Health on a Cloud: Modeling Digital Flows in an E-health Ecosystem
Bibliographic record
Abstract
A unified and well-knit e-health network is one that provides a common platform to its key stakeholders to facilitate a sharing of information with a view to promoting cooperation and maximizing benefits. A promising candidate worthy of being considered for this ponderous job is the emerging ‘cloud technology’ with its offer of computing as a utility, which seems well-suited to foster such a network bringing together diverse players who would otherwise remain fragmented and be unable to reap benefits that accrue from cooperation. The e-health network serves to provide added value to its various stakeholders through syndication, aggregation and distribution of this health information, thereby reducing costs and improving efficiencies. Because such a network is in fact an interconnected ‘network of networks’ that delivers a product or service through both competition and cooperation, it can be thought of as a business ecosystem. . This study attempts to model the digital information flows in an e-health ecosystem and analyze the resulting strategic implications for the key players for whom the rules of the game are bound to change given their interdependent added-values. The ADVISOR framework is deployed to examine the values created and captured in the ecosystem. Based on this analysis, some critical questions that must be addressed as necessary preconditions for an e-Health Cloud, are derived. The paper concludes with the conjecture that “collaboration for value” will replace “competition for revenue” as the new axiom in the health care business that could ideally usher in a fair, efficient and sustainable ecosystem.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".