Nurs FPX 8022 Assessment 3 Risk Mitigation Plan

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Student name Capella University NURS-FPX8022 Professor Name Submission Date Risk Mitigation Plan The risk mitigation plan is based on the vulnerabilities that are critical and identified by the safety assurance factors of EHR resilience (SAFER) defines the assessment in Cedars-Sinai Medical Center. The systematic mitigation plans address the high-risk areas, such as artificial intelligence (AI) algorithm validation, alert optimization, employee training, workflow integration, and cybersecurity measures. Evidence-based interventions can prove the decrease in risk frequency to occasional occurrence at the minimal harm to the patient and organization (Deshields et al., 2021). The methodical process delivers safe technology adoption alongside clinical excellence and regulatory adherence during the deployment process of the AI-enhanced decision support. Risk Management Plan Table Analysis and Strategic Framework The table of the detailed risk management plan provided in appendix A tackles five main vulnerabilities identified as a result of the assessment of the SAFER guide at Cedars-Sinai Medical Center. All of the identified risks have evidence-based risk mitigation strategies that can convert a high probability and high harm event into a low impact event with well-organized interventions (Hafeez et al., 2021). The table shows how the lack of proper AI algorithm verification and alert fatigue leads to the development of cybersecurity measures and staff training strategies. The strategic plan means that patient safety will be prioritized during technology implementation, and the overall organizational sustainability and regulatory compliance will be preserved during the process of the deployment of the AI-enhanced clinical decision support. Ethical or Legal Issues The impossibility of reducing the risks inherent in the application of AI can be a gross breach of ethics, including discrimination and prejudice in dealing with patients. Legal consequences: malpractice, ineffective risk management in regulatory measures, and even class lawsuits in the event of patient injury are possible legal consequences of wrong AI forecasts (Al-Dulaimi and Mohammed, 2025). The unaddressed cybersecurity threats expose the health information security to threats whereby violations of the Health Insurance Portability and Accountability Act (HIPAA) regulations and huge financial penalties are incurred (Choi et al., 2023). The issue of professional liability comes up when healthcare practitioners engage in the use of AI systems whose validation is not verified and institutions that may lose accreditation and suffer reputational losses. The second-order implications of ill risk management are much greater than patient safety in short terms up to organizational sustainability and trust of community in long-term. The suggested AI-based clinical decision support technology will include extensive HIPAA compliance strategies in the form of advanced encryption and role-based access control schemes. Multi-factor authentication will help in the security of ensured access to protected health information by authorized personnel within AI algorithms and predictive analytics platforms (Murdoch, 2021). Cybersecurity practices involve regular monitoring of the network, automated systems of threat detection, and frequent penetration test to find the vulnerability in AI infrastructure. The data governance policies refer to the explicit guidelines in the management of machine learning datasets, encompassing the minimum necessary access principles and the audit trail principle. Agreements between businesses and AI vendors comprise definite HIPAA regulations, breach notification, and data retention provisions, which ensure regulatory compliance at every stage of the technology lifecycle. Literature Justification The exhaustive planning of AI algorithms verification processes is justified through the vast amount of evidence that suggests the outstanding importance of systematic testing with regard to healthcare AI estimations. The algorithms are less biased and more correct in the predictions concerning diverse groups of patients as explained by Nazer et al. (2023). The strategic plan of implementation is in line with the best practices established in the successful implementation of AI in the national healthcare systems on a large scale (Esmaeilzadeh, 2024). The present possibility of tracking AI performance in real time removes the written-down problems that are associated with AI wear out over time, which maintains clinical performance and patient safety outcomes. The multi-modal training model reveals adult learning evidence-based principles which supplement technology assimilation and competency development within healthcare settings. The alert mitigation strategies are completely supported by Gani et al. (2025) as it can be demonstrated that prioritization of smart alerts is highly influential in terms of clinician response and safety hazard. Kwok et al. (2025) provide a good clue that intensive staff training programs are directly connected to the improved patient outcomes and reduced implementation-associated errors. All the evidence-based interventions are grounded in the risk factors present in the assessment in the SAFER guides along with the preservation of the cohesion with the existing quality improvement plans in healthcare. Change Management Strategies Introduction of AI-based clinical decision support system should be implemented through a structured change management oriented to the use of the 8-step change model by Kotter. Development of urgency will include introducing the existing Leapfrog C and C grade and patient safety improvement opportunities in Cedars-Sinai to the executive team and the clinical personnel. Finding a guiding coalition involves the creation of multidisciplinary teams that include physicians, nurses, informaticists, and quality improvement specialists to become the champions of AI adoption (Kwok et al., 2025). Establishing a clear vision of better patient outcomes and safety measures will give the stakeholders realistic targets and deliverables of the technological implementation. Effective change management involves special interventions within different populations of stakeholders in the healthcare context of the Cedars-Sinai Medical Center. The resistance to new technology among frontline clinicians should be addressed with the help of hands-on training, peer support systems, and the very evidence of the workflow efficiency improvement. The presenters of business cases in the administrative offices should be provided with a genuinely complicated structure of the attractive aspect of results, risk reduction, and adherence to the regulations (Esmaeilzadeh, 2024). Sustaining change involves the establishment of feedback loops, continuous improvement process and reward positive behavior programs at the adoption and sustainable change stages in the AI implementation life cycle. References for Nurs Fpx assessment 3 https://doi.org/10.1108/ijlma-08-2024-0295 https://doi.org/10.1001/jamanetworkopen.2021.17391 https://doi.org/10.1016/j.ijmedinf.2023.105149 https://doi.org/10.1186/s12871-021-01411-9 https://doi.org/10.3322/caac.21672 https://doi.org/10.1016/j.artmed.2024.102861 https://doi.org/10.1108/omj-03-2020-0893 https://doi.org/10.1186/s12889-025-22846-6 https://doi.org/10.18196/jrc.v5i5.22508 https://doi.org/10.1186/s12910-021-00687-3 https://doi.org/10.1371/journal.pdig.0000278 https://doi.org/10.1007/978-3-031-10525-8_10 FAQ’s for Nurs fpx 8022 Assessment 3 Can I

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