Integrated chronic disease management in primary-secondary care : a systematic literature review of the evidence
Bibliographic record
Abstract
Introduction: Diabetes has traditionally been managed as a single chronic disease state, but it exists with co-morbidities such as depression and metabolic syndrome. Treatment is multifaceted, requiring both primary and secondary care, however, the delivery of diabetes care is often fragmented. Integrated chronic disease management is a growing model of interest, and is underpinned by the chronic care model (CCM), devised as a guide for primary care management of patients with chronic conditions. The model identifies six key elements for effective care, and has shown promise in improving the management of diabetes. Aim: To find empirical evidence of integrated care interventions targeted at co-morbidities including diabetes, across primary/secondary care. Method: A systematic review of peer reviewed literature from PubMed, CINAHL, Embase, Cochrane Library and Joanna Briggs was performed. Studies were reviewed according to inclusion criteria- studies published in English, between 2004-2014, empirical studies, studies with evidence of primary/secondary implementation, and those dealing with chronic co-morbid disease states. Results: 51 studies met the inclusion criteria. Included studies were mostly from the US (38), with five from Australia, UK (2), Canada (2), Netherlands (1), Norway (1), Ireland (1), and one multi-country study. It was found that all interventions adopted at least one (average 3-4) of the chronic care model, with the majority implementing delivery system redesign activities within the primary care practice/s. We found evidence of interventions which significantly reduced emergency department and hospital admissions, improved processes of care, patient health outcomes such as HbA1c, improved patient satisfaction, and reduced costs. Conclusion/Implications for practice: Diabetes exists as a co-morbid disease, requiring both primary and secondary care. We found that integrated care interventions adopting elements of the chronic care model positively impacted on patient outcomes, service utilisation, as well as costs. This review has highlighted that it may not be necessary to adopt all CCM elements to improve clinical outcomes, patient satisfaction and costs.
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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 | 0.016 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".