies statistically valid results that can be generalized to broader populations to achieve similar outcomes in other healthcare settings.

Middle-Range Theory and Application of Nursing Theories in the Research

Middle-range theories are narrow in scope and testable in nursing research. Katharine Kolcaba’s theory of comfort is appropriate for research on identifying sepsis due to its primary emphasis on comfort as a fundamental component of patient care. Kolcaba stressed that nurses achieve high-quality care by addressing the patient’s comfort needs and preferences (Lin et al., 2023).

Effective pain management, individualized care, and early detection of illness to prevent emotional and physical distress optimize comfort and well-being. Nursing theories apply to the research problem as a source of nursing ideas and how it should be structured to achieve a specific patient outcome. They also provide the conceptual framework (roadmap) for the research to enable the researcher to understand and analyze the research problem.

Literature Review Purpose and Design

APRNs can utilize different tools for early detection of sepsis to improve treatment outcomes. Adams et al. (2022) conducted a study on the effectiveness of an alert system for early detection of sepsis. In this prospective, multi-site cohort study, researchers explored the link between patient outcomes and provider interaction with the alert system. This alert system, the Targeted Real-time Early Warning System (TREWS), alerted healthcare providers before the initiation of antibiotic therapy.

Targeted outcomes included reducing the in-hospital mortality rate, organ failure, and length of stay. Researchers expected a significant difference in outcomes between the group whose alert was confirmed by a provider within three hours and those who did not.

Blinding, Randomization, and Ethical Issues

Researchers may influence the study outcomes by allowing the participants to have too much information about the study. Blinding involves withholding information regarding which group the participants have been assigned to (Persson et al., 2024). Randomization helps mitigate selection bias. It involves randomly assigning participants to separate groups by chance (randomly) and giving them different treatments whose outcomes are compared (Berger et al., 2021). In this quantitative study, Adams et al. (2022) did not reveal whether the participants belonged to the alert or comparison group.

However, participants were assigned to each group depending on the risk level without randomization. Ethical considerations include conducting a risk-free study with voluntary participation through informed consent. The research was approved by Johns Hopkins University’s internal review board to ensure it adhered to all ethical requirements.

Sampling, Recruitment, and Sample Size

Researchers employed the purposive sampling method. In this approach, eligible participants are intentionally selected based on their characteristics. Accordingly, Adams et al. (2022) recruited patients of eighteen years and above who presented to the ED or were admitted to an inpatient unit from two academic and three community hospitals that utilized the alert system. For the primary analysis of the targeted patient outcomes, Adams et al. (2022) focused on patients with an alert trigger at most one hour before admission. These patients also met the sepsis criteria based on HER-based sepsis phenotyping.

The EHR-based sepsis phenotyping is a refinement criterion where researchers exclude patients with features consistent with sepsis but can be explained by other conditions like hemorrhage (Adams et al., 2022). Researchers also excluded all patients who received an antibiotic before the alert.

Methods, Instruments, Validity, and Reliability

Adams et al. (2022) obtained patient data from electronic health records (EHRs). For labs, vital signs, the patient’s total sequential organ failure assessment (SOFA) score, and the average Acute Physiology and Chronic Health Evaluation score, researchers utilized the worst measurement in the last 24 hours as the reference data. In research, validity represents the accuracy of a measure (how well an instrument is), while reliability entails consistency (Ahmed & Ishtiaq, 2021).

In this case, a data collection tool is reliable if it can reproduce the results under similar conditions. EHRs’ data quality depends on the completeness of the records, a critical study element that was accomplished in the study. Patients’ conditions and the features of the clinical environment may cause variations that researchers addressed by adjusting relative and absolute reductions.

Data Analysis, Statistics, Measurement Levels

Adams et al. (2022) applied descriptive statistics to analyze the results. Through descriptive statistics, researchers summarize and describe the primary features of a data set (Sharma, 2022). These features could be differences between groups, improvements, and others. For the statistical tests, Adams et al. (2022) applied logistic regression to analyze in-hospital mortality rates and differences between the alert and comparison groups. Linear regression was used for mean SOFA progression and the median length of stay.

The other test was the heteroskedasticity-robust estimators for standard errors and confidence intervals (CIs). The main data (mortality, SOFA, and length of stay) were measured at the ratio level since the variables can be ordered, differences obtained, and have a value of true zero.

Study Findings and Limitations

The study’s findings demonstrate a positive link between early detection of sepsis and treatment outcomes. Adams et al. (2022) found that early warning systems for sepsis identification help healthcare providers identify at-risk patients and prioritize early treatment. Outcomes achieved included reduced in-hospital mortality rate, organ failure incidences, and length of hospital stay. Researchers identified several limitations typical in quantitative research. For instance, the alert system had prespecified settings, affecting the data obtained. Secondly, the study was not free from residual confounding due to the lack of randomization.

The other limitation necessitating further studies in broader populations is limited generalizability since the study was conducted in a single hospital system and geographical region.

Trustworthiness and Applicability of the Study

The trustworthiness of research is identified by indicators such as transferability and dependability. The methods and the tools applied in data collection and analysis make the study trustworthy and applicable in nursing to improve patient outcomes. Generally, Adams et al. (2022) conducted credible research, and nurses can depend on its findings as a guide to practice improvement. The experimental nature of the study allowed the researcher to measure outcomes and compare results between different groups.

The statistical significance nature denotes that results are not by chance but due to a specific cause, such as a clinical intervention. The study’s primary statistical significance test was the p-value for SOFA scores. A p-value below 0.05 indicates statistical significance (Shreffler & Huecker, 2023).

Summary

In conclusion, it is crucial to highlight the main sections of this presentation. The problem necessitating research is the early identification of sepsis to improve patient outcomes. Early identification is essential for timely response to prevent the severe impact of sepsis. A quantitative study best suits the research problem to provide numerical data that can be analyzed statistically and objectively. Kolcaba’s theory of comfort aligns with the study’s objectives since it stresses the importance of a caring, comfortable environment for the patient through pain management and interventions that alleviate emotional and physical stress.

As demonstrated through the critical analysis of the quantitative study, nursing literature supports early identification of sepsis to reduce mortality rate, organ failure problems, and length of stay in hospitals.

References

• Adams, R., Henry, K. E., Sridharan, A., Soleimani, H., Zhan, A., Rawat, N., … & Saria, S. (2022). Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis. Nature Medicine, 28(7), 1455-1460. https //doi.org/10.1038/s41591-022-01894-0
• Ahmed, I., & Ishtiaq, S. (2021). Reliability and validity Importance in medical research. JPMA. The Journal of the Pakistan Medical Association, 71(10), 2401–2406. https //doi.org/10.47391/JPMA.06-861
• Berger, V. W., Bour, L. J., Carter, K., Chipman, J. J., Everett, C. C., Heussen, N., … & Randomization Innovative Design Scientific Working Group Robert A Beckman. (2021). A roadmap to using randomization in clinical trials. BMC Medical Research Methodology, 21, 1-24. https //doi.org/10.1186/s12874-021-01303-z
• Burdick, H., Pino, E., Gabel-Comeau, D., McCoy, A., Gu, C., Roberts, J., … & Das, R. (2020). Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals. BMJ health & care informatics, 27(1) e100109. https //doi.org/10.1136/bmjhci-2019-100109
• Grove, S. K. & Gray, J. R. (2022). Understanding nursing research Building an evidence-based practice. Elsevier Health Sciences.
• Lin, Y., Zhou, Y., & Chen, C. (2023). Interventions and practices using Comfort Theory of Kolcaba to promote adults’ comfort an evidence and gap map protocol of international effectiveness studies. Systematic Reviews, 12(1), 33. https //doi.org/10.1186/s13643-023-02202-8
• Persson, I., Macura, A., Becedas, D., & Sjövall, F. (2024). Early prediction of sepsis in intensive care patients using the machine learning algorithm NAVOY® Sepsis, a prospective randomized clinical validation study. Journal of Critical Care, 80, 154400. https //doi.org/10.1016/j.jcrc.2023.154400
• Sharma, S. (2022). Nursing research and statistics- e-book. Elsevier Health Sciences.
• Shreffler, J. & Huecker, M. R. (2023). Hypothesis testing, p value, confidence intervals, and significance. National Library of Medicine.