Addressing overuse of health services in health systems: a critical interpretive synthesis
Bibliographic record
Abstract
BACKGROUND: Health systems are increasingly focusing on the issue of 'overuse' of health services and how to address it. We developed a framework focused on (1) the rationale and context for health systems prioritising addressing overuse, (2) elements of a comprehensive process and approach to reduce overuse and (3) implementation considerations for addressing overuse. METHODS: We conducted a critical interpretive synthesis informed by a stakeholder-engagement process. The synthesis identified relevant empirical and non-empirical articles about system-level overuse. Two reviewers independently screened records, assessed for inclusion and conceptually mapped included articles. From these, we selected a purposive sample, created structured summaries of key findings and thematically synthesised the results. RESULTS: Our search identified 3545 references, from which we included 251. Most articles (76%; n = 192) were published within 5 years of conducting the review and addressed processes for addressing overuse (63%; n = 158) or political and health system context (60%; n = 151). Besides negative outcomes at the patient, system and global level, there were various contextual factors to addressing service overuse that seem to be key issue drivers. Processes for addressing overuse can be grouped into three elements comprising a comprehensive approach, including (1) approaches to identify overused health services, (2) stakeholder- or patient-led approaches and (3) government-led initiatives. Key implementation considerations include the need to develop 'buy in' from stakeholders and citizens. CONCLUSIONS: Health systems want to ensure the use of high-value services to keep citizens healthy and avoid harm. Our synthesis can be used by policy-makers, stakeholders and researchers to understand how the issue has been prioritised, what approaches have been used to address it and implementation considerations. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42014013204 .
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.292 | 0.449 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.041 | 0.027 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.024 | 0.018 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".