Opportunities and Risks of Terminological Ambiguity: The Perception of Value in Value-Based Healthcare – A Multi-Method Study
Bibliographic record
Abstract
Abstract BackgroundValue-based healthcare (VBHC) argues healthcare needs to be refocused to maximize value creation, defining value as the value quota (VQ) of outcomes important for the patient divided by the cost of the care. Value is central to the VBHC concept but could be an ambiguous term for professionals wanting to adopt the concept in an implementation process. We set out to explore the perception of value amongst different stakeholders who implement VBHC.Methods The perception of value, cost and VBHC was analysed using content analysis of semi-structured interviews from 19 clinicians and non-clinicians involved in implementation of VBHC. In addition, we exemplified the value quota (VQ) with data from a clinical trial to exemplify the possible association between patient reported outcome measurements (Kansas City Cardiomyopathy Questionnaire and the EQ5D ), their perception of care (n=248) and cost. ResultsClinicians described value as a dynamic concept dependent on the patient and the clinical setting, stating that improving outcomes was more important than containing costs. Value for non-clinicians appeared more driven by the interplay between the outcome and cost or resources. The quantitative data suggested a poor association between patients’ perception of value and VQ. ConclusionsOur findings indicate that there is great variation in how different stakeholders (clinicians, non-clinicians) perceive the key concept of value when implementing VBHC. The most dominant influence was the voice of clinicians, focusing on increasing treatment efficacy and improving medical outcomes but having a limited focus on cost and what matters to patients. Moreover, patients’ own perception of value provided during a care period was poorly connected to the calculated value quota. If the concept of value is defined primarly by clinicians’ own assumptions, there is a clear risk that history will simply be repeated and the need for innovation will not be met. A single-minded focus on value” could therefore result in missing the target. The patients’ and non-clinicians’ perception of value must also be integrated with the clinical perception, if VBHC is going to deliver on the promise to increase healthcare efficiency and effectiveness.
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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 | 0.079 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".