Toward a Clearer Understanding of Value‐Based Healthcare: A Concept Analysis
Bibliographic record
Abstract
Background: Value‐based healthcare (VBHC) aims to improve the quality of healthcare delivery while reducing costs and also aims for outcomes that are of utmost importance from patients’ perspectives. Despite a growing interest in VBHC, a significant knowledge gap persists within the existing literature in the absence of a clear conceptualization of VBHC itself. Aim: The aim of the present study was to develop a comprehensive understanding of the concept of VBHC in order to arrive at a definition based on the evidence in the existing literature. Method: A concept analysis approach was used to identify the concept’s defining attributes, its antecedents, consequences, and its empirical referents. Results: The analysis of the concept yielded three defining attributes: monetary value of health service, quality of care, and patient‐centered care. The analysis also identified several crucial antecedents for transitioning traditional fee‐for‐service models to those focused on value; it also identified key interrelated consequences: improved patient outcomes, cost reduction, and increased patient satisfaction. Conclusion: The concept analysis of VBHC provides a comprehensive framework for understanding its key components and challenges. By aligning healthcare delivery with the values and needs of patients, VBHC represents a promising avenue toward achieving high‐quality, sustainable healthcare. The findings from this analysis call for a collaborative effort among healthcare leaders, researchers, and policymakers to further refine and implement VBHC models, ensuring healthcare systems are both patient‐centered and cost‐effective. These findings also have implications for nursing management.
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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 | 0.020 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".