Global Adoption of Value-Based Health Care Initiatives Within Health Systems
Bibliographic record
Abstract
Importance: Health systems worldwide are facing several contextual challenges threatening their sustainability, including aging populations with complex health care needs, workforce shortages, and persistent health disparities, which are driving health care costs. Optimizing health systems to respond to contextual challenges and offer quality care for all requires innovative frameworks like value-based health care (VBHC) and high-value health systems (HVHS) frameworks that focus on improving patient outcomes while minimizing costs. Objective: To examine how value-based initiatives have been introduced in health systems worldwide. Evidence Review: A comprehensive literature search was conducted across MEDLINE/PubMed, Embase, Health Business Elite, and Web of Science Core Collection. The search included controlled vocabulary terms relevant to VBHC and covered publications between January 1, 2007, and July 7, 2023. After title and abstract screening, followed by full-text review, experimental, observational, and case studies that examined the implementation of the VBHC framework or its elements were included. Articles that focused solely on insurance, cost-effectiveness analysis, theoretical models without implementation, nonempirical studies (eg, reviews, commentaries), and gray literature (eg, news articles) were excluded. Findings: Of 11 948 articles initially identified for potential inclusion, the final sample included 50 initiatives, with 47 from high-income countries, 2 from upper-middle-income countries, and 1 from a lower-middle-income country. The review revealed that VBHC adoption remains confined to the departmental or institutional level, with few examples of systemwide or national implementation. Although many initiatives integrated various elements of the VBHC framework and components of the HVHS model, none achieved full implementation of all aspects. Conclusions and Relevance: This scoping review showed that since its formal introduction in 2006, VBHC has been widely recognized as a strategy for improving health system performance, but large-scale adoption will require a strategic shift toward integrating value-based components at national and regional levels. These findings highlight the need for research on effective implementation models, particularly in lower-resource settings, to guide policymakers and health system leaders in scaling VBHC and transitioning toward HVHS.
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 | 0.027 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".