Governance of Cross-Organizational Healthcare Document Exchange through Watermarking Services and Alerts
Bibliographic record
Abstract
There is an increasing demand for sharing documents for process integration among organizations. Web services technology has recently been widely proposed and gradually adopted as a platform for supporting such an integration. There are no holistic solutions thus far that are able to tackle the various protection issues, specifically regarding the security and privacy protection requirements in cross-organizational progress integration. This paper proposes the exchange of documents through a Document / Image Exchange Platform (DIEP), replacing traditional ad-hoc and manual exchange practices. The authors show how the contemporary technologies of Web services under a Service-Oriented Architecture (SOA), together with watermarking, can help protect document exchanges with layered implementation architecture. Furthermore, to facilitate governance and regulation compliance against protection policy violation attempts, the management and the affected parties are notified with alerts for warning and possible handling. The authors discuss the applicability of the proposed platform with a physician towards security and privacy protection requirements based on the Health Insurance Portability and Accountability Act (HIPAA) in the United States, which imposes national regulations to protect individuals’ healthcare information. The proposed approach aims at facilitating the whole governance process from technical to management level with a single unified platform.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".