NURS FPX 6414 Tool Kit for Bioinformatics Paper Example

NURS FPX 6414 Tool Kit for Bioinformatics Paper Example

NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics

NURS FPX 6414 Tool Kit for Bioinformatics Assignment Brief

Course: NURS-FPX6414 Advancing Health Care Through Data Mining

Assignment Title: NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics

Assignment Overview

This assessment task focuses on the development of a comprehensive tool kit for implementing bioinformatics in healthcare organizations or practice settings. Bioinformatics, a field intersecting biology and computer science, plays a crucial role in enhancing patient care outcomes through informed decision-making and data analysis. The tool kit will encompass evidence-based policies, guidelines, and practical recommendations to facilitate the effective integration of bioinformatics into healthcare practices.

Understanding Assignment Objectives

The primary objective of this assignment is to demonstrate proficiency in applying data management techniques, querying health information system databases, and articulating strategies for the responsible and accountable use of data in nursing practice. By assembling a tool kit for bioinformatics implementation, students will showcase their ability to evaluate evidence-based policies, guidelines, and recommendations, as well as communicate professionally through the executive summary.

The Student’s Role

As a nursing student, your role is to act as a healthcare leader tasked with creating structured policies, guidelines, and recommendations for the implementation of bioinformatics in healthcare settings. Your responsibilities include conducting research, analyzing data, and synthesizing information to develop a comprehensive tool kit that addresses the challenges and opportunities associated with bioinformatics integration.

Competencies Measured

This assessment measures various competencies essential for nursing professionals, including:

• Apply data management techniques: Evaluate evidence-based policies, guidelines, and practical recommendations for the implementation of bioinformatics. Apply specific examples of bioinformatics implementation to inform and plan for quality outcomes in care delivery.
• Articulate strategies for querying and generating reports: Analyze the legal and ethical ramifications of using bioinformatics in practice. Incorporate responsible and accountable use of data within bioinformatics.
• Communicate as a practitioner-scholar: Compose a professionally written executive summary that explains the policy, guidelines, and implementation recommendations in the context of a specific organizational example.

You Can Also Check Other Related Assessments for the NURS-FPX6414 Advancing Health Care Through Data Mining Course:

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NURS FPX 6414 Tool Kit for Bioinformatics Paper Example

Tool Kit for Bioinformatics Implementation

In response to the challenges posed by the COVID-19 pandemic, the healthcare sector has increasingly turned to Health Information Technology (HIT) to enhance patient care, streamline processes, and mitigate risks (Wu et al., 2020). One crucial aspect of HIT is the utilization of Clinical Decision Support Systems (CDSS) and Best Practice Advisory (BPA) alerts, which can significantly improve diagnostic accuracy, treatment efficiency, and resource allocation. This paper aims at presenting a comprehensive tool kit for the effective implementation of CDSS and BPA alerts in healthcare settings, thereby addressing critical needs for enhanced patient care and risk mitigation.

Evidence-Based Policy

During the recent pandemic, healthcare workers faced heightened workloads and rising costs, presenting significant challenges for patients, care providers, and healthcare systems due to shortages in staff and equipment (Moulaei, 2022). Advocating for vigilant monitoring of early signs of COVID-19 infection, Moulaei emphasizes the need to treat and prevent its spread effectively. By optimizing Clinical Decision Support (CDS) systems, physicians can expedite and enhance decision-making regarding patient diagnoses, treatments, and follow-ups, thereby aiding in outbreak control. Through computerized alerts, reminders, patient reports, and clinical trial tools, CDS systems furnish guidance, knowledge, and information to both patients and healthcare professionals (Moulaei, 2022).

Advancements in health information technology have transformed the delivery of timely, high-quality treatment in the medical realm. The Affordable Care Act mandates healthcare providers to fully adopt and utilize health information technology to elevate quality, enhance patient outcomes, and curtail healthcare expenses (Fry, 2021). Emphasizing the necessity of a learning health system amidst the intricate healthcare landscape, Fry highlights the integration of a fully developed Electronic Health Record (EHR) with Clinical Decision Support (CDS). Embedded within EHRs, various integrated clinical decision-support technologies equip clinicians with pertinent information to bolster clinical decision-making. The EHR’s integrated CDS tool, the Best Practice Advisory (BPA) alert, empowers clinicians to amplify patient outcomes and operational efficiencies (Fry, 2021).

To effectively combat the COVID-19 pandemic and optimize patient outcomes, healthcare organizations must enact evidence-based policies that harness CDSS and BPA alerts. Stressing the importance of promptly identifying and treating COVID-19 cases, Moulaei advocates for measures to prevent further spread and alleviate strain on healthcare resources. Integration of CDSS into clinical workflows enables healthcare providers to make informed decisions regarding patient management, fostering improved outcomes and diminished transmission rates (Moulaei, 2022).

Guidelines

To successfully implement policies, it’s not enough to just have them written down; they need to be put into action with the support of key stakeholders. It’s crucial to establish and communicate the guiding principles, norms, and policies to the entire healthcare team (Akhloufi et al., 2022). Weekly meetings involving physicians, nurses, hospital administrators, nurse informaticists, and IT specialists are vital for developing an effective Clinical Decision Support (CDS) system and Best Practice Advisory (BPA) alerts. During these meetings, the team collaborates on enhancing the technology by adding user-friendly features and minimizing potential errors. Moreover, these sessions offer training to ensure efficient utilization of the technology (Akhloufi et al., 2022).

Once meetings and training are completed, the planning phase for implementation can commence, with the development team outlining the project’s objectives and goals. Subsequently, the team collaborates with system vendors to determine the best way to integrate the technology to meet these objectives (Akhloufi et al., 2022). Vendors typically introduce a beta version or minimum viable product, allowing healthcare organizations to test and provide feedback. Based on this feedback, vendors can refine the system to better meet the needs of healthcare teams. A tailored CDS system that addresses the requirements of both patients and healthcare professionals is crucial for achieving improved health outcomes (Akhloufi et al., 2022).

Practical Recommendations

Educating Stakeholders

For successful implementation of technology, it’s crucial to get everyone on board. Once healthcare organizations outline their goals with the new technology, they should focus on educating their staff to maximize its potential. Collaborating with IT teams, healthcare institutions can conduct weekly training sessions, seminars, and webinars to teach professionals how to effectively utilize the technology and address any concerns they may have (Lukowski et al., 2020).

Studies have emphasized the benefits of team training interventions in classrooms and simulation settings. Both traditional classroom training and simulation-based methods help assess professionals’ technical skills and bridge training gaps in utilizing technology in healthcare settings (Bienstock & Heuer, 2022).

Monitoring Data for Outcome Evaluation

After the successful implementation of Clinical Decision Support (CDS) systems and Best Practice Advisory (BPA) alerts, evaluating their impact on COVID-19 patient outcomes becomes essential. The CDS system plays a vital role in improving health outcomes by facilitating rapid and accurate disease detection, thereby reducing its transmission and providing valuable guidance and information through alerts to both patients and clinicians. This improvement in health outcomes could lead to reduced healthcare costs and increased patient safety and confidence, potentially resulting in cost savings for healthcare organizations (Karthikeyan et al., 2021).

Research by Saegerman et al. (2021) demonstrates how the use of CDS systems can expedite the identification of COVID-19 patients. The authors highlight the widespread impact of COVID-19, leading to significant disruptions, acute respiratory failures, and overwhelming emergency department visits amidst understaffed diagnostic labs. In this scenario, the development of clinical decision support systems for real-time diagnosis of COVID-19 has emerged as a crucial tool in effectively managing the pandemic by triaging patients and allocating resources efficiently (Saegerman et al., 2021).

A Specific Example of Bioinformatics Implementation

Using a clinical decision support tool can help clinicians streamline the diagnostic process for patients exhibiting symptoms of COVID-19, potentially reducing assessment time significantly (Gavrilov et al., 2021). Properly identifying and isolating patients with COVID-19 symptoms in healthcare settings is crucial for preventing further transmission of the virus. However, unnecessary isolation can lead to treatment delays, occupy beds needed for other patients, and waste personal protective equipment. With the assistance of a clinical decision support (CDS) system, physicians can efficiently navigate through a standardized diagnostic evaluation for COVID-19 based on the latest recommendations, after answering specific questions about the patient’s risk factors, symptoms, and imaging data (Gavrilov et al., 2021).

Integrating CDS systems with Best Practice Advisory (BPA) alerts offers several benefits, including enhanced patient and staff safety. Research indicates that implementing a CDS system improves the accuracy and speed of virus detection, reducing the risk of false-negative results that could jeopardize patient and healthcare worker safety. Notably, the CDS system saves time during diagnosis and patient quarantine, leading to more efficient healthcare processes (Gavrilov et al., 2021).

 

Process Comparison	Before CDS System Implementation	After CDS System Implementation
Time Required for Accurate COVID-19 Diagnosis	1-2 days	5-6 hours
Healthcare Costs	$9500	$2000
Number of Unidentified Patients in Quarantine	10-20 patients	5 patients
Number of False Negative Results	7-8 false negative results	3-4 false negative results

 

Conclusion

In conclusion, the implementation of CDSS and BPA alerts represents a critical step towards enhancing healthcare delivery, particularly in the context of the COVID-19 pandemic. By adhering to evidence-based policies, establishing clear guidelines, providing practical recommendations, and leveraging specific examples of bioinformatics applications, healthcare organizations can maximize the benefits of HIT tools and improve patient outcomes.

References

Akhloufi, H., van der Sijs, H., Melles, D. C., van der Hoeven, C. P., Vogel, M., Mouton, J. W., & Verbon, A. (2022). The development and implementation of a guideline-based clinical decision support system to improve empirical antibiotic prescribing. BMC Medical Informatics and Decision Making, 22(1). https://doi.org/10.1186/s12911-022-01860-3

Bienstock, J., & Heuer, A. (2022). A review on the evolution of simulation-based training to help build a safer future. Medicine, 101(25), e29503. https://doi.org/10.1097/MD.0000000000029503

Gavrilov, D., Kuznetsova, T., Gusev, A., Korsakov, N., & Novitskiy, R. (2021). Application of a clinical decision support system to assess the severity of the new coronavirus infection COVID-19. European Heart Journal, 42(Supplement_1). https://doi.org/10.1093/eurheartj/ehab724.3054

Karthikeyan, A., Garg, A., Vinod, P. K., & Priyakumar, U. D. (2021). Machine learning-based Clinical Decision Support System for early COVID-19 mortality prediction. Frontiers in Public Health, 9. https://doi.org/10.3389/fpubh.2021.626697

Lukowski, F., Baum, M., & Mohr, S. (2020). Technology, tasks and training – Evidence on the provision of employer-provided training in times of technological change in Germany. Studies in Continuing Education, 1–22. https://doi.org/10.1080/0158037x.2020.1759525

Moulaei, K. (2022). Diagnosing, managing, and controlling COVID-19 using Clinical Decision Support systems: A study to introduce CDSS applications. Journal of Biomedical Physics and Engineering, 12(02). https://doi.org/10.31661/jbpe.v0i0.2105-1336

Saegerman, C., Gilbert, A., Donneau, A.-F., Gangolf, M., Diep, A. N., Meex, C., Bontems, S., Hayette, M.-P., D’Orio, V., & Ghuysen, A. (2021). Clinical decision support tool for diagnosis of COVID-19 in hospitals. PLOS ONE, 16(3), e0247773. https://doi.org/10.1371/journal.pone.0247773

Wu, G., Yang, P., Xie, Y., Woodruff, H. C., Rao, X., Guiot, J., Frix, A.-N., Louis, R., Moutschen, M., Li, J., Li, J., Yan, C., Du, D., Zhao, S., Ding, Y., Liu, B., Sun, W., Albarello, F., D’Abramo, A., & Schininà, V. (2020). Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study. European Respiratory Journal, 56(2). https://doi.org/10.1183/13993003.01104-2020

Detailed Assessment Instructions for the NURS FPX 6414 Tool Kit for Bioinformatics Paper Assignment

Description

Assessment 3 Instructions: Tool Kit for Bioinformatics

Assemble a 3-5 page tool kit for the implementation of bioinformatics in an organization or practice setting. Then, provide a one-page executive summary describing a specific instance of how bioinformatics might be implemented under the tool kit policies and guidelines.

INTRODUCTION

This assessment focuses on how leaders create structure, guidance, and clarity when faced with adversity and choice for health care delivery. Best practices are important for helping organizations to assess, monitor, and use bioinformatics to enhance outcomes for patient care. Bioinformatics best practices are disseminated through the use of policy, guidelines, and practical recommendations. Using these tools leads to organized, collaborative, and accountable decision making.

TOOL KIT

Use the professional literature, the Internet, and any other resources you locate, to assemble a tool kit for implementing bioinformatics in an organization. Your tool kit should include:

• An evidence-based policy that explains what is to be done and why.
• Guidelines detailing how to apply the policy in practice.
• Practical recommendations to assist in implementing the use of bioinformatics.
  • How to how educate stakeholders on this new practice.
  • When to monitor data to evaluate outcomes on the use of the policy.
• An in-depth look at a specific example of bioinformatics, demonstrating how the policy, guidelines and recommendations will result in quality outcomes with care delivery.
  • Include data in the form of actual data tables to demonstrate the responsible and accountable use of data in practice.

Support your policy, guidelines, and recommendations with references that speak to the legal and ethical ramifications of data use in bioinformatics and the implications for responsible and accountable use of data in practice.

EXECUTIVE SUMMARY

Using a specific example, write a one-page executive summary for administration to explain how the policy, guidelines, and recommendations will govern the use of bioinformatics in the organization or practice setting.

ADDITIONAL REQUIREMENTS

• Tool Kit:
  • Length: 3–5 pages.
  • Font and font size: Times New Roman, 12 point.
  • Reference: 5–7 scholarly sources. Additional references may be used.
  • Written communication: Written communication is free of errors that detract from the overall message.
  • APA formatting: Format your tool kit using APA style. Use the APA Style Paper Template [DOCX] to format your tool kit. Be sure to include the following:
    • Appropriate section headings.
    • A running head on all pages.
    • A title page and references page.
• Executive Summary:
  • Length of Executive Summary: 250 words.

COMPETENCIES MEASURED

By successfully completing this assessment, you will demonstrate your proficiency in the following course competencies and assessment criteria:

• Competency 1: Apply data management techniques to decision making in nursing practice.
  • Evaluate evidence-based policy, guidelines, and practical recommendations for the implementation of bioinformatics in an organization or practice setting.
  • Apply a specific example of an implementation of bioinformatics to inform and plan for quality outcomes with care delivery.
• Competency 3: Articulate strategies for querying and generating reports from health information system databases.
  • Analyze the legal and ethical ramification of using bioinformatics in practice.
  • Incorporate responsible and accountable use of data with bioinformatics.
• Competency 6: Communicate as a practitioner-scholar, consistent with the expectations of a nursing professional.
  • Compose an executive summary that is professionally written and explains the policy, guidelines, and implementation recommendations in the context of a specific organizational example.

Resources: Bioinformatics

• McGonigle, D., & Mastrian, K. (2018). Nursing informatics and the foundation of knowledge (4th ed.). Jones & Bartlett. Available in the courseroom via the VitalSource Bookshelf link. 

• Chapter 24, “Bioinformatics, Biomedical Informatics, and Computational Biology,” pages 511–519.

• Kodra, Y., Weinbach, J., Posada-de-la-Paz, M., Coi, A., Lemonnier, S. L., van Enckevort, D., Roos, M., Jacobsen, A., Cornet, R.,  Faisal Ahmed, S., Bros-Facer, V., Popa, V., Van Meel M., Renault, D., von Gizycki, R, Santoro, M., Landais, P., Torreri, P., Carta, C., . . . Taruscio, D. (2018). Recommendations for improving the quality of rare disease registries. International Journal of Environmental Research and Public Health, 15(8), 1–22.
• Krempel, R., Kulkarni, P., Yim, A., Lang, U., Habermann, B., &Frommolt, P. (2018). Integrative analysis and machine learning on cancer genomics data using the Cancer Systems Biology Database (CancerSysDB). BMC Bioinformatics, 19(1), 1–10.
• Regan, M., Engler, M. B., Coleman, B., Daack‐Hirsch, S., & Calzone, K. A. (2019). Establishing the genomic knowledge matrix for nursing science. Journal of Nursing Scholarship, 51(1), 50–57.
• Yang, J., Zhang, S., Zhang, J., Dong, J., Wu, J., Zhang, L., Guo, P., Tang, S., Zhao, Z., Wang, H., Zhao, Y., Zhang, W., and  Wu, F. (2018). Identification of key genes and pathways using bioinformatics analysis in septic shock children. Infection and Drug Resistance, 11, 1163–1174.
• Zhang, K., Kong, X., Feng, G., Xiang, W., Chen, L., Yang, F., Cao, C., Ding, Y., Chen, H., Chu, M., Wang, P., & Zhang, B. (2018). Investigation of hypoxia networks in ovarian cancer via bioinformatics analysis. Journal of Ovarian Research, 11(16), 1–11.

Resources: Understanding Implications in Practice

• McGonigle, D., & Mastrian, K. (2018). Nursing informatics and the foundation of knowledge (4th ed.). Jones & Bartlett. Available in the courseroom via the VitalSource Bookshelf link. 

• Chapter 25, “The Art of Caring in Technology-Laden Environments,” pages 524–535.

• David, H. B. F., &Belcy, S. A. (2018). Heart disease prediction using data mining techniques. ICTACT Journal on Soft Computing, 9(1), 1817–1823.
• El aboudi, N., &Benhlima, L. (2018). Big data management for healthcare systems: Architecture, requirements, and implementation. Advances in Bioinformatics, 2018, 1–10.
• Hoyle, P. (2019). Health information is central to changes in healthcare: A clinician’s view. Health Information Management Journal, 48(1), 48–51.

Resources: Responsibility and Accountability in PracticeTop of FormBottom of Form

• Favaretto, M., de Clercq, E., &Elger, B. S. (2019). Big data and discrimination: Perils, promises and solutions. A systematic review. Journal of Big Data, 6(1), 1–27.
• Gurgen Erdogan, T., &Tarhan, A. (2018). A goal-driven evaluation method based on process mining for healthcare processes. Applied Sciences, 8(6), 1–22.
• Ienca, M., Ferretti, A., Hurst, S., Puhan, M., Lovis, C., &Vayena, E. (2018). Considerations for ethics review of big data health research: A scoping review. PloS One, 13(10), 1–15.
• Seyhan, A. A., &Carini, C. (2019). Are innovation and new technologies in precision medicine paving a new era in patients centric care? Journal of Translational Medicine, 17(114), 1–28.
• Shepheard, J. (2019). Ethical leadership and why health information management professionals need to be involved. Commentary on Health Information Is Central to Changes in Healthcare: A Clinician’s View (Hoyle, 2019). Health Information Management Journal, 48(1), 52–55.

Resources: Scope and Standards of Practice

• American Nurses Association. (2015). Nursing informatics: Scope and standards of practice (2nd ed.). Silver Spring, MD: Author.

Resources: Writing and Research Resources

• PRINT
  • APA Style Paper Tutorial [DOCX].
  • MSN Program Library Research Guide.

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