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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 Toolkit for Bioinformatics in Health Security In light of the COVID-19 virus, heightened concerns about health security have emerged, particularly among individuals who visited hospitals during the outbreak and feared contracting the virus in the healthcare setting (Wu et al., 2020). Rapid identification and treatment of COVID-19 infections are crucial for enhancing people’s sense of security. Health Information Technology, including Clinical Decision Support Systems (CDSS) and Best Practice Advisory (BPA) alerts, plays a vital role in achieving this objective (Wu et al., 2020). This paper aims to provide a comprehensive toolkit for the implementation of CDSS and BPA alerts. Evidence-Based Policy for Healthcare The burden imposed by the COVID-19 pandemic has escalated the workload for healthcare workers and significantly inflated healthcare costs. Failure to control the spread of the illness could lead to substantial challenges for patients, care providers, and health systems due to a shortage of medical professionals and equipment (Moulaei, 2022). Monitoring early signs of COVID-19 infections is essential, and the optimized use of CDSS can assist physicians in making informed decisions, resulting in quicker and more accurate diagnoses and outbreak containment (Moulaei, 2022). In the realm of health information technology, the Affordable Care Act mandates healthcare providers to adopt and fully utilize technology to improve quality, patient outcomes, and reduce healthcare costs (Fry, 2021). A fully developed Electronic Health Record (EHR) with Clinical Decision Support (CDS) is crucial for a learning health system capable of navigating the complex healthcare landscape. Integrated clinical decision support technologies, such as Best Practice Advisory (BPA) alerts, enhance clinical decision-making by providing relevant information to clinicians (Fry, 2021). Guidelines for Effective Policy Implementation Successful policy implementation requires the support of key stakeholders. Communicating guiding principles, norms, and policies to the entire healthcare workforce is essential (Akhloufi et al., 2022). Regular meetings involving physicians, nurses, hospital administrators, nurse informaticists, and information technology specialists should be conducted to develop an efficient CDSS and BPA alert system. These meetings aim to improve technology user-friendliness, minimize errors, and provide training on efficient technology usage (Akhloufi et al., 2022). Following meetings and training sessions, the implementation planning may commence, with the development team defining project goals. Collaboration with system vendors is essential for effective technology integration, with vendors potentially introducing a beta version or minimum viable product for testing and 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 buy-in from all relevant stakeholders. Healthcare organizations can educate their staff on maximizing technology potential through weekly training sessions, seminars, and webinars, while also addressing staff concerns (Lukowski et al., 2020). Classroom-based team training interventions and simulation have been shown to be beneficial for assessing technical competence and addressing training gaps in healthcare technology use (Bienstock & Heuer, 2022). Monitor Data to Evaluate Outcomes After successfully implementing CDSS and BPA alert systems, evaluating their impact on COVID-19 patient outcomes is crucial. The potential of the CDSS system 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 in Action 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 and After CDSS Implementation Metric Before CDSS Implementation After CDSS Implementation Time to make an accurate diagnosis of COVID-19 1-2 days 5-6 hours Healthcare costs $9500 $2000 Unidentified patients in quarantine 10-20 patients 5 patients False Negative Results 7-8 false negatives 3-4 false negatives Conclusion This study explored the feasibility of utilizing CDSS systems in the administration and management of COVID-19. The CDSS system’s ability to swiftly diagnose COVID-19 patients assists 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, 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,