Combining Integration of Care and a Population Health Approach: A Scoping Review of Redesign Strategies and Interventions, and their Impact
Bibliographic record
Abstract
BACKGROUND AND AIM: Many health systems attempt to develop integrated and population health-oriented systems of care, but knowledge of strategies and interventions to support this effort is lacking. We aimed to identify specific redesign strategies and interventions, and to present evidence of their effectiveness. METHOD: A modified scoping review process was carried out. Fifteen relevant examples of integrated care organizations that incorporated a broad population health approach in countries of the Organization for Economic Cooperation and Development described in 57 articles and reports were included in analysis. RESULTS: Seven key redesign strategies and multiple redesign interventions have been identified and are described. Most commonly used redesign strategies included focusing on health and wellness, embracing intersectoral action and partnerships, addressing health in vulnerable groups, and addressing a wide range of determinants of health, including making improvements in health services. Redesign interventions included creative and innovative ways of addressing clinical and non-clinical issues such as establishing housing surgeries in primary care, establlishing vast social and provider networks to support patients with complex needs and also broadening of the scope of services, workforce redesign and other. Potential reductions in the utilization of care and costs could be derived by the wider adoption of these strategies and interventions. CONCLUSION: Development of integrated and population health-oriented systems of care requires the redesign of how services are organized and delivered, and how organizations and care systems operate. Combining integration of care with the population health approach can be supported by a set of cohesive strategies and interventions aimed at preventing disease, addressing social determinants of health and improving health equity at both population- and individual-level.
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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 | 0.104 | 0.214 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.035 | 0.032 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".