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NURS FPX 4905 Assessment 4 Intervention Proposal

nurs fpx 4905 assessment 4

Student Name

Capella University

NURS-FPX4905 Capstone Project for Nursing

Prof. Name

Date

Intervention Proposal

The Longevity Center is a specialized healthcare facility focusing on regenerative medicine. Its services include hormone therapy, preventive care, and advanced diagnostics tailored to patients seeking proactive, personalized wellness solutions. A major challenge observed at the site involves diagnostic delays, particularly in complex cases where early identification is essential for successful treatment outcomes (Sierra et al., 2021). This proposal introduces a structured intervention strategy aimed at minimizing diagnostic delays by utilizing workflow redesign and health information technology.

Identification of the Practice Issue

Delays in diagnosis are common when patients present with multiple symptoms that lack clear clinical pathways. Such delays significantly hinder treatment planning, particularly in regenerative medicine, where prompt identification of conditions like hormonal imbalances, micronutrient deficiencies, or autoimmune triggers determines the success of therapies such as stem cell infusions, peptide protocols, or bioidentical hormone replacement (Sierra et al., 2021).

Investigations into current practices revealed that the delays stem primarily from:

Problem IdentifiedImpact
Fragmented communication among staffProlonged result interpretation
Lack of prioritization protocolsMissed or late intervention
No structured system for urgent labsReduced effectiveness of regenerative therapies

Timely and structured diagnostics are therefore critical for enhancing patient outcomes.

Current Practice

At present, The Longevity Center operates with paper-based intake forms and manual transfer of data into the Electronic Health Record (EHR). This outdated process raises the risk of information loss and diagnostic delays. Laboratory results are reviewed manually without a systematic alert mechanism for abnormal findings, and there is no Clinical Decision Support System (CDSS) in place to streamline prioritization (Sierra et al., 2021).

Additionally, staff members follow non-standardized workflows, creating inconsistencies in care. This is particularly concerning in regenerative medicine, where therapies like platelet-rich plasma (PRP) injections and hormonal optimization rely heavily on precise and timely diagnostic input.

Proposed Strategy

To address the identified gaps, the proposal suggests introducing:

  1. A standardized diagnostic intake system
  2. Integration of a CDSS within the EHR

This approach directly addresses the issues of intake variability, delayed lab interpretation, and unstructured decision-making (Wolfien et al., 2023).

Key Components of the Strategy

InterventionDetailsExpected Benefit
Digital intake documentationFull capture of patient history, red flags, and baseline data entered into EHRStreamlined patient information flow
CDSS integrationAutomated flagging of abnormal results with evidence-based recommendationsFaster, safer diagnostics
Staff trainingEducation on standardized workflows and digital intake proceduresImproved adherence and consistency
Interprofessional huddlesRegular discussions on CDSS alerts and lab resultsEnhanced communication and teamwork

This strategy assumes that staff will be adequately trained, technology will be gradually adopted, and communication will be optimized to support patient-centered regenerative care (Klein, 2025).

Impact on Quality, Safety, and Cost

The intervention is expected to significantly improve the quality, safety, and cost-effectiveness of care at The Longevity Center.

Anticipated Outcomes

DimensionImpact of InterventionExamples in Regenerative Care
QualityStandardized documentation and CDSS support ensure accurate diagnosesPRP readiness, hormone level assessments
SafetyAlerts for critical lab values reduce missed diagnosesAbnormal cytokine or hormone imbalance detection
CostPrevents unnecessary testing and emergency interventionsSavings of \$100–\$500 per test and up to \$15,000 per acute episode

By addressing diagnostic delays, patient satisfaction is expected to rise due to timely, individualized care plans.

Role of Technology

Technology is central to this plan, specifically through CDSS integration within the EHR. This innovation provides real-time alerts, differential diagnosis support, and automated follow-up reminders (Derksen et al., 2025).

How Technology Improves Care

  • Data Integration: Consolidates lab values and patient histories into one system.
  • Error Reduction: Reduces cognitive overload and human error.
  • Collaboration: Shared dashboards enhance interdisciplinary communication.
  • Analytics: Tracks diagnostic patterns to improve long-term workflows.

This system ensures that regenerative therapies like stem cell treatments, PRP, and hormone optimization are supported with accurate, evidence-based diagnostic data.

Implementation at Practicum Site

Implementation requires a phased approach to minimize disruption.

Phases of Implementation

PhaseActivityPurpose
PilotSmall-scale rollout of standardized intake and CDSSTest workflows and gather feedback
TrainingInteractive sessions with staffBuild confidence and competence
ExpansionClinic-wide adoptionEnsure consistency in regenerative care
EvaluationMonitor outcomes, safety, and efficiencyRefine and sustain improvements

Challenges such as staff resistance and budget constraints may arise. These can be addressed through leadership support, external grants, and IT partnerships (Makhni & Hennekes, 2023).

Interprofessional Collaboration

The success of this strategy depends on team-based collaboration.

Team Roles

Team MemberRole in Intervention
PhysiciansDefine diagnostic criteria and oversee treatment pathways
Nurse Practitioners/NursesStandardize intake processes and ensure accurate patient histories
IT SpecialistsCustomize and integrate CDSS within EHR
Administrative StaffCoordinate training and monitor compliance

Daily huddles supported by a shared dashboard will further enhance communication, ensuring accurate decision-making in regenerative therapies (Hermerén, 2021).

Conclusion

This intervention, centered on standardized intake and CDSS integration, aims to eliminate diagnostic delays, enhance safety, and reduce costs at The Longevity Center. With a phased rollout, interprofessional teamwork, and technological support, this strategy aligns with evidence-based practice and reflects the BSN nurse’s role in leading sustainable healthcare improvements.

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), 1–17. https://doi.org/10.3390/ijms23052850

NURS FPX 4905 Assessment 4 Intervention Proposal

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), 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 Naturehttps://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

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

Cowan, R., Harland, L., & Yusof, M. M. (2024). Improving diagnostic pathways using digital health innovations: A systematic review. BMC Health Services Research, 24(1), 1–18. https://doi.org/10.1186/s12913-024-10872-9

NURS FPX 4905 Assessment 4 Intervention Proposal

Greenhalgh, T., Rosen, R., & Shaw, S. (2021). Digital transformation in healthcare: Lessons from the COVID-19 pandemic. BMJ, 372, n110. https://doi.org/10.1136/bmj.n110

Jones, D., Murphy, K., & McCormack, B. (2022). Nursing leadership in implementing evidence-based practice: Overcoming barriers to change. Journal of Nursing Management, 30(5), 1085–1093. https://doi.org/10.1111/jonm.13562

Lester, C. A., Patel, V., & Frankel, A. (2020). Clinical decision support systems and diagnostic safety: Opportunities for improving patient outcomes. BMJ Quality & Safety, 29(4), 279–286. https://doi.org/10.1136/bmjqs-2019-009754

Topol, E. J. (2023). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 29(3), 437–445. https://doi.org/10.1038/s41591-023-02242-6

Vawdrey, D. K., & Hripcsak, G. (2024). Reducing diagnostic errors through EHR optimization and decision support. Journal of the American Medical Informatics Association, 31(1), 45–53. https://doi.org/10.1093/jamia/ocad089

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