Clinical Decision Support Systems And Stakeholders

 

Interdisciplinary collaboration is vital for the effective integration of Clinical Decision Support (CDS) systems into healthcare workflows. The Family Nurse Practitioner (FNP) plays a pivotal role in this process, acting as a bridge between clinical practice and technological innovation. The FNP's unique position in healthcare allows them to understand patient needs, clinical workflows, and the practical challenges of implementing CDS tools. Their involvement ensures that these systems are user-friendly, clinically relevant, and aligned with patient-centered care principles (Hockings et al., 2020). 

Importance of Stakeholder Collaboration

Diverse Expertise: Collaborating with stakeholders such as computer scientists, pharmacists, and other healthcare professionals brings diverse perspectives to the table. This interdisciplinary approach enhances the design and functionality of CDS tools, ensuring they address real-world clinical challenges (Hockings et al., 2020).

Patient-Centered Care: Engaging patients as stakeholders ensures that CDS tools are tailored to their needs and preferences. This collaboration fosters trust and improves patient outcomes.

Ethical and Legal Considerations: Working with legal and ethical experts helps navigate the complexities of data privacy, consent, and regulatory compliance, which are critical for the successful implementation of CDS systems.

Continuous Improvement: Collaboration with stakeholders allows for ongoing feedback and iterative improvements to CDS tools, ensuring they remain effective and relevant in dynamic healthcare environments.

Justification for Stakeholder Collaboration

The integration of pharmacogenetics into CDS systems exemplifies the need for interdisciplinary collaboration. For instance, the article highlights how nursing and computer science professionals worked together to refine a pharmacogenetics CDS tool via a mobile application (Dodson & Layman, 2023). This collaboration ensured the tool was both scientifically accurate and practically applicable in clinical settings.

Additionally, other scholarly articles emphasize the importance of interdisciplinary collaboration in navigating the complexities of clinician decision-making and optimizing patient care (Tippenhauer et al., 2024). The FNP's ability to engage with stakeholders across disciplines is crucial for the success of these initiatives.

By recognizing the value of collaboration and actively engaging with stakeholders, the FNP can drive the development and implementation of CDS strategies that enhance patient care and streamline clinical workflows. This approach not only leverages the strengths of diverse expertise but also ensures that CDS systems are ethically sound, patient-centered, and adaptable to the evolving healthcare landscape.


AI Generated Case Study: Interdisciplinary Collaboration for CDS in Urgent Care (Microsoft, 2025).

Background

Chest pain is one of the most common complaints in urgent care settings, and timely identification of myocardial infarction (MI) is critical to patient outcomes. The variability in clinical workflows and decision-making processes necessitates a robust CDS system that integrates evidence-based guidelines and supports clinicians in making accurate and timely decisions.

Design Phase

  1. Stakeholder Engagement:

    • Family Nurse Practitioners (FNPs): Provided insights into patient care workflows and clinical challenges.

    • Cardiologists: Contributed expertise on MI diagnosis and management protocols.

    • Computer Scientists: Designed the CDS system architecture and algorithms.

    • Ethical and Legal Experts: Ensured compliance with data privacy regulations.

    • Patients: Shared perspectives on usability and accessibility of the system.

  2. System Features:

    • Integration with Electronic Health Records (EHR) for real-time data access.

    • Risk stratification tools based on validated scoring systems (e.g., Marburg Heart Score).

    • Alerts for high-risk patients requiring immediate transfer to emergency care.

    • Decision pathways for managing low-risk chest pain within the urgent care setting.

Implementation Phase

  1. Training:

    • Conducted interdisciplinary workshops to train clinicians on using the CDS system.

    • Provided simulation-based training for urgent care staff to familiarize them with the system's workflows.

  2. Pilot Testing:

    • Deployed the CDS system in a rural urgent care clinic.

    • Monitored its performance and collected feedback from users.

Evaluation Phase

  1. Metrics:

    • Accuracy of MI diagnosis.

    • Time to intervention for high-risk patients.

    • Patient outcomes and satisfaction.

    • Clinician adherence to CDS recommendations.

  2. Results:

    • Improved identification of high-risk patients, with reduced time to emergency care transfer.

    • Enhanced standardization of care for low-risk chest pain patients.

    • Positive feedback from clinicians and patients on system usability.

Conclusion

This case study highlights the importance of interdisciplinary collaboration in designing, implementing, and evaluating a CDS system for managing chest pain in urgent care settings. By leveraging the expertise of diverse stakeholders, the system successfully improved clinical workflows and patient outcomes.


References 

Dodson, C., & Layman, L. (2023). Interdisciplinary collaboration among nursing and computer science to refine a pharmacogenetics clinical decision support tool via mobile application. CIN: Computers, Informatics, Nursing, 41(6), 442–448. https://doi.org/10.1097/CIN.0000000000000865 

Hockings, J. K., Pasternak, A. L., Erwin, A. L., Mason, N. T., Eng, C., & Hicks, J. K. (2020). Pharmacogenomics: An evolving clinical tool for precision medicine. Cleveland Clinic Journal of Medicine, 87(2), 91–99. https://doi.org/10.3949/ccjm.87a.19073

Microsoft. (2025). Copilot [AI software]. Microsoft. https://copilot.microsoft.com/chats/F7zg5EemWE3cBSQZwtDcj 

OpenAI. (2023). ChatGPT (April 3, 2025). Retrieved from https://chat.openai.com/

Tippenhauer, K., Philips, M., Largiadèr, C., & Sariyar, M. (2024). Using the PharmCAT tool for pharmacogenetic clinical decision support. Briefings in Bioinformatics, 25(1), bbad452. https://doi.org/10.1093/bib/bbad452



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