Rethinking Healthcare Performance Evaluation Systems towards the People-Centredness Approach: Their Pathways, their Experience, their Evaluation
Bibliographic record
Abstract
Patient experience should be the starting point to achieve a high quality of care. Coherently, healthcare performance evaluation systems, driving the change in line with the main strategic goals, should be designed considering the patient perspective. Instead, they are traditionally defined according to the healthcare service provider's point of view. Consequently, they reproduce a "silo-vision" characterized by a clear separation of responsibilities limited to a specific setting of care or to a single organization. This commentary discusses the importance of using patient-reported measures together with indicators based on administrative data to evaluate cross-setting healthcare services within a multidimensional healthcare performance evaluation system. The experience of the Tuscany regional healthcare Performance Measurement System (PMS), implemented more than 10 years ago and in continuous evolution, represents an innovative example of how to measure the quality of the whole care pathway including patient experience. This new approach is based on a systematic, systemic and standardized collection of patient-reported experience measures in several healthcare pathways and evaluating them using a coherent graphical representation. Targets, incentives and other managerial tools are fixed, overcoming organizational boundaries and integrating the patient point of view with the goal of moving the healthcare system towards a patient-centredness approach to care.
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 | 0.442 | 0.420 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.005 | 0.043 |
| Scholarly communication | 0.051 | 0.050 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.008 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 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".