Effective Multidisciplinary Antibiotic Stewardship: Integrating Laboratory Antimicrobial Resistance Analysis, Nursing Management Protocols, and Administrative Governance to Reduce Hospital-Acquired Infections
Bibliographic record
Abstract
Background: The global proliferation of multidrug-resistant organisms (MDROs) has precipitated a crisis in modern healthcare, threatening to undermine the foundations of infection management. Hospital-acquired infections (HAIs) constitute a severe complication of inpatient care, affecting between 5% and 15% of hospitalized patients worldwide, with prevalence rising significantly in intensive care units (ICUs) and resource-limited settings. The conventional standard of care, characterized by vertical, single-discipline Antibiotic Stewardship Programs (ASPs) typically led by infectious disease physicians or clinical pharmacists, has achieved optimization in pharmacy procurement but has struggled to arrest the transmission of complex resistant pathogens such as Carbapenem-resistant Enterobacterales (CRE) and Candida auris. These traditional models often function in isolation, failing to integrate the critical "frontend" capabilities of bedside nursing and the diagnostic intelligence of the microbiology laboratory. Consequently, the Multidisciplinary Collaborative Management Model—a holistic framework integrating real-time Laboratory Antimicrobial Resistance Analysis, empowered Nursing Management Protocols, and robust Administrative Governance—has emerged as a promising alternative to address these systemic gaps. Objective: The primary objective of this systematic review is to comprehensively evaluate and compare the effectiveness of the Multidisciplinary Collaborative Management Model versus Standard Single-Discipline Stewardship in reducing the incidence of HAIs and optimizing antimicrobial utilization among adult inpatients in acute care settings globally. The review specifically aims to quantify the impact on MDRO detection rates, antimicrobial consumption metrics, and patient-centered outcomes including mortality and length of stay. Methods: A systematic review was conducted in strict adherence to the PRISMA 2020 guidelines. A comprehensive search strategy was executed across major bibliographic databases including PubMed, Embase, CINAHL, and Scopus, targeting literature published between 2010 and 2025. The review employed a rigorous PICO framework: Population (adult inpatients), Intervention (integrated multidisciplinary stewardship), Comparison (standard care/siloed ASP), and Outcomes (MDRO incidence, antibiotic consumption, mortality). Inclusion criteria encompassed randomized controlled trials (RCTs), quasi-experimental pre-post studies, and prospective cohorts. Risk of bias was assessed using the Cochrane Risk of Bias tool (RoB 2.0) for trials and the Newcastle-Ottawa Scale (NOS) for observational studies. Data were synthesized using a narrative approach complemented by tabulated quantitative comparisons. Results: The review identified 37 studies meeting the inclusion criteria, encompassing data from over 3,000 participants across diverse healthcare settings including China, the United Arab Emirates, Europe, and Sub-Saharan Africa. The synthesis of evidence indicates a superior efficacy of the multidisciplinary model. Primary outcome analysis revealed that integrated interventions reduced the overall MDRO detection rate from 60.1% to 52.5% in high-prevalence settings, with specific reductions in Carbapenem-resistant Klebsiella pneumoniae (CRKP) of nearly 9%. Antimicrobial consumption, measured in Defined Daily Doses (DDDs), decreased significantly, with one large-scale study reporting a reduction in Antibiotic Use Density (AUD) from 50.15 to 35.76 DDDs per 100 patient-days. Secondary outcomes demonstrated a profound clinical impact: the integration of rapid diagnostic tests with stewardship teams reduced the time to optimal therapy by approximately 29 hours and was associated with a 28% reduction in mortality odds (OR 0.72). Nursing-led protocols significantly improved compliance with de-escalation strategies and infection prevention bundles, although sustainability remained a challenge without administrative backing. Conclusion: The Multidisciplinary Collaborative Management Model represents a significant advancement over standard stewardship approaches. By effectively coupling the diagnostic precision of the laboratory with the continuous surveillance of bedside nursing and the enforcement power of administrative governance, healthcare facilities can achieve substantial reductions in both antimicrobial resistance and HAI incidence. The findings suggest that future clinical practice must dismantle disciplinary silos in favor of integrated governance structures. Future research should prioritize the economic analysis of these interventions in low-resource settings and explore the role of automated digital surveillance in sustaining compliance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".