NURS FPX 8022 Assessment 3 Risk Mitigation Plan

NURS FPX 8022 Assessment 3

Risk Mitigation Plan

Student name

NURS-FPX8022

Capella University

Professor Name

Submission Date

Introduction

Technology plays a crucial role in detecting and implementing strategies that mitigate risks in healthcare provision. On the one hand, it can be said that the Epic electronic health record (EHR) systems have many benefits due to the high level of features (including the use of AI-based predictive analytics). Still, on the other hand, threats are also present that have the potential to damage the safety of the patient, the effectiveness of the working process, and the data integrity (Yaqoob et al., 2021).

The mitigation measure outlined in the following section builds upon the integration of predictive analytics into the Epic EHR platform of the Massachusetts General Hospital (MGH), where the safety assurance factors of EHR resilience (SAFER) guides are used. Guides will help to identify vulnerabilities, estimate the probability of risk, and implement systematic risk reduction. The plan specifically aims to identify potential risks, which include: medication errors, incomplete documentation, late detection of adverse events, downtime phenomenon, data entry errors, and interoperability issues. A deep risk management and organization will allow MGH to improve patient safety standards, expand Leapfrog and Medicare Compare scores, and the quality of care.

Risk Mitigation Plan

One of the most serious risks has been defined as medication administration errors, the probability of which is high at the beginning, and the patient may suffer severely. Human errors are generally caused by handwork and incorrect transmission of information or dose (Dalalah & Dalalah, 2023). The risks will be significantly reduced by the integration of the barcode medication administration (BCMA) system with the real-time warning system provided by Epic to the MGH. Once mitigated, the likelihood of recurrence is very low, and detrimental damage is limited to the medium category, so severe and more precise drug administration will be obtained.

The second risk is missing clinical documentation that initially occurs at an intermediate level and may not only delay the provision of care but also lead to wrong decision-making. The introduction of clinical decision support in the system, combined with the standardized EHR templates, will prevent the risk (Cooley et al., 2025). The interventions will also reduce contradictory and doubtful documentation, and the risk of being unable to log any of it (or the risk of being unable to log any at all) will have an insignificant influence that will lead to slight damage to patients.

The other high-risk zone is the zone of delayed adverse event identification, during which the failure to detect the first warning signs can cause disastrous complications, including sepsis or cardiac arrest. Initially, the risk is high and the consequences are dire. However, predictive analytics can be used to observe patient vitals and lab value trends on an active basis and in Epic (Merrill et al., 2023).

The systems warn of the development of conditions, before they can occur, which is the lowest possible frequency and the least possible damage. Moderate risk level: There is a likelihood of an EHR downtime or system failure with extreme disruption in care delivery. The type of downtime will disrupt access to both patient records and treatment (Larsen et al., 2020). MGH reduces the likelihood of such events to an extremely low level with redundant, cloud-based failover systems and well-developed downtime procedures, and these events have little impact on patient safety and workflow continuity.

Data entry errors by staff are a common problem with high likelihood and moderate or serious consequences. These delays may be purely human error, e.g., weariness or misunderstanding. The employee education on examples reinforced through real-time verification and error reporting in Epic reduces the incidence to moderate ranges and limits the possible harm to low ranges (Maria & Segovia, 2025).

Finally, a high risk is characterized by the lack of interoperability of the systems, as there is no harmony in communication between the departments or outside providers. It can harm patient safety, and consequently, it can also take a long time to deliver the information that patients so desperately need (Li et al., 2022). Health Level Seven International (HL7) FHIR standard will enable the MGH to be limited by restricting i,t, and will provide interoperability among the healthcare networks. The risk is reduced after mitigation and is described as a moderate occurrence and low potential harm.

Ethical or Legal Issues

The risks detected in the Epic EHR framework at MGH can be extremely detrimental to the organization in terms of ethical and legal consequences unless they are mitigated. In particular, if medication reconciliation fails or clinical decision support shows false information, the patient can be harmed (e.g., by adverse drugs, improper diagnosis, taking too long to act, etc.) (Ciapponi et al., 2021). The question is ethically flawed, both in terms of beneficence and nonmaleficence, since the well-being of the patient is jeopardized in the case of preventable harm.

The malpractice suits, loss of accreditation, and federal regulatory fines under the Centers for Medicare and Medicaid Services may all leave MGH vulnerable to litigation in court due to the legal lapses. Furthermore, the emergence of new medical mistakes might also be triggered by the fact that the documentation, in its turn, may not be written correctly as well, and this lack of information exchange between the patient and practitioner may also trigger the situation between practitioners and patients’ deterioration or improvement as well (Howick et al., 2024). This can also lead to a decline in the institution’s reputation, resulting in lower Leapfrog and Medicare Compare ratings, reduced funding, and diminished patient trust. These untreated risks, in turn, have a spillover effect on the patients, medical professionals, and the sustainability of organizations.

The adoption of Epic EHRs’ AI-based predictive analytics in MGH is coherent with the Health Insurance Portability and Accountability Act of 1996 (HIPAA), as it offers a high degree of confidentiality, integrity, and access to health information of the patients. This corresponds to multidirectional security, in which the control of access in the data direction is implemented at the level of ensuring data security: multifactor authentication or role (Suleski et al., 2023).

HIPAA-compliant infrastructure offers predictive analytics tools based on de-identified data to give a bigger picture of population health, without exposing sensitive data. Other security measures that can be adopted to prevent breaches and ransomware attacks are end-to-end encryption, firewall implementation, and intrusion detection systems (Suleski et al., 2023). Regular risk assessments using the SAFER framework will also ensure that the organization complies with privacy and security requirements developed by HIPAA (Sittig et al., 2025). The safeguards can be combined to protect against a range of risks, both operationally and clinically, and offer legal and ethical safeguards to patients, practitioners, and institutions.

Literature Justifications

Evidence-based best practices for improving patient safety, workflow efficiency, and data security can inform the proposed actions to mitigate the identified risks in the Epic EHR implementation at MGH. For example, we can refer to a risk of prescription and medication reconciliation and pharmacist check to avoid adverse drug events and prescription errors (Ciapponi et al., 2021) by using automated EHR alerts to reduce this risk.

Recently, Elbeddini et al. (2021) study showed that adverse drug events are among the most common reasons that lead to preventable hospital readmissions with the assistance of more efficient medication reconciliation procedures. In addition to this, patients at high risk for medication errors can be predicted by preemptive analytics in Epic; this literature supports prognostication for patients at risk for harm to allow for proactive interventions to mitigate harm (Chishtie et al., 2023). Not only are the measures effective in reducing the risk of harm, but they are also effective in upholding the ethical duty of nonmaleficence by eliminating the possibility of exposing patients to unnecessary risks.

Clinical decision support (CDS) is another important problem where false or incomplete data might compromise timely interventions. The potential dangers of implementing real-time alerts, evidence-based recommendations, and standardized workflow relationships in the Epic could be offset by an increase in diagnostic accuracy and a reduction in variability in the provision of care (Chishtie et al., 2023). Syrowatka et al. (2023) state that integrated CDS plays an important role in the clinical guideline adherence of clinicians, reducing the time clinicians and patients spend on diagnosis and decision-making.

Also, the data security enforcement through multi-level encryption, role-based access, and auditing done on a regular basis meets both the legal and ethical requirements. Organizations’ increased ability to prevent cyber-attacks through a strong cybersecurity posture improves patient compliance and the confidence that they have in the institutions they trust with HIPAA compliance (Pool et al., 2024). It is also important to have proactive cybersecurity due to the rise in ransomware attacks on healthcare facilities. Finally, documentation and communication workflows are coordinated so that providers in the disciplines receive consistent and accurate patient information. Arndt et al. (2022) agreed that EHRs are transparent and facilitate teamwork by enabling universal communication, thereby eliminating the possibility of mistakes during handoffs. Combined, the proposed actions are evidence-based as well as feasible in the MGH context.

Change Management Strategies

The implementation of the Epic EHR-associated actions and the mitigation of the risks disclosed by the SAFER Guide require the use of evidence-based change management strategies. In the MGH setting, where interdisciplinary teams, IT, and clinicians have to work together, change strategies have to be structured to enable alignment, acceptance, and sustainability. The best practices considered staff and leadership support with and inclusion in continuing training as preconditions for addressing resistance and increasing adoption (Calduch et al., 2021). Hence, change management strategies and approaches should be customized to achieve clinical effectiveness as well as workflow efficiency.

One of the best models is the 8-step change model from Kotter because it is a structured blueprint to follow for significant system change, which also applies to the Epic implementation. The first step for implementation is to build urgency around the risks of patient safety and compliance, and the second step is to build a vision where the EHR optimization is linked to improved outcomes (Miles et al., 2023).

Having a guiding coalition of clinical leaders, information technology specialists, and operational managers ensures shared responsibility and builds credibility. Second, apply dedicated training and feedback processes for staff during the course of delivering ‘low hanging fruit’, to establish the (rather) rapid victories that build momentum (Miles et al. 2023). Kotter’s model is especially useful for the healthcare context because it enables the systematization of organizational goals with individualized motivation and ensures the strong generalization of proposed measures.

The other applicable model is the Lewin model of change management which is complementary to Kotters steps but is more behavioural and cultural oriented. Mechanisms of unfreezing that will address an unwillingness to participate from those who work in the hospital by emphasizing the risks of less than optimal EHR practices and the benefits of improved work processes (Harrison et al., 2021). The theoretical understanding and technical education provided during the change stage prepare staff to utilize Epic’s functionalities, including predictive analytics, CDS warnings, and secure communication systems. The fourth stage is refreezing in which the policies, auditing, and the rewards are locked in and become part of the new normal business (Harrison et al., 2021). The acceptance process of cultural changes culminates in the adoption of changes that reduce the risk and improve the safety and security of patient data in the long term.

Conclusion

Finally, the digitalization of MGH has both opportunities and challenges that require the systematic management of change. The adoption of Epic EHR should be supported by evidence-based frameworks that will be pegged on the SAFER Guides to ensure safety, efficiency, and sustainability. By using strong security principles along with ethics and HIPAA compliance, the hospital can better guarantee patients’ data is kept secure and reduce workflow interruption, while improving clinical outcomes. According to the Kotter and Lewin models, training will help them adopt new practices and increase the likelihood of staff embracing these changes, while also facilitating the transition to a culture of accountability and innovation.

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Appendix

Risk Mitigation Plan