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NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics

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    NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics

    Student Name

    Capella University

    NURS-FPX 6414 Advancing Health Care Through Data Mining

    Prof. Name

    Date

    Tool Kit for Bioinformatics

    The heightened concerns regarding health security following the emergence of the COVID-19 virus, particularly among individuals who visited hospitals during the outbreak and were anxious about potential virus transmission in the hospital environment, have been well-documented (Wu et al., 2020). Addressing such concerns requires early measures, such as the rapid identification and treatment of COVID-19 infections, to enhance people’s sense of security. This objective can be effectively realized through the utilization of Health Information Technology, including Clinical Decision Support Systems (CDSS) and Best Practice Advisory (BPA) alerts (Wu et al., 2020). Consequently, this paper presents a toolkit for the implementation of CDSS and BPA alerts.

    Evidence-Based Policy

    The escalating burden of the COVID-19 pandemic has significantly increased the workload for healthcare workers and inflated healthcare costs, posing challenges for patients, care providers, and health systems (Moulaei, 2022). To address these challenges, it is crucial for healthcare providers to closely monitor early signs of COVID-19 infection. Optimizing the use of CDSS can assist physicians in making well-informed decisions about patient diagnoses, treatments, and follow-ups, thereby leading to quicker and more accurate diagnoses and effective outbreak containment (Moulaei, 2022).

    In the realm of health information technology, especially within the medical field, the delivery of high-quality and timely treatment has been streamlined. The Affordable Care Act mandates healthcare providers to adopt and fully utilize health information technology to enhance quality, patient outcomes, and reduce healthcare costs (Fry, 2021). A fully developed electronic health record (EHR) with clinical decision support (CDS) is essential for a learning health system capable of navigating the complex healthcare landscape. Integrated clinical decision support technologies within electronic health records, such as Best Practice Advisory (BPA) alerts, enhance clinical decision-making by providing relevant information to clinicians (Fry, 2021).

    Guidelines

    Effective policy implementation necessitates the support of key stakeholders, emphasizing the importance of communicating guiding principles, norms, and policies to the entire healthcare workforce (Akhloufi et al., 2022). Regular meetings involving physicians, nurses, hospital administrators, nurse informaticists, and information technology specialists are essential for developing an efficient CDSS and BPA alert system. These meetings aim to improve the technology’s user-friendliness, minimize errors during its use, and provide training on efficient technology usage (Akhloufi et al., 2022).

    Following meetings and training sessions, the implementation planning can commence, with the development team defining project goals. Collaboration with system vendors is crucial for integrating technology effectively to achieve these goals (Akhloufi et al., 2022). Vendors may introduce a beta version or minimum viable product for healthcare organizations to test and provide feedback, leading to system adjustments tailored to the needs of patients and healthcare professionals (Akhloufi et al., 2022).

    Practical Recommendations

    Stakeholders Education

    Successful technology implementation necessitates the buy-in of all relevant stakeholders. Healthcare organizations can educate their staff on maximizing technology potential through weekly training sessions, seminars, and webinars while addressing staff concerns (Lukowski et al., 2020). Studies have demonstrated the benefits of classroom-based team training interventions and simulation for assessing technical competence and addressing training gaps in healthcare technology use (Bienstock & Heuer, 2022).

    Monitor Data to Evaluate Outcomes

    After successfully implementing the CDSS and Best Practice Advisory (BPA) alert systems, it is crucial to evaluate their impact on COVID-19 patient outcomes. The CDSS system’s potential to enhance health outcomes through rapid and accurate disease detection can reduce its spread, lower healthcare costs, and increase patient safety (Karthikeyan et al., 2021).

    Saegerman et al. (2021) demonstrated that the CDSS system facilitated the rapid identification of COVID-19 patients, aiding triage efforts in understaffed diagnostic labs during the pandemic. This clinical decision support tool plays a crucial role in managing the pandemic (Saegerman et al., 2021).

    A Specific Example of Bioinformatics

    Clinicians can significantly reduce the time required to evaluate patients with COVID-19 symptoms by using a clinical decision support tool for diagnostic assessments (Gavrilov et al., 2021). Effective quarantine of patients with COVID-19 symptoms is essential to prevent further virus spread in healthcare facilities. The CDSS system guides practitioners through a standardized COVID-19 diagnostic workup based on the latest recommendations, streamlining the process (Gavrilov et al., 2021).

    The integration of CDSS systems with Best Practice Advisory (BPA) alerts offers several advantages, including improved patient and staff safety, rapid virus detection, and time-saving benefits (Gavrilov et al., 2021).

    Process

    Before the implementation of the CDSS systemAfter the implementation of the CDSS system
    Time to make an accurate diagnosis of COVID-191-2 days5-6 hours
    Healthcare costs$9500$2000
    Unidentified patients in quarantine10-20 patients5 patients
    False Negative Results7-8 false negative results3-4 false negative results

    Conclusion

    This study explores the feasibility of using CDSS systems in the administration and management of COVID-19. The CDSS system’s ability to swiftly diagnose COVID-19 patients aids healthcare professionals in containing its spread, reducing complications, lowering unnecessary treatment costs, shortening diagnostic procedures, and improving clinical performance and 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

    Fry, C. (2021). Development and evaluation of best practice alerts: Methods to optimize care quality and clinician communication. AACN Advanced Critical Care, 32(4), 468–472. https://doi.org/10.4037/aacnacc2021252

    NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics

    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, 9https://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, 1–22https://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

    NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics

    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 the 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