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 wellness clinic focusing on regenerative medicine, including hormone replacement therapies, advanced diagnostic testing, and preventive healthcare. The patient base is diverse, ranging from individuals seeking proactive wellness management to those with complex medical conditions requiring innovative interventions. However, a recurring challenge in the practice involves diagnostic delays, particularly in multifaceted cases where early intervention significantly influences treatment outcomes (Sierra et al., 2021).

The goal of this proposal is to introduce a strategic intervention that integrates technology and workflow optimization to reduce diagnostic delays and improve patient-centered regenerative care.

Identification of the Practice Issue

Delays in diagnostics have been observed in patients presenting with multiple symptoms and unclear clinical patterns. In regenerative medicine, early recognition of factors such as hormonal imbalances, micronutrient deficiencies, and autoimmune activity is critical for maximizing treatment effectiveness. For instance, bioidentical hormone therapy, peptide therapy, and cellular rejuvenation require timely and accurate assessments to achieve optimal results (Sierra et al., 2021).

A review of current operations at The Longevity Center revealed that diagnostic delays often stem from fragmented staff communication and the absence of prioritization systems for urgent lab interpretations. This has resulted in prolonged treatment planning and reduced therapeutic efficiency.

Current Practice

At present, the clinic relies heavily on paper-based intake processes that are manually transcribed into the electronic health record (EHR). This increases the chance of information loss and clerical errors. Laboratory results are manually reviewed without a structured alert system, and there is no Clinical Decision Support System (CDSS) in place to prioritize urgent cases (Sierra et al., 2021).

Staff members often follow non-standardized workflows, creating inconsistencies in quality and timing of care. These inefficiencies are particularly problematic in regenerative treatments—such as platelet-rich plasma (PRP) therapies, stem cell infusions, and hormonal optimization—where treatment success depends heavily on rapid and precise diagnostic interpretation.

Proposed Strategy

The proposed intervention is the implementation of a standardized diagnostic intake process combined with a CDSS. This addresses current inefficiencies such as late lab interpretation, inconsistent workflows, and lack of structured decision-making.

Key Components of the Strategy

1. Standardized Intake and Digital Documentation

   • Nursing staff and providers will be trained to use structured intake templates that ensure complete patient histories, identification of red flags, and baseline assessments.

   • Patient intake forms will be digitized and directly uploaded into the EHR, reducing transcription errors and enhancing accessibility of data.

2. Integration of CDSS into EHR

   • The CDSS will flag abnormal results, provide evidence-based recommendations tailored to regenerative medicine, and send alerts for urgent cases (Khalil et al., 2025).

   • Alerts will be specific to regenerative indicators such as abnormal hormone panels, cytokine elevations, or nutritional deficiencies.

3. Workflow Redesign and Interprofessional Huddles

   • Daily or weekly clinical huddles will review CDSS alerts and track laboratory trends, ensuring timely interventions for regenerative readiness (e.g., PRP, stem cell infusions).

   • IT professionals will oversee smooth integration of CDSS with minimal disruptions to ongoing workflows (Klein, 2025).

Table 1

Current Challenges vs. Proposed Strategy

Current Challenge	Impact	Proposed Strategy	Expected Benefit
Paper-based intake	Data loss, delays	Digital intake integrated with EHR	Faster, more accurate documentation
Manual lab review	Missed abnormalities	Automated CDSS alerts	Timely detection of critical values
No prioritization	Delayed interventions	Standardized workflow + alerts	Streamlined, faster care
Fragmented communication	Variability in care	Interprofessional huddles	Consistent, collaborative decision-making

Impact on Quality, Safety, and Cost

The adoption of standardized diagnostic intake and CDSS technology will significantly improve quality, safety, and cost-efficiency.

• Quality: Consistent documentation and automated clinical guidance reduce diagnostic errors and omissions, ensuring evidence-based decisions. This is particularly important in regenerative medicine where conditions involve complex cellular and hormonal processes (Ghasroldasht et al., 2022).

• Safety: Automatic alerts flag critical abnormalities such as cytokine surges or endocrine dysfunction, preventing treatment complications. Shared dashboards enhance interprofessional communication, reducing the risk of missed handoffs (White et al., 2023).

• Cost: Early detection of abnormalities reduces emergency admissions and unnecessary testing. While initial investment in CDSS and training is required, the long-term financial savings are significant. Preventive detection could save thousands per avoided acute episode.

Role of Technology

The core technological innovation is the integration of CDSS into the EHR. This provides real-time analysis, flags abnormal values, and supports evidence-based regenerative care protocols (Derksen et al., 2025).

Benefits of this integration include:

• Reduced cognitive burden on providers.

• Prevention of duplicate testing and unnecessary costs.

• Improved communication across disciplines via shared dashboards.

• Analytics tracking to identify workflow bottlenecks and improve efficiency.

By automating routine processes and supporting clinical reasoning, technology ensures that regenerative treatments like bioidentical hormone therapy, peptide therapy, and PRP injections are initiated at the right time with the highest likelihood of success (Hermerén, 2021).

Implementation at Practicum Site

A phased approach will be used for implementation.

1. Pilot Testing:

   • A small team of providers will trial the standardized intake and CDSS integration.

   • Feedback will be collected to refine workflows.

2. Training & Change Management:

   • Interactive training sessions and continuing education credits will support staff adoption.

   • Clinical leadership will advocate for the benefits of the system to reduce resistance (Ghasroldasht et al., 2022).

3. Addressing Challenges:

   • Resistance to change: Managed through communication, peer champions, and training.

   • Financial constraints: Explore grants, phased licensing, and academic partnerships.

   • Technical challenges: Engage IT early to ensure smooth EHR-CDSS integration (Makhni & Hennekes, 2023).

Interprofessional Collaboration

Collaboration across disciplines is essential for successful implementation.

• Nurses & Nurse Practitioners: Conduct standardized intake, identify red flags, and ensure accuracy in documentation.

• Physicians: Validate regenerative-specific diagnostic criteria and align them with individualized treatment plans.

• IT Staff: Integrate CDSS with EHR, customize dashboards, and troubleshoot technical issues.

• Administrative Staff: Manage scheduling, training logistics, and compliance monitoring (Hermerén, 2021).

Table 2

Roles in Implementation

Team Member	Role in Strategy	Contribution to Regenerative Care
Nurses & NPs	Intake & early assessment	Identify red flags, support timely planning
Physicians	Oversight & treatment alignment	Guide precision-based interventions
IT Staff	CDSS-EHR integration	Ensure technical reliability
Admin Staff	Training & workflow coordination	Support staff adoption and compliance

Conclusion

The proposed intervention—standardized diagnostic intake supported by a CDSS—offers a systematic solution to diagnostic delays in regenerative medicine. By improving quality, safety, and cost-effectiveness, the strategy supports The Longevity Center’s mission of personalized, high-tech patient care. Successful adoption will depend on phased implementation, interprofessional collaboration, and effective leadership from BSN-prepared nurses, ensuring evidence-based practice drives sustainable clinical change.

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

NURS FPX 4905 Assessment 4 Intervention Proposal

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

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), 127. https://doi.org/10.1186/s12912-025-03272-w

Klein, N. J. (2025). Patient blood management through electronic health record [EHR] optimization. In Advances in Patient Safety and Clinical Informatics (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

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

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