Smart Healthcare and Value Co-creation: The Service Science Perspective to Healthcare Quality Improvements
Bibliographic record
Abstract
This paper aims to identify efficient paths for improving healthcare service quality by adopting the theoretical framework of Service Science based on value co-creation and smart healthcare. Through a literary review, the links between healthcare quality, patient satisfaction and value co-creation are identified and analyzed from a smart healthcare perspective. Theoretical implications are tested through the analysis of the Lazio region case in Italy. The results make it possible to identify the possible positive effects on the quality of the health service deriving from the application of new management logics based on sharing and co-creating services between health professionals and patients by using the latest digital technologies. This work suggests the adoption of new logics, practices and technological tools that allow health managers to design experiences and services able to satisfy patients' different needs while benefiting from them through "win-win" solutions, able to create a higher value for all the actors involved in the healthcare process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".