Article citationsMore>>

Zhang, L., Wang, Y., Zhao, M., Sun, W., Chen, X., Li, J., et al. (2024) Derivation and Validation of a Prediction Model for Unplanned ICU Transfer after Thoracic Surgery. Chest, 166, 1120-1128.

has been cited by the following article:

• TITLE: Innovations in Nursing-Driven Bed Resource Optimization for Thoracic Oncology Patients: An Evidence-Based Practice Review

  AUTHORS: Baowen Huang

  KEYWORDS: Thoracic Oncology, Bed Resource Allocation, Nursing Risk Stratification, IoT in Healthcare, Value-Based Nursing

  JOURNAL NAME: Open Journal of Nursing, Vol.15 No.7, July 29, 2025

  ABSTRACT: Background: Structural inefficiencies in bed allocation for thoracic oncology patients remain a global challenge. This systematic review synthesizes evidence on nurse-led innovations addressing this issue. Methods: Following PRISMA 2020 guidelines, we searched PubMed, CNKI, and Wanfang databases (January 2020-March 2025). Quality Appraisal: Two independent reviewers assessed methodological quality using Joanna Briggs Institute (JBI) tools for analytical studies and ROBIS for systematic reviews. Discrepancies were resolved through consensus. 28/32 studies (87.5%) met ≥6 JBI criteria, with confounding control being the most frequent limitation. ROBIS indicated low bias risk for 3 systematic reviews. Inclusion criteria covered peer-reviewed studies in English/Chinese reporting quantitative outcomes. Of 1372 initial records, 32 studies met selection criteria after dual screening. Full inclusion criteria: 1) Thoracic oncology focus; 2) Nurse-led bed management intervention; 3) Reported occupancy/transfer/readmission metrics; 4) Controlled before-after or RCT design; 5) ≥6-month implementation. Results: Four evidence-based innovations demonstrated significant impact: 1) Cross-Departmental Admission Criteria Scale reduced unplanned ICU transfers by 27% (95% CI: 22.1 - 31.9; P Conclusions: Nursing-led bed optimization requires multidimensional integration of risk assessment protocols, technology-enabled surveillance, and policy-supported reimbursement mechanisms.