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 specialized clinical practice that emphasizes regenerative medicine, including hormone therapy, preventive diagnostics, and personalized wellness care. The clinic provides services to a diverse patient base seeking proactive, individualized treatment. A persistent challenge within the practice involves delays in diagnostics, particularly in complex cases where early intervention is crucial. Such delays often compromise treatment timelines and outcomes in regenerative medicine. Therefore, this proposal presents a structured intervention designed to minimize diagnostic delays through technological integration and workflow redesign (Sierra et al., 2021).

Identification of the Practice Issue

Diagnostic delays have been a recurring issue at The Longevity Center, especially for patients presenting with multiple or ambiguous symptoms. These delays extend treatment planning and negatively impact outcomes. In regenerative medicine, early identification of factors such as hormonal deficiencies, nutritional imbalances, or autoimmune activity is vital for the success of therapies, including peptide treatments, bioidentical hormone therapy, and cellular rejuvenation techniques (Sierra et al., 2021).

Assessment of current practices revealed that a lack of streamlined communication, delayed laboratory result interpretation, and absence of prioritization guidelines are major contributors to the delays. These gaps highlight the urgent need for a more efficient diagnostic process.

Current Practice

At present, The Longevity Center uses a combination of paper-based intake forms and manual data entry into electronic health records (EHR). This dual system increases errors, delays, and potential data loss. Laboratory results are manually interpreted, and the absence of an automated alert mechanism often results in overlooked abnormalities.

Furthermore, the clinic lacks a Clinical Decision Support System (CDSS) to guide diagnostic reasoning or flag urgent cases. Staff members follow varied, non-standardized workflows, which leads to inconsistent care delivery. Such inefficiencies are particularly problematic in regenerative care, where therapies like stem cell infusions, platelet-rich plasma (PRP) treatments, or hormonal optimization depend on timely diagnostics and data-driven decisions (Sierra et al., 2021).

Proposed Strategy

The proposed solution involves implementing a standardized diagnostic intake process supported by a CDSS. This approach directly addresses inconsistencies in intake documentation, late laboratory interpretations, and fragmented decision-making processes (Wolfien et al., 2023).

Key aspects of the strategy include:

1. Standardized Intake and Digital Documentation

• Transition from paper-based forms to digital intake through the EHR.
• Comprehensive documentation of patient history, including red flags critical for regenerative therapies.
• Automatic integration of lab panels (hormone levels, micronutrient markers, inflammatory indicators) into the patient chart.

2. Clinical Decision Support System (CDSS)

• Automated alerts for abnormal findings such as cytokine levels, deficiencies, or hormone imbalances.
• Evidence-based recommendations tailored to regenerative medicine protocols.
• Reminders for timely follow-ups and intervention scheduling.

3. Workflow Redesign and Interprofessional Huddles

• Structured interdisciplinary huddles to review CDSS alerts and lab trends.
• Real-time dashboards for monitoring regenerative readiness (e.g., PRP preparation or stem cell viability).
• IT integration to ensure seamless EHR-CDSS connectivity (Khalil et al., 2025).

Impact on Quality, Safety, and Cost

Dimension	Current State	With Proposed Intervention
Quality	Variable documentation and delayed interpretation	Consistent documentation, timely diagnostics, evidence-based decisions (Ghasroldasht et al., 2022)
Safety	Missed abnormalities, poor communication	Automated alerts, reduced human error, better interdisciplinary communication (White et al., 2023)
Cost	Higher expenses from emergency care and redundant testing	Early detection saves \$8,000–\$15,000 per case; avoids \$100–\$500 in unnecessary tests; long-term cost savings despite upfront training/tech investment (White et al., 2023)

The combined effect is improved diagnostic accuracy, enhanced patient safety, and reduced financial burden for both the clinic and patients.

Role of Technology

Technology is the cornerstone of this intervention, with the CDSS integrated directly into the EHR platform. This system will:

• Provide real-time clinical guidance, flagging abnormal labs and suggesting evidence-based regenerative options.
• Reduce cognitive load by consolidating data from multiple sources into one accessible dashboard.
• Support team-based communication through shared dashboards and automatic alerts during interdisciplinary discussions (Derksen et al., 2025).
• Continuously evaluate trends in biomarkers, helping refine regenerative protocols over time.

By embedding CDSS into the workflow, clinicians will be empowered to make timely, evidence-based decisions, ensuring precision in therapies like hormone replacement, cellular rejuvenation, and PRP injections (Hermerén, 2021).

Implementation at Practicum Site

The strategy will be introduced in phases:

1. Pilot Phase – Small team testing of standardized intake and CDSS integration. Feedback collected for refinement.
2. Training Phase – Interactive workshops, continuing education credits, and peer champions to encourage adoption.
3. Full Rollout – Gradual expansion across the clinic with IT support and troubleshooting (Klein, 2025).

Challenges and Solutions:

Challenge	Solution
Staff resistance to change	Secure early leadership support, demonstrate clinical benefits, incentivize participation with CE credits (Ghasroldasht et al., 2022).
Financial limitations	Apply for quality improvement grants, negotiate phased licensing, partner with academic institutions for research-based funding.
Technology integration issues	Early IT involvement, system compatibility checks, pilot-testing in simulated environments (Makhni & Hennekes, 2023).

Interprofessional Collaboration

Collaboration among multiple disciplines is essential for successful implementation.

• Nurses/Nurse Practitioners – Conduct structured intake, ensure accurate patient history for regenerative treatments.
• Physicians/Clinical Leaders – Define diagnostic criteria, oversee treatment pathways such as cellular therapy or hormonal balancing.
• IT Professionals – Customize CDSS, troubleshoot integration, and maintain technical systems.
• Administrative Staff – Manage training schedules, monitor protocol adherence, and facilitate logistics.

Daily interdisciplinary huddles supported by a shared dashboard will enable open communication, timely decisions, and improved workflow efficiency (Makhni & Hennekes, 2023). This collective approach ensures alignment with the clinic’s vision of high-tech, patient-centered regenerative care.

Conclusion

The proposed intervention introduces standardized diagnostic intake and CDSS integration to address diagnostic delays at The Longevity Center. By enhancing quality, improving patient safety, and reducing costs, this strategy aligns with the clinic’s mission of providing precise, regenerative care. Its success will depend on phased implementation, interprofessional collaboration, and leadership-driven support, reflecting the BSN nurse’s pivotal role in leading evidence-based practice transformation.

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), 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), 2850. https://doi.org/10.3390/ijms23052850

Hermerén, G. (2021). The ethics of regenerative medicine. Biologia Futura, 72(2), 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. BMC Nursing, 24(1), 1–15. 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

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, e45948. https://doi.org/10.2196/45948

NURS FPX 4905 Assessment 4 Intervention Proposal

Cowan, K., Schwartz, J., & Mehta, S. (2022). The role of electronic health records and decision support in precision medicine: Opportunities and challenges. Journal of Personalized Medicine, 12(4), 521. https://doi.org/10.3390/jpm12040521

Gkoulalas-Divanis, A., Loukides, G., & Sun, J. (2023). Enhancing patient safety through predictive analytics and decision support: A review. Healthcare Analytics, 3(1), 100081. https://doi.org/10.1016/j.health.2023.100081

Shahmoradi, L., Safdari, R., & Omidinia, S. (2022). Clinical decision support systems and quality improvement in healthcare: A systematic review. Health Information Science and Systems, 10(1), 1–15. https://doi.org/10.1007/s13755-022-00189-3