Value in Healthcare and the Role of the Patient Voice
Bibliographic record
Abstract
A prevailing feature of recent healthcare delivery and reform initiatives is a focus on increasing the value provided by investment in services, alongside a more nuanced understanding of how such value should be considered. Effective measurement of this value remains an elusive goal for most health system performance assessment (HSPA) systems. A more prominent role for the patient voice can enable a better understanding of value at both patient and population levels. The Tuscan HSPA model has evolved over the past several years by adopting the perspective of service users, including multiple dimensions of performance, and illustrating the interactions of these elements. For the heart failure pathway, this approach has now been further developed to combine these dimensions with the systematic electronic collection of patient-reported outcome measures and patient-reported experience measures - initially in a specialist hospital. This enables a richer understanding of the value delivered by professionals as they operate in reality, as opposed to by organizational boundaries, and more timely and actionable insights into the drivers of that value. This commentary sets out the latest developments in the Tuscan HSPA and the lessons from implementation.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.051 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.015 | 0.014 |
| 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".