NURS FPX 8022 Assessment 3 Sample FREE DOWNLOAD

NURS FPX 8022 Assessment 3

Risk Mitigation Plan

Student Name

NURS-FPX8022

Capella University

Professor Name

Submission Date 

Introduction

CDSS is a critical element in enhancing decision-making, reducing medication errors, and improving patient outcomes with real-time, evidence-based support alerts. In spite of the advantages of CDSS, the technology has a number of risks (Laka et al., 2024). The risk mitigation plan is able to identify ways of mitigating the key risks that were identified during the proposed integration of CDSS with the existing barcode medication administration (BCMA) technology at St. Francis Health Services using the safety assurance factors of EHR resilience (SAFER) Guides. The evaluation categorizes every risk according to its likelihood of happening and the injury it will cause, with targeted measures to facilitate patient safety, minimize clinical errors, and facilitate successful technology implementation.

Risk Mitigation Plan

Risk assessment of the integration of the CDSS with the BCMA system at St. Francis Health Services determines a number of areas of risk. The failure to offer real-time clinical alerts and embedded decision-support logic is one of the most significant risks that sometimes can lead to the gravest harm. The gap limits the system to recognize the high-risk medication interactions and fall-related alerts that cannot be identified within a short time, which results in adverse patient outcomes (Cai et al., 2024). To this end, the plan focuses on the introduction of timely and accurate alerts based on patient-specific data to enhance clinical decision-making and prevent errors at the point of care (Chaparro et al., 2022). The plan will assist in curbing the risk of medication errors.

Alert fatigue in nursing staff is another widely-spread risk that, despite being of a minor nature on average, poses a threat to the performance of CDSS by increasing the chances of major alerts being ignored or suppressed. Nurses are currently not properly trained to address various degrees of clinical alerts and should be educated and provided with context-sensitive alerting mechanisms to strike the right balance between usability and safety (Chaparro et al., 2022). Also, there are some reconciliation issues with data that can be very harmful when not synchronized with administration, medication orders, and actual administration (Cai et al., 2024).

The weakness is being addressed by incorporating automated cross-verification capabilities in the CDSS to ensure quality of the data and reduce medication errors (Laka et al., 2024). All the suggested countermeasures should create a safer and more efficient system that will support medication safety, enhance workflow, and align with key healthcare quality metrics.

Ethical or Legal Issues

The inability to act in an appropriate manner towards the identified risks of applying the CDSS in the St. Francis Health Services would cause the organization to face serious ethical and legal issues. At an ethical level, the safety of patients is the top priority, and the negligence of the risks such as the lack of real-time alerts or data reconciliation errors may lead to medication errors, adverse drug events, or treatment delays (Rasool et al., 2020). The issue leads to the violation of the ethical principles of beneficence and non-maleficence since patients may be harmed as a result of avoidable system failure (Vemuri et al., 2022).

The possible consequence of poor alert training is the alert fatigue, which can result in the clinician forgetting that these alerts are life-saving and with time becoming disillusioned with the technology and experiencing moral distress or professional consequences. The outcomes of the violation are the absence of trust in the health care system and worse treatment and health outcomes of the patients (Vemuri et al., 2022). Institutions will also be subjected to regulatory fines, loss of reputation and possible legal consequences of failing to comply with patient safety and information protection standards.

Patient injuries or medication errors due to the inadequately designed system safeguards may endanger the institution to suffer malpractice lawsuits, regulatory penalties, and reputation loss. The standards require that healthcare organizations must ensure a safe care environment, and failing to ensure this with technology may contravene the standards (Miziara & Miziara, 2022). Besides, mishandling of patient data also results in a violation of the health insurance portability and accountability act (HIPAA), since the act demands strict measures to ensure the confidentiality, data integrity, and access controls of the patient data (Edemekong et al., 2024). The consequences of violating HIPAA may be severe to the organization.

The planned CDSS integration at St. Francis would be compliant with the HIPAA requirements because the plan envisages the application of secure authentication methods, data-in-transit encryption, and access control to the sensitive health information based on the roles of personnel. Cybersecurity systems can consist of regular system auditing, intrusion detection systems, proper network design, and contingency plans in case of data security or system failure. The safeguards ensure patient confidentiality and prevent data loss and alteration to the clinical data that are critical to ethical and legal considerations (Cremer et al., 2022). By making proactive moves on the risks before they occur, the institution protects not only the patients but also clinicians, organizational trust, and even regulatory compliance.

Literature Justifications

The measures that have been suggested to address the risks posed during the implementation of the integration of the clinical decision support system with the barcode medication administration at St. Francis Health Services are sensible since they are backed by evidence and best practices in health informatics and patient safety. Applying real-time, patient-specific warnings is critical to the improvement of medication safety since timely decision support has been confirmed to reduce adverse drug events and improve clinical outcomes (Chaparro et al., 2022). The automated cross-verification tools used to reconcile data will eliminate a well-documented cause of medication error by increasing consistency and accuracy in the medication orders and administration, as well as the patient charts (Laka et al., 2024). The strategy will eventually decrease the possibility of discrepancies.

The problem of alert fatigue is tackled by evidence-based, individualized alerts and comprehensive training based on the evidence that an excessive number or non-specificity of alerts leads to desensitization of clinicians, undermining the quality of clinical decision support (Chaparro et al., 2022). The personalisation of alert limits and clinician training have also helped to increase adherence to alerts and minimise overrides, leading to safer medication practices (Syrowatka et al., 2024). Moreover, the extensive training of the nursing personnel allows them to perform alerts adequately and address them, avoiding the danger of cognitive overload and enhancing patient care.

The mitigation strategies are oriented toward evidence-based practice that seeks to achieve a balanced degree of clinical decision support, cost-effective patient safety maximization, and the minimization of workflow interruption and cognitive load. The direction to leads to effective adoption and sustainment of technologies in healthcare organizations (Laka et al., 2024). Therefore, the recommended actions are both responding directly to the known risks and are founded on the current academic guidance on the best practices to maximize clinical outcomes and support health workers.

Change Management Strategies

In order to properly implement the proposed combination CDSS with BCMA and address the risks identified in the SAFER guide, goal-oriented change management actions are required. At the St Francis Health Services long-term care facility, the frontline implementers are the nursing personnel, the information technology support, clinical informatics, pharmacists, and administrative managers (Laukka et al., 2020). Practical solutions should include communication, engagement and capacity building to support long-term and sustainable change in practice.

Among the key strategies, one can note the 8-step change model developed by Kotter, which begins with setting a sense of urgency with regard to improving medication safety and clinical decision-making. The process begins with the reporting of Leapfrog safety scores and patient safety data on the staff to show the current gaps and how they need to improve (Miles et al., 2023). Presence of a guiding coalition of clinical champions (physicians, nurses, pharmacists) might also lead to buy-in and spearhead implementation. Another critical step is enabling action by removing barriers, such as poor training or process disruption, with special training programs and planned implementation timelines (Miles et al., 2023). The model developed by Kotter also involves short-term victories, such as the appreciation of the initial improvement in the rates of medication error or end-user satisfaction with the CDSS alerts.

The other helpful model is that of Lewin on change management, which is a three-stage model unfreezing the old routine, introducing new technology with enough support, and refreezing new behavior into normal practice. During unfreezing, staff will be involved through town halls or focus groups during which they will be asked to reflect on current dissatisfaction with manual medication processes (Stanz et al., 2021). A practical training and on-line guidance during a transition period will de-stress. Finally, new policies and performance evaluation through refreezing of changes supports the behavioral change to become permanent (Stanz et al., 2021). These measures improve the clinical and operational character of long-term care facilities and allow workers to embrace technological innovation in a safe and effective way.

Conclusion

To sum up, a clinical decision support system coupled with barcode medication administration at St. Francis Health Services is an evidence-based strategic approach to improving patient safety and clinical efficiency. The plan will lay the foundation of a safe, effective, and sustainable transition through a way of tackling the threats identified by the SAFER Guides and through implementation of change management strategies that can be applied in the long-term care setting. The proposed technology will help make decisions more efficiently, reduce drug error, and ensure higher quality of care with proper risk management, ethical practices, and staff engagement.

Appendix

Risk Mitigation Plan