NURS FPX 4905 Assessment 4 Intervention Proposal

Name

Capella university

NURS-FPX4905 Capstone Project for Nursing

Prof. Name

Date

Intervention Proposal

The Longevity Center is a wellness-oriented clinical facility specializing in regenerative medicine, encompassing hormone therapy, advanced diagnostics, and preventive care. The clinic caters to a diverse patient population seeking personalized, proactive healthcare strategies. A recurring challenge at this site is diagnostic delays, particularly in complex cases where timely intervention is critical (Sierra et al., 2021). The purpose of this proposal is to present a strategic intervention that leverages technology and workflow optimization to reduce diagnostic delays and enhance patient outcomes.

Identification of the Practice Issue

Diagnostic delays frequently occur in patients presenting with multiple symptoms or unclear clinical pathways, leading to extended treatment planning periods. In regenerative medicine, prompt recognition of hormonal imbalances, micronutrient deficiencies, or autoimmune triggers is essential for effective treatments such as bioidentical hormone therapy, peptide therapy, and cellular rejuvenation protocols (Sierra et al., 2021). Prior assessments at the clinic revealed delayed interpretation of lab results due to fragmented communication and the absence of standardized prioritization protocols.

Current Practice

Currently, The Longevity Center relies on paper-based intake forms and manual data entry into the electronic health record (EHR). This process increases the risk of data loss and delays in care. Lab results are manually reviewed without an automated alert system to identify critical abnormalities. The absence of a Clinical Decision Support System (CDSS) hampers diagnostic reasoning and prioritization of urgent cases. Staff follow non-standardized workflows, leading to inconsistencies in care quality and timelines—an issue especially problematic in regenerative medicine, where timely, data-driven decisions are crucial for initiating therapies like stem cell infusions, platelet-rich plasma treatments, and hormonal optimization protocols (Sierra et al., 2021).

Proposed Strategy

To address these challenges, the introduction of a standardized diagnostic intake process integrated with a CDSS is recommended. This strategy directly tackles inconsistencies in intake, delayed lab interpretation, and unstructured decision-making—critical barriers in regenerative medicine (Wolfien et al., 2023). The approach focuses on optimizing workflows, training personnel, and implementing technology-driven solutions.

Key Elements of the Strategy:

• Standardized Intake Procedures: Nursing personnel and providers will be trained to complete comprehensive documentation of patient history, identify red flags, and conduct initial assessments critical for planning regenerative treatments.

• Digitalized Workflow: Patient intake will be fully integrated into the EHR, improving the quality, accessibility, and circulation of essential clinical information, such as hormone levels, micronutrient panels, and inflammatory markers.

• Automated CDSS Alerts: The CDSS will automatically flag abnormal results, provide evidence-based recommendations tailored to regenerative medicine, and remind clinicians to make timely interventions (Khalil et al., 2025).

• Interprofessional Huddles: Regular team meetings will review CDSS alerts and lab trends, including those related to PRP readiness or cellular repair status.

• IT Integration: IT staff will ensure seamless integration of the CDSS with minimal workflow disruption.

Assumptions: Successful implementation assumes adequate staff training, gradual technology adoption, and enhanced communication protocols (Klein, 2025).

Impact on Quality, Safety, and Cost

The adoption of a standardized intake and CDSS will enhance the quality, safety, and cost-effectiveness of care at The Longevity Center.

Aspect	Impact
Quality	Standardized protocols reduce diagnostic omissions, improve consistency in documentation, and support evidence-based decision-making for regenerative treatments, including PRP, stem cell, and peptide therapies (Ghasroldasht et al., 2022).
Safety	Automated alerts for critical lab values, such as cytokine levels or hormonal imbalances, enhance patient safety. Shared dashboards improve interdisciplinary communication and reduce handoff errors (White et al., 2023).
Cost	Early detection of metabolic or immune abnormalities prevents costly acute episodes ($8,000–$15,000 per case) and avoids unnecessary tests ($100–$500 per test), offsetting initial training and technology costs.

Role of Technology

Technology plays a central role in this intervention, particularly through the integration of a CDSS within the existing EHR. The system will:

• Provide real-time clinical guidance by analyzing patient data and flagging abnormal results.

• Suggest differential diagnoses and evidence-based recommendations during intake and diagnostics.

• Reduce cognitive load and minimize human error by standardizing workflows.

• Enable seamless access to historical patient records, lab values, and biomarkers essential for regenerative protocols.

• Support interprofessional collaboration via shared dashboards and automated alerts for overdue follow-ups, high-risk symptoms, or duplicate testing (Derksen et al., 2025; Klein, 2025).

This technology aligns with the clinic’s focus on high-quality, patient-centered regenerative care (Hermerén, 2021).

Implementation at Practicum Site

Implementing the proposed intervention will require a phased approach sensitive to the clinic’s structure and challenges:

1. Pilot Phase: Introduce the standardized intake process and CDSS to a small team, collect feedback, and refine workflows (Klein, 2025).

2. Addressing Staff Resistance: Engage clinical leadership early, communicate benefits, provide interactive training, continuing education credits, and identify peer champions (Ghasroldasht et al., 2022).

3. Financial Considerations: Seek external grants, phased licensing agreements, or academic partnerships to manage budget constraints.

4. Technical Integration: Collaborate with IT to test system compatibility and simulate real workflow scenarios before full implementation (Makhni & Hennekes, 2023).

Interprofessional Collaboration

Successful implementation depends on coordinated interdisciplinary collaboration among physicians, nurse practitioners, nurses, medical assistants, administrative staff, and IT professionals.

Role	Contribution
Nurse Practitioners & Nurses	Execute standardized intake, document patient history, and identify factors guiding regenerative therapies (PRP, peptides).
IT Personnel	Integrate CDSS into EHR, customize features, troubleshoot technical issues.
Administrative Staff	Coordinate training, schedules, and adherence monitoring.
Physicians & Clinical Leaders	Define diagnostic criteria for regenerative therapies, oversee implementation, and support workflow adoption.
Teamwork Tools	Daily interdisciplinary huddles and shared EHR dashboards enhance communication and collaboration (Makhni & Hennekes, 2023).

Conclusion

The proposed intervention aims to streamline diagnostics by implementing standardized intake and CDSS integration, improving accuracy, patient safety, and operational efficiency. By supporting early detection and reducing unnecessary procedures, the plan enhances patient outcomes and lowers costs. Successful adoption requires interprofessional collaboration, strategic planning, and phased implementation. This initiative highlights the BSN nurse’s role in leading evidence-based improvements in clinical practice.

References

Derksen, C., Walter, F. M., Akbar, A. B., Parmar, A. V. E., Saunders, T. S., Round, T., Rubin, G., & Scott, S. E. (2025). The implementation challenge of computerised clinical decision support systems for the detection of disease in primary care: Systematic review and recommendations. Implementation Science, 20, 1–33. https://doi.org/10.1186/s13012-025-01445-4

Ghasroldasht, M. M., Seok, J., Park, H.-S., Liakath Ali, F. B., & Al-Hendy, A. (2022). Stem cell therapy: From idea to clinical practice. International Journal of Molecular Sciences, 23(5). https://doi.org/10.3390/ijms23052850

Hermerén, G. (2021). The ethics of regenerative medicine. Biologia Futura, 72, 113–118. https://doi.org/10.1007/s42977-021-00075-3

NURS FPX 4905 Assessment 4 Intervention Proposal

Khalil, C., Saab, A., Rahme, J., Bouaud, J., & Seroussi, B. (2025). Capabilities of computerized decision support systems supporting the nursing process in hospital settings: A scoping review. Biomed Central Nursing, 24(1). https://doi.org/10.1186/s12912-025-03272-w

Klein, N. J. (2025). Patient blood management through electronic health record [EHR] optimization (pp. 147–168). Springer Nature. https://doi.org/10.1007/978-3-031-81666-6_9

Makhni, E. C., & Hennekes, M. E. (2023). The use of patient-reported outcome measures in clinical practice and clinical decision making. The Journal of the American Academy of Orthopaedic Surgeons, 31(20), 1059–1066. https://doi.org/10.5435/JAAOS-D-23-00040

Sierra, Á., Kim, K. H., Morente, G., & Santiago, S. (2021). Cellular human tissue-engineered skin substitutes investigated for deep and difficult to heal injuries. Regenerative Medicine, 6(1), 1–23. https://doi.org/10.1038/s41536-021-00144-0

NURS FPX 4905 Assessment 4 Intervention Proposal

White, N., Carter, H. E., Borg, D. N., Brain, D. C., Tariq, A., Abell, B., Blythe, R., & McPhail, S. M. (2023). Evaluating the costs and consequences of computerized clinical decision support systems in hospitals: A scoping review and recommendations for future practice. Journal of the American Medical Informatics Association, 30(6), 1205–1218. https://doi.org/10.1093/jamia/ocad040

Wolfien, M., Ahmadi, N., Fitzer, K., Grummt, S., Heine, K.-L., Jung, I.-C., Krefting, D., Kuhn, A. N., Peng, Y., Reinecke, I., Scheel, J., Schmidt, T., Schmücker, P., Schüttler, C., Waltemath, D., Zoch, M., & Sedlmayr, M. (2023). Ten topics to get started in medical informatics research. Journal of Medical Internet Research, 25. https://doi.org/10.2196/45948