Student Name
Capella University
NURS-FPX4905 Capstone Project for Nursing
Prof. Name
Date
Intervention Proposal
The Longevity Center is a specialized clinical facility focused on wellness and regenerative medicine, offering services such as hormone therapy, preventive care, and advanced diagnostic testing. Its patient population primarily consists of individuals seeking proactive, personalized healthcare approaches. A recurring challenge identified at the center is the delay in diagnostic processes, particularly in complex cases where early detection is critical for successful outcomes (Sierra et al., 2021). This proposal aims to outline a strategic intervention designed to reduce diagnostic delays by enhancing workflow efficiency and implementing technology, specifically through the integration of a Clinical Decision Support System (CDSS).
Identification of the Practice Issue
What is the main issue impacting patient care at The Longevity Center?
Patients who present with multiple or unclear symptoms frequently experience delays in receiving a definitive diagnosis, which in turn delays treatment initiation. This issue is particularly concerning in regenerative medicine, where interventions such as peptide therapies, bioidentical hormone replacement, and cellular rejuvenation are heavily dependent on early identification of contributing factors such as hormonal imbalances, autoimmune triggers, and nutritional deficiencies (Sierra et al., 2021).
What factors contribute to these delays?
An internal review of clinical operations identified several sources of inefficiency: fragmented communication among staff, absence of standardized prioritization protocols, and delayed interpretation of laboratory results. These gaps compromise patient outcomes because regenerative therapies rely on precise and timely diagnostics to optimize effectiveness.
Current Practice
How does The Longevity Center currently manage patient intake and diagnostics?
Currently, the clinic relies on paper-based intake forms that staff later enter manually into the Electronic Health Record (EHR). This process increases the risk of transcription errors and slows the overall workflow. Laboratory results are also reviewed manually, with no automated alert system to identify critical values.
Table 1
Current Practice Gaps
| Issue | Current Approach | Consequences in Regenerative Care |
|---|---|---|
| Patient intake | Paper-based, manually entered into EHR | Risk of missing data; slow processing |
| Lab result review | Manual review without alerts | Delayed recognition of critical values |
| Clinical decision support | No CDSS implemented | Inconsistent reasoning; lack of evidence-based care |
| Staff workflows | Non-standardized and variable | Inconsistent timelines and care quality |
Without standardized protocols and a CDSS, workflow variability remains high, negatively affecting treatments such as stem cell infusions, platelet-rich plasma (PRP) therapies, and hormone optimization protocols.
Proposed Strategy
What is the proposed solution to address diagnostic delays?
The intervention focuses on implementing a standardized diagnostic intake process integrated with a CDSS. This strategy aims to address gaps in patient intake, laboratory result interpretation, and clinical decision-making. The initiative will streamline staff workflows and optimize organizational processes through technology and workflow redesign (Wolfien et al., 2023).
Key Elements of the Strategy
- Implement standardized intake procedures, including comprehensive training for providers and nurses.
- Digitally integrate intake forms into the EHR to ensure accurate and complete documentation.
- Utilize an automated CDSS to flag abnormal lab values, provide evidence-based recommendations, and send reminders (Khalil et al., 2025).
- Conduct regular interdisciplinary huddles to review CDSS alerts and assess treatment readiness.
- Phase the implementation gradually with IT support to ensure smooth integration between CDSS and EHR (Klein, 2025).
Impact on Quality, Safety, and Cost
How will the proposed strategy improve care quality, patient safety, and financial sustainability?
Quality: Standardizing intake processes and using a CDSS will minimize diagnostic variability, improve consistency in care, and ensure alignment with evidence-based regenerative medicine protocols (Ghasroldasht et al., 2022).
Safety: Automated alerts will reduce the risk of missing critical lab findings, improve interdisciplinary communication, and minimize handoff errors (White et al., 2023).
Cost: Early identification of imbalances and the avoidance of unnecessary diagnostic tests will lower the financial burden of emergency interventions and redundant procedures, making the investment in technology cost-effective over time.
Table 2
Projected Benefits of Proposed Strategy
| Dimension | Expected Outcome | Example in Regenerative Care |
|---|---|---|
| Quality | Improved diagnostic accuracy; reduced omissions | Timely detection of micronutrient deficiencies |
| Safety | Automated alerts for abnormalities; fewer errors | Prevent missed hormonal imbalances or inflammation |
| Cost | Reduced unnecessary tests and emergency admissions | Avoiding $8,000–$15,000 emergency episodes |
Role of Technology
How does technology support this intervention?
Technology is central to the intervention. Integrating a CDSS with the existing EHR allows providers to access real-time, evidence-based guidance. Functions include flagging abnormal lab values, suggesting differential diagnoses, and recommending treatment options tailored to regenerative medicine protocols (Derksen et al., 2025). The system reduces cognitive load, limits human error, and ensures critical trends in biomarkers are not overlooked. Shared dashboards facilitate interprofessional communication, and analytics tools track longitudinal data to continuously refine diagnostic processes (Hermerén, 2021).
Implementation at Practicum Site
How will the intervention be implemented at The Longevity Center?
Implementation will be phased, beginning with a pilot program involving a small provider team to test redesigned workflows and CDSS integration. Lessons learned will inform broader adoption across the clinic (Klein, 2025).
What challenges are anticipated, and how will they be addressed?
- Staff resistance: Addressed through education, training, and structured change management.
- Financial constraints: Managed with phased licensing, grant opportunities, and academic partnerships.
- Technological barriers: Mitigated by pre-testing workflows in simulated environments with IT support (Makhni & Hennekes, 2023).
Interprofessional Collaboration
What roles are critical for the success of this initiative?
The intervention’s success depends on collaboration among multiple professional roles to integrate technology with clinical care.
Table 3
Interprofessional Roles in Implementation
| Role | Contribution | Example in Regenerative Care |
|---|---|---|
| Nurses & NPs | Implement intake redesign and complete histories | Identifying red flags for PRP or peptide therapies |
| Physicians | Define diagnostic criteria and treatment pathways | Determining eligibility for cellular therapies |
| IT Professionals | Manage EHR-CDSS integration and customization | Setting alerts for regenerative-specific labs |
| Administrative Staff | Coordinate training and monitor compliance | Organizing interdisciplinary team huddles |
Collaboration ensures that both technological and clinical components are implemented effectively, supporting patient-centered, evidence-based regenerative care.
Conclusion
The proposed intervention—standardized diagnostic intake integrated with a CDSS—will enhance diagnostic accuracy, improve workflow efficiency, and promote safe, high-quality, and cost-effective care. Leveraging technology, fostering interprofessional collaboration, and implementing a phased rollout will allow The Longevity Center to deliver timely, patient-centered regenerative treatments in line with best practices in precision medicine.
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
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). 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
NURS FPX 4905 Assessment 4 Intervention Proposal
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
NURS FPX 4905 Assessment 4 Intervention Proposal
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