NURS FPX 8022 Assessment 3 Risk Mitigation Plan
Student name
Capella University
NURS-FPX8022 Nursing Technology and Advanced Healthcare Information Systems
Professor Name
Submission Date
Risk Mitigation Plan
The risk mitigation plan uses the safety assurance factors of EHR resilience (SAFER) as a guide to the evaluation of the Cedars-Sinai Medical Center. The systematic mitigation plans are aimed at the high-risk zones, such as the validation of artificial intelligence (AI) algorithms, optimization of alerts, staff education, workflow optimization and cybersecurity measures. Evidence-based interventions prove the reduction of the risk of frequent to occasional occurrence with the minimum of possible harm to patients and organizations (Deshields et al., 2021). The systematic approach will guarantee safe technology implementation, clinical excellence, and regulatory adherence in the AI-enhanced decision support implementation process.
The total risk management plan table in Appendix A provides a critical vulnerability systematic approach to five vulnerabilities that have been determined using the SAFER guides assessment within Cedars-Sinai Medical Center. Both of the identified risks show evidence-based mitigation measures that can bring the high-probability, high-harm cases to the lower-impact, low-probability events by promoting structured interventions (Hafeez et al., 2021). The table shows how it can work its way up to insufficient AI algorithm validation and alert fatigue in cybersecurity measures and staff training systems. The methodological approach will fulfill the role of making sure that the implementation of technology is patient-focused, and at the same time, organizational resilience and adherence to regulatory requirements are not compromised in the AI-enhanced clinical decision support implementation process.
Ethical or Legal Issues
The failure to resolve the threats present in the implementation of AI can lead to severe ethical breaches, including bias in the algorithms and discriminatory treatment of patients. Legal consideration: inaccurate AI predictions present a potential malpractice, insufficient risk control in the regulatory proceedings, and even a lawsuit in a class action because of patient harm (Al-Dulaimi and Mohammed, 2025). The lack of cybersecurity protections exposes the health information to attacks, a factor that results in the Health Insurance Portability and Accountability Act (HIPAA) and imposes colossal financial penalties (Choi et al., 2023). The issues related to professional liability in healthcare professionals when utilizing non-validated AI systems and losing accreditation and reputation among the institutions. The second-order effects of the lack of risk control go way beyond the short-term patient safety to the long-term organizational survival and the trust of the community.
The suggested AI-enhanced clinical decision support technology will feature extensive HIPAA compliance procedures that will be implemented by the advanced encryption protocols and role-based access control. The multi-factor authentication on AI algorithms and predictive analytics platforms is implemented to guarantee that only authorized personnel have access to the protected health information (Murdoch, 2021). The measures that are taken on cybersecurity include constant monitoring of the network, automatic detection of threats, and periodic penetration tests to detect vulnerabilities in AI infrastructure. Data governance policies provide well-defined guidelines concerning how machine learning datasets are handled, and they give the minimum necessary principles of access and audit trail requirements. There are special HIPAA compliance stipulations, breach notification practices and data retention policies in business associate agreements with AI vendors that ensure compliance with regulations throughout the technology lifecycle.
Literature Justification
The comprehensive construction of verification procedures involving AI algorithms has been supported with a tremendous amount of evidence, which suggests the unparalleled importance of systematic testing in the specifics of healthcare AI estimations. The validation processes, as Nazer et al. (2023) emphasize, reduce the bias and increase the accuracy of the algorithms in the predictions concerning different population groups of patients. The planned implementation approach adheres to the best practices established on the successful application of AI to large-scale healthcare systems in the country (Esmaeilzadeh, 2024). The existing capability to track AI functionality in real time eradicates documented problems associated with AI wear out with time, maintaining clinical performance and patient safety results.
The evidence-based principles of adult learning demonstrated by the multi-modal training framework help to supplement technology assimilation and competency improvement in healthcare settings. The measures of the mitigation of alert fatigue are fully supported by Gani et al. (2025), and the priority of smart alerts has a strong influence on the rates of clinical responses and risks of safety. A strong hint that Kwok et al. (2025) introduce is that the successful implementation of intensive staff training programs is directly associated with the improvement of patient outcomes and the reduced number of errors in the implementation. All the evidence-based interventions are grounded on the underlying risk factors identified within the SAFER guides assessment, as well as the continuity with the existing strategies of healthcare quality improvement.
Change Management Strategies
Implementation of AI-enhanced clinical decision support needs to be a structured change management approach with the use of Kotters 8-step model of change. The establishment of urgency entails bringing forth the current opportunities of the Leapfrog C grade and patient safety improvement opportunities at Cedars-Sinai to the executive leadership and clinical personnel. The development of the guiding coalition involves inclusion of multidisciplinary teams that comprise physicians, nurses, informaticists and experts in quality improvement to lead the way in adopting AI (Kwok et al., 2025). The formation of a clear vision of better patient outcomes and safety measures will give the stakeholders a concrete goal and the benefits anticipated due to the implementation of the technology.
The healthcare setting at the Cedars-Sinai Medical Center needs to make designated interventions in different populations of stakeholders to manage the change. Resistance to new technology by frontline clinicians needs to be dealt with through hands-on education, peer support systems, and the real evidence of workflow efficiency improvement. The presentations of business cases about the appealing benefit of the outcomes, risk reduction, and regulation adherence are really complicated as they should be provided to administrative executives (Esmaeilzadeh, 2024). Change maintenance also involves establishing feedback loops, a continuous improvement process, and programs that reinforce positive behaviors at the phases of change adoption and sustainability in the lifecycle of the AI implementation.
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References for
NURS FPX 8022 Assessment 3
Al-Dulaimi, A. O. M., & Mohammed, M. A.-A. W. (2025). Legal responsibility for errors caused by artificial intelligence (AI) in the public sector. International Journal of Law and Management. https://doi.org/10.1108/ijlma-08-2024-0295
Chi, E. A., Chi, G., Tsui, C. T., Jiang, Y., Jarr, K., Kulkarni, C. V., Zhang, M., Long, J., Ng, A. Y., Rajpurkar, P., & Sinha, S. R. (2021). Journal of American Medical Association Network Open, 4(7), e2117391. https://doi.org/10.1001/jamanetworkopen.2021.17391
Choi, S. J., Chen, M., & Tan, X. (2023). Assessing the impact of health information exchange on hospital data breach risk. International Journal of Medical Informatics, 177, e105149. https://doi.org/10.1016/j.ijmedinf.2023.105149
Colquhoun, D. A., Davis, R. P., Tremper, T. T., Mace, J. J., Gombert, J. M., Sheldon, W. D., Connolly, J. J., Adams, J. F., & Tremper, K. K. (2021). BioMed Central Anesthesiology, 21(1), e196. https://doi.org/10.1186/s12871-021-01411-9
Esmaeilzadeh, P. (2024). Artificial Intelligence in Medicine, 151(1), e102861. https://doi.org/10.1016/j.artmed.2024.102861
NURS FPX 8022 Assessment 3 Risk Mitigation Plan
Journal of Medical Internet Research, 27, e62763. https://doi.org/10.2196/62763
Hafeez, H., Abdullah, M. I., Zaheer, M. A., & Ahsan, Q. (2021). Organization Management Journal, 19(1), 2-21. https://doi.org/10.1108/omj-03-2020-0893
Kwok, W. Y. Y., Wong, F. K. Y., Wong, A. K. C., & Bayuo, J. (2025). BioMed Central Public Health, 25(1), e1678. https://doi.org/10.1186/s12889-025-22846-6
Mahmood, R. K., Mahameed, A. I., Lateef, N. Q., Jasim, H. M., Radhi, A. D., Ahmed, S. R., & Waghmare, P. T. (2024). Optimizing network security with machine learning and multi-factor authentication for enhanced intrusion detection. Journal of Robotics and Control (JRC), 5(5), 1502–1524. https://doi.org/10.18196/jrc.v5i5.22508
BioMed Central Medical Ethics, 22(1), e122. https://doi.org/10.1186/s12910-021-00687-3
Bias in artificial intelligence algorithms and recommendations for mitigation. Public Library of Science Digital Health, 2(6), e0000278. https://doi.org/10.1371/journal.pdig.0000278
Appendices for
NURS FPX 8022 Assessment 3
Appendix A: Risk Mitigation Plan
SAFER Guides Risk Mitigation Plan – Cedars-Sinai Medical Center
Risk Identified by SAFER Guides | Possibility of Occurrence (Frequent, Sometimes, Never) | Potential for Harm (Severe, Mild, None) | Mitigation to Address Risks | Possibility of Occurrence (Frequent, Sometimes, Never) | Potential for Harm (Severe, Mild, None) |
Inadequate AI Algorithm Validation and Testing | Frequent | Patient – Severe; Organization – Severe | Pre-Implementation Actions: Establish comprehensive AI algorithm validation protocols, including retrospective testing on historical patient data, prospective validation studies with clinical outcomes tracking, and multi-site validation across diverse patient populations (Chi et al., 2021). Implementation Strategy: Deploy phased rollout beginning with low-risk clinical scenarios, implement real-time algorithm performance monitoring with automated alerts for prediction accuracy below 85%, and establish a multidisciplinary AI oversight committee including clinicians, informaticists, and quality improvement specialists. | Sometimes | Patient – Mild; Organization – Mild |
Alert Fatigue from Enhanced Decision Support | Frequent | Patient – Severe; Organization – Mild | Alert Optimization Strategy: Implement intelligent alert prioritization using clinical severity scoring, contextual filtering based on patient acuity and provider role, and machine learning-driven alert customization (Colquhoun et al., 2021). Clinical Integration: Establish an alert governance committee to review and approve all new alerts, implement user-specific alert preferences based on specialties, and create alert effectiveness metrics tracking acceptance rates and clinical outcomes. | Sometimes | Patient – Mild; Organization – Mild |
Insufficient Staff Training on Advanced AI Features | Sometimes | Patient – Mild; Organization – Severe | Comprehensive Training Program: Develop multi-modal training approach including interactive e-learning modules, hands-on simulation exercises, and competency-based assessments for AI-enhanced clinical decision support (Sanfilippo et al., 2022). Competency Framework: Establish role-specific competency requirements for physicians, nurses, and support staff, implement mandatory certification processes before system access, and create ongoing education requirements with annual recertification. | Never | Patient – None; Organization – Mild |
Workflow Disruption During AI Implementation | Frequent | Patient – Mild; Organization – Severe | Change Management Strategy: Implement comprehensive workflow redesign using lean methodology principles, conduct detailed workflow analysis pre- and post-implementation, and establish dedicated workflow optimization teams for each clinical unit (Hafeez et al., 2021). Phased Implementation: Deploy gradual rollout across clinical units starting with early adopter departments, implement parallel processing during transition periods, and establish rollback procedures for critical workflow failures. | Sometimes | Patient – None; Organization – Mild |
Data Security Vulnerabilities in AI Systems | Sometimes | Patient – Severe; Organization – Severe | Cybersecurity Enhancement: Implement advanced encryption protocols for AI data transmission, establish multi-factor authentication for AI system access, and deploy continuous network monitoring with automated threat detection (Mahmood et al., 2024). Access Control Management: Create role-based access controls specific to AI algorithms, implement audit logging for all AI system interactions, and establish regular access review processes with quarterly permission updates. | Sometimes | Patient – Mild; Organization – Mild |
Capella Professors to choose from for
NURS-FPX8022
JacQualine Abbe.
Sean Forster.
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