Collaborative Chronic Disease Management in Primary and Emergency Care: A Multidisciplinary Approach Integrating Health Administration, Pharmacy, Nursing, General Practice, EMS, Psychology, and Laboratory Services
Bibliographic record
Abstract
Chronic diseases such as diabetes mellitus, cardiovascular diseases, chronic respiratory disorders, and mental health conditions have become the leading causes of death and disability worldwide, accounting for a vast proportion of healthcare utilization and expenditure. Managing these conditions effectively requires long-term, continuous, and comprehensive care that addresses not only the physiological aspects but also the psychosocial and behavioral dimensions of health. The traditional healthcare model, characterized by fragmentation and reactive responses, has proven insufficient for managing the complexity of chronic illnesses. This review article underscores the value of a multidisciplinary and integrated approach that bridges the gap between primary care and emergency services. It examines the roles of diverse healthcare professionals—health administrators, pharmacists, nurses, general practitioners, EMS personnel, psychologists, and laboratory technicians—in delivering coordinated and patient-centered care. Each profession contributes essential expertise, and when functioning cohesively, they form a network capable of providing proactive monitoring, timely interventions, psychosocial support, and data-driven decision-making. Furthermore, the article discusses real-world barriers to integration, such as institutional silos, lack of interoperable health information systems, role ambiguity, and inconsistent interprofessional education. Drawing from international case studies and evidence-based models, it offers practical recommendations to foster collaboration, including policy reforms, the use of shared care plans, interprofessional training programs, and digital integration strategies. In doing so, this review presents a compelling case for a systemic shift toward team-based chronic disease management across healthcare levels, aiming for improved clinical outcomes, enhanced patient experiences, and more sustainable healthcare systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".