Student Name
Capella University
NURS-FPX4905 Capstone Project for Nursing
Prof. Name
Date
Intervention Proposal
The Longevity Center is a specialized clinical practice focused on regenerative medicine, offering hormone therapy, advanced diagnostics, and preventive care. Its patients seek proactive and personalized healthcare solutions. However, the clinic struggles with recurring diagnostic delays, particularly in complex cases where early intervention is crucial (Sierra et al., 2021). The purpose of this intervention proposal is to recommend a structured strategy that reduces diagnostic delays through improved workflow organization and the adoption of technology.
Identification of the Practice Issue
Diagnostic delays often occur in cases presenting with multiple symptoms or unclear pathways. This issue is especially significant in regenerative medicine, where timely identification of hormonal imbalances, nutritional deficiencies, or autoimmune responses is critical for effective treatment outcomes (Sierra et al., 2021). Previous assessments revealed that delayed interpretation of laboratory results stemmed from fragmented staff communication and lack of prioritization protocols. These delays directly affect the success of advanced regenerative therapies, including peptide treatments, bioidentical hormone therapy, and cellular rejuvenation procedures.
Current Practice
The Longevity Center currently uses paper-based intake forms combined with manual EHR entry. This increases the risk of errors and slows down the diagnostic process. Laboratory results are reviewed manually without the support of automated alerts. The absence of a Clinical Decision Support System (CDSS) prevents staff from efficiently flagging abnormalities or prioritizing urgent cases (Sierra et al., 2021). Moreover, staff rely on non-standardized workflows, leading to inconsistencies and variability in care quality. Such inefficiencies are particularly harmful in regenerative medicine, where treatments like platelet-rich plasma (PRP), stem cell therapies, and hormone optimization depend on timely diagnostics.
Proposed Strategy
The proposed solution is to implement a standardized diagnostic intake process supported by a CDSS. This will ensure that all patient histories, laboratory results, and red flags are systematically recorded and promptly addressed. The intake process will be digitized and integrated into the EHR, improving accessibility and communication of critical data such as hormone levels, micronutrient deficiencies, and inflammatory markers (Wolfien et al., 2023).
Key Interventions
Proposed change: Standardized intake documentation
Expected outcome: Accurate patient histories, early recognition of red flags
Proposed change: CDSS integration within EHR
Expected outcome: Automatic flagging of abnormal labs, evidence-based diagnostic support
Proposed change: Staff training and workflow redesign
Expected outcome: Streamlined communication, improved adoption of new processes
Proposed change: Interprofessional huddles and review meetings
Expected outcome: Improved collaboration, timely action on diagnostic alerts
Proposed change: Dedicated IT support for CDSS-EHR integration
Expected outcome: Smooth technical operation with minimal disruptions
These changes are based on the assumption that staff will adapt with proper training, and that communication structures will be enhanced to support collaboration (Klein, 2025).
Impact on Quality, Safety, and Cost
The proposed intervention significantly improves care quality, safety, and affordability.
Quality improvements will result from consistent documentation and standardized workflows that reduce diagnostic errors. For example, early identification of regenerative indicators such as inflammatory markers or hormone profiles ensures timely initiation of therapies like PRP or peptide treatment (Ghasroldasht et al., 2022).
Safety will be enhanced through automated alerts in the CDSS, preventing critical abnormalities such as cytokine imbalances or immune triggers from being overlooked. Real-time dashboards will support communication across disciplines and reduce handoff errors (White et al., 2023).
Cost savings are also expected. Early detection can prevent acute complications that may otherwise cost $8,000 to $15,000 per episode. Additionally, avoiding unnecessary laboratory or imaging tests saves between $100 and $500 per case. While initial investments in technology and training are necessary, long-term financial benefits from efficiency, reduced liability, and improved patient outcomes outweigh these expenses (White et al., 2023; Dyb et al., 2021).
Role of Technology
Technology is central to this improvement plan. Integrating a CDSS within the existing EHR will allow for real-time analysis of patient data. The system will automatically flag abnormal results, suggest evidence-based diagnostic options, and provide recommendations relevant to regenerative medicine (Derksen et al., 2025).
Technological benefits include automated alerts for overdue follow-ups, dashboards for interdisciplinary collaboration, and analytical features that highlight workflow bottlenecks. Integration with the EHR reduces provider workload and ensures that biomarkers critical to regenerative care are not overlooked (Klein, 2025). By embedding decision support within the clinical workflow, technology supports both efficiency and accuracy.
Implementation at Practicum Site
Implementation will occur in phases to minimize disruption. A pilot phase will first test the standardized intake process and CDSS integration with a smaller team, ensuring that feedback is collected and adjustments are made before clinic-wide adoption (Klein, 2025). Staff resistance will be addressed through leadership support, clear communication of benefits, and incentives such as continuing education credits. Peer champions will also promote adoption.
Financial challenges may arise due to licensing and training costs. To address this, external funding opportunities and phased investments will be explored (Makhni & Hennekes, 2023). Additionally, close collaboration with IT staff will ensure compatibility between the CDSS and existing EHR. Test environments will be used to simulate workflows and reduce disruptions during rollout.
Interprofessional Collaboration
The intervention requires active collaboration among all healthcare professionals. Nurse practitioners and nursing staff will oversee standardized intake procedures, ensuring complete documentation. IT staff will manage CDSS integration and resolve technical issues. Administrative teams will handle scheduling, training, and compliance monitoring. Physicians and clinical leaders will set regenerative-specific diagnostic guidelines and integrate results into personalized care plans (Hermerén, 2021).
Daily interdisciplinary huddles will further ensure that critical laboratory results are reviewed collectively, improving precision and teamwork. This collaborative approach aligns with regenerative medicine’s emphasis on personalized and holistic care (Makhni & Hennekes, 2023).
Conclusion
Introducing standardized intake processes and CDSS integration at The Longevity Center will reduce diagnostic delays, enhance efficiency, and improve patient-centered outcomes. The strategy strengthens diagnostic quality, boosts patient safety, and reduces long-term costs. Its success depends on phased implementation, interprofessional collaboration, and strong leadership engagement. This proposal highlights the role of BSN nurses in driving evidence-based improvements within 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
Dyb, K., Warth, L. L., & Sætren, G. B. (2021). The role of digital technologies in healthcare innovation: A review of the literature. Journal of Innovation in Health Informatics, 28(1), 1–12. https://doi.org/10.14236/jhi.v28i1.1220
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
NURS FPX 4905 Assessment 4 Intervention Proposal
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. 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. 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