Health systems integration: state of the evidence
Bibliographic record
Abstract
INTRODUCTION: Integrated health systems are considered a solution to the challenge of maintaining the accessibility and integrity of healthcare in numerous jurisdictions worldwide. However, decision makers in a Canadian health region indicated they were challenged to find evidence-based information to assist with the planning and implementation of integrated healthcare systems. METHODS: A systematic literature review of peer-reviewed literature from health sciences and business databases, and targeted grey literature sources. RESULTS: Despite the large number of articles discussing integration, significant gaps in the research literature exist. There was a lack of high quality, empirical studies providing evidence on how health systems can improve service delivery and population health. No universal definition or concept of integration was found and multiple integration models from both the healthcare and business literature were proposed in the literature. The review also revealed a lack of standardized, validated tools that have been systematically used to evaluate integration outcomes. This makes measuring and comparing the impact of integration on system, provider and patient level challenging. DISCUSSION AND CONCLUSION: Healthcare is likely too complex for a one-size-fits-all integration solution. It is important for decision makers and planners to choose a set of complementary models, structures and processes to create an integrated health system that fits the needs of the population across the continuum of care. However, in order to have evidence available, decision makers and planners should include evaluation for accountability purposes and to ensure a better understanding of the effectiveness and impact of health systems integration.
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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 | 0.061 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.012 | 0.021 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".