Enhancing Primary Care Delivery: A Comprehensive Review of Collaboration among Multidisciplinary Teams
Bibliographic record
Abstract
Enhancing primary care delivery increasingly depends on the strength of collaboration among multidisciplinary teams (MDTs), especially as patient needs grow more complex and chronic diseases become more prevalent. This review synthesizes contemporary evidence on how coordinated teamwork among physicians, nurses, pharmacists, allied health professionals, social workers, and care coordinators improves the accessibility, safety, and efficiency of primary care. A structured search across major scientific databases identified empirical studies published between 2016 and 2025 examining team-based models, collaborative mechanisms, and resulting clinical and organizational outcomes. Findings show that MDT collaboration significantly enhances chronic disease management, medication optimization, patient education, and preventive care delivery. Patients benefit from better continuity, improved satisfaction, and greater self-management capacity, while healthcare organizations experience reduced fragmentation, fewer unnecessary hospital visits, and more efficient resource utilization. However, the review also reveals persistent challenges, including role ambiguity, communication gaps, variable leadership structures, and limited health information integration. Overall, the evidence supports MDT collaboration as a foundational driver of high-quality, patient-centered primary care, provided that systems invest in clear governance structures, interoperable digital tools, and continuous interprofessional training. Strengthening these collaborative mechanisms is essential for achieving resilient, integrated, and sustainable primary care models worldwide.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".