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NURS FPX 8012 Assessment 4 Risk Mitigation

Student Name

Capella University

NURS-FPX 8012 Nursing Technology and Health Care Information Systems

Prof. Name

Date

Risk Management Plan: Identifying and Addressing Risks Using SAFER Guides

Risk Identification:

RiskPossibility of OccurrencePotential for HarmMitigation Strategy
Data loss due to low resilience of softwareSometimesMildImplement a robust contingency plan
Poor IT infrastructureFrequentSevereInvest in upgrading technology infrastructure (Rhoades et al., 2022)
Low clinical workflowFrequentMildEnhance staff productivity through training (DiAngi et al., 2019)
Misrepresentation of patient dataSometimesSevereIntegrate a reliable patient identification system (Riplinger et al., 2020)
Poor communication among staffFrequentSevereUtilize novel communication channels to reduce barriers
Electronic Health Data LeakageSometimesSevereImplement multifactor authentication for accessing patient data (Bahache et al., 2022)

Ethical or Legal Issues Related to Identified Risks:

Distorted patient information poses serious ethical and legal risks, potentially violating privacy rights and leading to legal consequences. Patients have the right to expect confidentiality, and any misrepresentation of their information could breach this trust (Balynska et al., 2021). Such breaches may result in legal actions, and healthcare professionals must uphold ethical standards to avoid legal repercussions (Choi et al., 2019).

Adverse Consequences of Unaddressed Risks:

Failure to address these risks within a healthcare organization can lead to poor-quality patient care, financial instability, and low staff morale. Patient safety may be compromised, resulting in medical errors and potential legal actions. Non-compliance with regulations, such as HIPAA, can lead to penalties and reputational damage. Operational risks, like ineffective staffing, may impact financial performance. Proactive risk identification and mitigation are crucial for ensuring patient and staff safety, regulatory compliance, and financial stability.

Justification of Actions:

Upgrading Electronic Health Record (EHR) systems, improving IT infrastructure, and enhancing staff training can streamline healthcare processes, reduce errors, and provide real-time insights for better decision-making (Rhoades et al., 2022; DiAngi et al., 2019). Implementing a patient identification system ensures accuracy in clinical records (Riplinger et al., 2020). Multifactor authentication safeguards patient data and complies with HIPAA regulations (Bahache et al., 2022).

Change Management Strategies:

Effective change management is vital for successful implementation. The Lewin model and ADKAR model offer structured approaches. The Lewin model’s three stages—thawing, changing, and refreezing—can facilitate the transition to upgraded EHR systems and improved software consistency (Harrison et al., 2021). The ADKAR model emphasizes Awareness, Desire, Knowledge, Ability, and Reinforcement, providing a framework for staff training and ensuring successful change implementation (Balluck et al., 2020).

Application of Change Management Strategies:

For the Allen Medical Clinic, addressing EHR management flaws requires a focus on staff training and IT infrastructure improvement. By employing the Lewin model and ADKAR model, the clinic can enhance patient outcomes, staff satisfaction, and overall organizational performance. Change management strategies also contribute to improved collaboration and shared vision among stakeholders.

References:

Bahache, A. N., Chikouche, N., & Mezrag, F. (2022). Authentication schemes for healthcare applications using wireless medical sensor networks: A survey. SN Computer Science, 3(5), Article 300. https://doi.org/10.1007/s42979-022-01300-z

Balluck, J., Asturi, E., & Brockman, V. (2020). Use of the ADKAR and CLARC change models to navigate staffing model changes during the COVID-19 pandemic. Nurse Leader, 18(6). https://doi.org/10.1016/j.mnl.2020.08.006

Balynska, O., Teremetskyi, V., Zharovska, I., Shchyrba, M., & Novytska, N. (2021). Patient’s right to privacy in the health care sector. Georgian Medical News, 321, 147–153. https://pubmed.ncbi.nlm.nih.gov/35000925/

Choi, S. J., Johnson, M. E., & Lehmann, C. U. (2019). Data breach remediation efforts and their implications for hospital quality. Health Services Research, 54(5), 971–980. https://doi.org/10.1111/1475-6773.13203

DiAngi, Y. T., Stevens, L. A., Halpern – Felsher, B., Pageler, N. M., & Lee, T. C. (2019). Electronic health record (EHR) training program identifies a new tool to quantify the EHR time burden and improves providers’ perceived control over their workload in the EHR. JAMIA Open, 2(2), 222–230. https://doi.org/10.1093/jamiaopen/ooz003

NURS FPX 8012 Assessment 4 Risk Mitigation

Harrison, R., Fischer, S., Walpola, R. L., Chauhan, A., Babalola, T., Mears, S., & Le-Dao, H. (2021). Where do models for change management, improvement and implementation meet? A systematic review of the applications of change management models in healthcare. Journal of Healthcare Leadership, 13(13), 85–108. NCBI. https://doi.org/10.2147/jhl.s289176

Ilkafah, I., Mei Tyas, A. P., & Haryanto, J. (2021). Factors related to the implementation of nursing care ethical principles in Indonesia. Journal of Public Health Research, 10(2). https://doi.org/10.4081/jphr.2021.2211

Milella, F., Minelli, E. A., Strozzi, F., & Croce, D. (2021). Change and innovation in healthcare: Findings from literature. ClinicoEconomics and Outcomes Research, 13, 395–408. https://doi.org/10.2147/ceor.s301169

Nuamah, J. K., Adapa, K., & Mazur, L. (2020). Electronic health records (EHR) simulation-based training: A scoping review protocol

. BMJ Open, 10(8), e036884. https://doi.org/10.1136/bmjopen-2020-036884

Rhoades, C. A., Whitacre, B. E., & Davis, A. F. (2022). Higher electronic health record functionality is associated with lower operating costs in urban—but not rural—hospitals. Applied Clinical Informatics, 13(3), 665–676. https://doi.org/10.1055/s-0042-1750415

Riplinger, L., Piera-Jiménez, J., & Dooling, J. P. (2020). Patient identification techniques – approaches, implications, and findings. Yearbook of Medical Informatics, 29(1), 81–86.