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Risk Identified by SAFER Guides | Possibility of Occurrence | Potential for Harm | Mitigation to Address Risks |
Inadequate staff training on a clinical decision support system (CDSS) | Sometimes | M (Patient) | 1. Design an effective training program that involves initial customizing e-tutorials and ongoing employee skills assessment. 2. Develop various training tools to attend different learning styles, such as e-learning, workshop sessions, and simulation. 3. Utilize the CDSS team members as change agents within each department to ensure continuous knowledge sharing and support (Bekesiene et al., 2024). The resultant change is that the occurrence chance is reduced to “Never,” and the harm potential is reduced to “None.”
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Limited end-user involvement in CDSS design and configuration | Sometimes | Mild (Patient) | 1. Establish a structured process for engaging clinicians and other stakeholders in developing and customizing CDSS rules and alerts. 2. Conduct regular user development and gather input on CDSS performance and usability (Gao et al., 2024). 3. Implement a governance that includes representation from various clinical disciplines to oversee CDSS design and configuration decisions. Resultant change: The possibility of occurrence was reduced to “Never,” and the potential or harm was reduced to “None.” |
Insufficient integration of CDSS with clinical workflows | Sometimes | Mild (Patient) | 1. Conduct thorough workflow analyses to identify opportunities for seamless integration of CDSS into existing clinical processes. 2. Collaborate with end-users to design CDSS interfaces and prompts that minimize disruption to clinical workflows (Schütze et al., 2023). 3. Implement real-time monitoring and feedback mechanisms to identify and address workflow integration issues. Resultant change: The possibility of occurrence was reduced to “Never,” and the potential for harm was reduced to “None.” |
Inadequate monitoring and maintenance of CDSS performance | Sometimes | Mild (Patient) | 1. Establish a dedicated CDSS monitoring team responsible for continually assessing system performance, identifying issues, and implementing necessary updates. 2. Develop and implement a standardized process for reviewing and analyzing CDSS-related patient safety events and near-misses (Ulapane et al., 2023). 3. Review and update CDSS rules and alerts based on the latest clinical evidence and guidelines. Resultant change: The possibility of occurrence was reduced to “Never,” and the potential for harm was reduced to “None.” |
Lack of interoperability between CDSS and other health IT systems | Sometimes | Mild (Patient) | 1. Adopt The possibility of data exchange formats and protocols, such as Health Level Seven International (HL7) and Fast Healthcare Interoperability Resources (FHIR), to facilitate seamless data sharing between CDSS and other health IT systems. 2. Collaborate with vendors and other healthcare organizations to establish a shared framework for CDSS interoperability (Heeney et al., 2023). 3. Regularly test and validate data exchange processes to ensure the accuracy and completeness of information shared between CDSS and other systems. Resultant change: The possibility of occurrence was reduced to “Never,” and the potential for harm was reduced to “None.” |
Ethical or Legal Issues Related to Identified Risks
From the juridical point of view, letting these recognized perils go out of control in the CDSS implementation may lead the Mayo Clinic to a wide array of legal consequences. The injured patients who experience errors caused by CDSS may raise medical malpractice suits directed at the organization and the individuals involved (Syrowatka et al., 2023). NURS FPX 8012 Assessment 4: Risk Management Plan: They can imply vast amounts of financial penalties, reputation injury, and trust decline, which is a highly demanding task. Likewise, failing to adhere to regulatory requirements associated with health information technology systems, e.g., the HIPAA and HITECH Act, may attract legal applications, such as fines and sanctions.
The inability to address the risks of implementing Clinical Decision Support Systems (CDSS) at Mayo Clinic could put the professionals and the institution at risk of being sued and cited for safety issues. The main moral problem is the fact that the health of patients may be endangered, and their quality of care can be worsened. In the case of CDSS training for healthcare providers, the members are not fully involved in the system design, and clinical workflows need to be correctly integrated. This can result in CDSS misuse as alert triggers are missed, inappropriate alerts become errors, and diagnoses are delayed (Gorham et al., 2024). These factors may and do lead to patients being directly hurt. Beneficence and non-maleficence are the basic foundations of ethical considerations.
There may be seamless communication and teamwork disruption among healthcare providers when no consistency exists between the computerized clinical decision support system and other health IT technologies. This will likely create diversification in care delivery and make it possible to have repeated tests and take the wrong medication doses, compromising the patient’s safety (Lucas et al., 2022). The inability to impose continuous monitoring and performance of CDSS systems can be another reason for the system’s roleplay in generating outdated or incorrect medical recommendations, exposing patients to various health risks, and violating the principle of justice. Some patients are treated inadequately due to system failures.
NURS FPX 8012 Assessment 4: Negative Impacts
Serious consequences could arise, disregarding the ethical dilemmas and legal conflicts that follow implementing the CDSS at this clinic. Patients will likely be deprived of safety and care, resulting in worse outcomes and confidence in the healthcare system (Schütze et al., 2023). NURS FPX 8012 Assessment 4: Risk Management Plan: Experts would get stressed and tired of their jobs, and they might deal with ethical issues. On the other hand, the institution may be held accountable in case of legal liabilities or financial penalties, and the institution’s reputation could be damaged in such scenarios.
Justification of Actions to Address Identified Risks
The following implementations to mitigate the shown risks of a CDSS are rationalized based on journal articles and the newest technologies in health IT. Training clinicians with a specific framework based on initial training, competency assessment, and refresher courses will improve the proficiency and confidence in using CDSS with a readiness for better patient outcomes (Meaney et al., 2023). NURS FPX 8012 Assessment 4: Risk Management Plan: The CDSS integration plan shall incorporate various training methods and designate CDSS champions in every department. The idea is to support and guide all the staff to succeed using the system.
It is essential to make an orderly scheme for bringing end-users to the CDSS design and configuration, as the system must be connected with workflows provided by clinical teams so patients and doctors can satisfy their needs. The platform should periodically organize user feedback sessions and create an advisory committee with stakeholder engagement (Khafizova et al., 2023; Carli et al., 2020; Hussain et al., 2019). NURS FPX 8012 Assessment 4: Risk Management Plan: The end-users in the decision-making process and addressing their concerns at Mayo Clinic can create user engagement and yield more effective technology diffusion and utilization (Sutton et al., et al. 2020).
NURS FPX 8012 Assessment 4: Change Management Strategies
The implementation process of CDSS management at Mayo Clinic must remain manageable and within budget; therefore, tailored change management implementing plans should be adopted. One practical approach is to utilize Lewin’s Change Management Model, which consists of three stages: melt, thaw, change, and re-freeze. The second stage of the Mayo Clinic CDSS (inception) involves defining a vision and communication plan for the CDSS implementation (Ackerhanset al., 2024). This includes putting down in words what the technology is expected to achieve, what specific groups (either within or outside the organization) will benefit from it, and the roles and responsibilities they will have. Communicating through various platforms like meetings, emails, and newsletters can help update staff and keep them engaged during the implementation of changes (Stewart et al., 2022, p. 18). NURS FPX 8012 Assessment 4: Risk Management Plan: Besides that, this approach is crucial to the Mayo Clinic, which is multifaceted and complex, implying the efficiency of communication among various departments and levels of management. The change stage is when the proposed objectives have to be put into the workflow, training, staff helping, and progress monitoring. To end with, implementing the recommendations Stage requires reaffirming the introduced transformation and ensuring that the new methods and habits are unquestionable for everyone (Meaney et al., 2023). Lewin’s model bases its process on change management, which can aid the Mayo Clinic in handling the complexities by understanding the process of CDS implementation.
Another primary change management strategy taken is identifying and involving patients from various clinical units, including clinical champions and opinion leaders, who can assist in adopting and using the CDSS (Stewart et al., 2022). By referring to those people as role models and spokespersons for the technology – they can provide peer-to-peer support and share their insights with their peers. Evaluation tools are also essential as they provide practical input to the implementation team to identify and overcome all barriers or worries (Meaney et al., 2023). The choice of communicating the whole concept throughout the Mayo Clinic’s departments is especially relevant considering the number of clinical specialties and the necessity of cross-departmental collaboration to find its place basis for CDSS implementation. Through the involvement of the critical stakeholders in various units, who have unique fields of competence, the Mayo Clinic will be able to adapt the technology to an environment differently. Hopefully, it will gain much greater efficiency due to this uniqueness.
NURS FPX 8012 Assessment 4: Conclusion
Implementing clinical decision support systems (CDSS) at Mayo Clinic presents significant and potential risks. The SAFER Guides assessment has revealed several areas where the Mayo Clinic performs well, such as its robust data backup and disaster recovery infrastructure and standardized patient identification process. However, the assessment has also identified potential risks, including inadequate staff training, limited end-user involvement in CDSS design, and insufficient integration with clinical workflows.
References
Ackerhans, S., Huynh, T., Kaiser, C., & Schultz, C. (2024). Exploring the role of professional identity in implementing financial decision support systems—A narrative review. Implementation Science, 19(1). https://doi.org/10.1186/s13012-024-01339-x
Bekesiene, S., Smaliukienė, R., Vaičaitienė, R., Bagdžiūnienė, D., Kanapeckaitė, R., Kapustian, O., & Nakonechnyi, O. (2024). Prioritizing competencies for soldier’s mental resilience: An application of integrative fuzzy-trapezoidal decision-making trial and evaluation laboratory in updating training program. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1239481
Gao, E., Radpavar, I., Clark, E. J., Ryan, G. W., & Ross, M. K. (2024). Application of a user experience design approach for an EHR-based clinical decision support system. JAMIA Open, 7(1). https://doi.org/10.1093/jamiaopen/ooae019
Gorham, G., Abeyaratne, Heard, S., Moore, L., George, P., Kamler, P., Majoni, Chen, W., Balasubramanya, Talukder, Pascoe, S., Whitehead, A., Sajiv, BrownB.gaharan, & Cass, A. (2024). Developing an integrated clinical decision support system for the early identification and management of kidney disease—Building cross-sectoral partnerships. BMC Medical Informatics and Decision Making, 24(1). https://doi.org/10.1186/s12911-024-02471-w
Heeney, C., Bouamrane, M., M.den, S., Cresswell, K., Williams, R., & Sheikh, A. (2023). OptiOptimizingescribing in hospitals through the interoperability of systems and processes: A qualitative study in the UK, US, Norway and, the Netherlands. BMC Medical Informatics and Decision Making, 23(1). https://doi.org/10.1186/s12911-023-02316-y
Khafizova, A. A., Galimov, A. M., Kharisova, S. R., Grebenshchikova, L. Y., Yagudina, R. I., & Smirnova, L. M. (2023). The impact of healthcare digitalization on the medical education curricula and programs: Points of convergence and divergence. Contemporary Educational Technology, 15(4), ep479. https://doi.org/10.30935/cedtech/13768
Lucas, S. R., Pollak, E., & Makowski, C. (2022). A failure in the medication delivery system and systems investigation improve patient safety. Journal of Healthcare Risk Management: The Journal of the American Society for Healthcare Risk Management, 42(4). https://doi.org/10.1002/jhrm.21529
Meaney, P. A., Hokororo, Masenge, Mwanga, J. R., Kalabamu, F. S., Berg, M. D., Rozenfeld, B., Smith, Z., Chami, N., Mkopi, N., Castory, M., & Agweyu, A. (2023). Development of pediatric acute care education (PACE): An adaptive electronic learning (e-learning) environment for healthcare providers in Tanzania. Digital Health, 9. https://doi.org/10.1177/20552076231180471
Schütze, D., Holtz, S., Neff, M., Koehler, S., Schaaf, J., Frischen, L. S., Sedlmayr, B., & Müller, B. S. (2023). Requirements analysis for an AI-based clinical decision support system for general practitioners: A user-centered design process. BMC Medical Informatics and Decision Making, 23(1). https://doi.org/10.1186/s12911-023-02245-w
Stewart, E. C., Davis, J. S., Walters, T. S., Chen, Z., Miller, S. T., Duke, J. M., Alexander, L. R., Akohoue, S. A., Russell, R., Rowan, N., Campbell, L., Baxter, I., Tolbert, S., & Erves, J. C. (2022). Development of strategies for community-engaged dissemination by basic scientists: A case study. Translational Research, 252(6). https://doi.org/10.1016/j.trsl.2022.09.001
Syrowatka, A., Motala, A., Lawson, E., & Shekelle, P. (2023). Computerized clinical decision support to prevent medication errors and adverse drug events: Rapid review. PubMed; Agency for Healthcare Research and Quality (US). https://www.ncbi.nlm.nih.gov/books/NBK600580/
Ulapane, N., Forkan, A. R. M., Jayaraman, P. P., Schofield, P., Burbury, K., & Wickramasinghe, N. (2023). Using task technology fit theory to guide the codesign of mobile clinical decision support systems, in inolarspace.manoa.hawaii.edu. https://scholarspace.manoa.hawaii.edu/items/85bda762-a66f-4afa-af6a-a98a912e9d81
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Evidenced-Based Literature: Search and Organization
After that, the literature review and study must be guided by facts and aim to find ways to make drugs safer (Geerts et al., 2020). NURS FPX 8030 Assessment 2: Evidenced-Based Literature: Search and Organization, Combining the latest scientific research on the best ways to reduce drug mistakes will help us devise modifications to our systems that will have a substantial impact on our business.
Patient Concern, PICO(T) Inquiry, and Supporting Literature
Medicine mistakes present a substantial and frequent danger to patient well-being. Every year, many unfavorable drug incidents injure patients in medical environments (Mulac, 2022). From the data we have at this time, it is obvious that we need to improve systems methods to reduce the likelihood of drug errors and the troubles that they bring to patients.
The PICOT question: If nurses (P) use a Comprehensive Safety Guidelines over medication security (I) instead of normal medication procedures (C), do they report fewer medicinal mistakes (O) for a period of 12 weeks (T)?
A focused review of the current scientific research was conducted to identify gaps and repetitive issues that are causing so many drug errors in hospitals. The inquiries in Medline and CINAHL (Piña et al., 2020) revealed that 30% of drug orders have errors. NURS FPX 8030 Assessment 2: Evidenced-Based Literature: Search and Organization, According to Gaspar et al. (2023), errors in medications occur in up to two-thirds of patients who are hospitalized, which increases the cost of hospitalization by as much as $9000.
Search Strategy for Best Evidence
Studies about medication errors and treatment in the emergency department for adults were included and excluded using strict criteria. Following the removal of the duplicates, the first hits were screened for relevance based on the title and abstract. This identified 45 possible articles for full-text assessment. Eight studies were, however, included as they helped us determine the practical implications, underlying causes, and evidence-based solutions for drug safety vulnerabilities. Manual reference checks were also conducted to ensure that there was adequate reading.
