Understanding value-based healthcare – an interview study with project team members at a Swedish university hospital
Bibliographic record
Abstract
The purpose of this study is to explore how representatives from four project teams understand the concept of value-based healthcare (VBHC), since each representative is responsible for one of the pilot projects implementing VBHC at a university hospital in Sweden. A qualitative design was used to gain understanding of VBHC. Open-ended interviews were used as the data-collection method and content analysis of the transcribed interviews was carried out. Participants’ understanding of VBHC focused on how value was created for the patient and on measuring medical outcomes and costs, although costs were to some extent put aside. To measure value for the patients, it was the health professionals’ perspective about what patient should value that dominated the understanding of the concept VBHC. VBHC was understood as a strategy to strengthen value innovations and to loosen the grip of economic control. Benchmarking was seen as a future possibility to develop value innovations. Changes in organizational culture were understood by participants as a need to change healthcare from being professional-centred to patient-centred. The way the concept was understood omits parts of the original concept. This has implications for whether or not the concept as it is described by the participants should be understood as VBHC according to the intentions of the strategy described. The development of outcome measures was predominantly based on the health professionals’ experiences, which is why the patients’ perspective needs to be strengthened. Further studies of the process of implementing VBHC are needed.
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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 | 0.032 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".