MétaCan
Menu
Back to cohort
Record W2981182346 · doi:10.5430/ijba.v10n6p1

Smart Healthcare and Value Co-creation: The Service Science Perspective to Healthcare Quality Improvements

2019· article· en· W2981182346 on OpenAlexvenueno aff
Giuseppe Russo, Andrea Moretta Tartaglione, Ylenia Cavacece

Bibliographic record

VenueInternational Journal of Business Administration · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careKnowledge managementService (business)Healthcare serviceValue (mathematics)Quality (philosophy)Work (physics)BusinessPerspective (graphical)Process (computing)Co-creationComputer scienceProcess managementMarketingEngineeringEconomics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.828
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.370
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Business AdministrationSame topicService and Product InnovationFrench-language works237,207