An Agent-based Knowledge Management Framework for Electronic Health Record Interoperability
Bibliographic record
Abstract
In recent years, the dramatic increase in the use of information technology for healthcare has resulted in much innovative research on eHealth applications. But it has been widely acknowledged that unlocking the real value in clinical records is highly dependent upon health information standards that allow interoperability between various clinical systems, supporting the easy exchange of records among stakeholders in the patients circle of care. This paper proposes a software agent based virtual integration framework to integrate multiple electronic health record (EHR) systems from distributed (possibly heterogeneous) databases, featuring three properties. First, a loose coupling between EHR formats and software engineering of the application allows the agent based framework to be flexible for on-line reconfiguration and deployment. Second, the framework is designed with a knowledge base that supports both medical practitioners and consumers, by managing healthcare information at a higher knowledge-based level. Third, the framework integrates distributed databases as well as adaptive user interfaces to support personalized health information systems, which can be used by a range of users with differing requirements. We believe that this agent-based technical framework also demonstrates a new direction for handling other eHealth interoperability issues such as the development of personal health record systems, as well as providing a technical foundation for developing clinical decision support systems.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".