Standard-based data and service interoperability in eHealth systems
Bibliographic record
Abstract
International standardization in information representation, organization, and dissemination are meant to eliminate the discrepancies in communication among participating organizations and institutions in a particular domain. The management of domain information will then allow different participants to integrate their legacy information or application servers to a nation-wide network and use widely approved services to communicate their proprietary data and services with a large group of clients. In this context, traditional healthcare information systems require fundamental re-engineering to new network-centric environments in order to reduce the huge costs of healthcare while maintaining the expected quality of public health. This integration using new HL7 v3 standards and leading-edge information technologies will be the initial steps for shifting towards an interoperable healthcare environment. This paper aims at addressing new challenges in standard-based interoperability provision among legacy healthcare information systems, while adhering to international and national standards for data and service representations. We introduce a framework to employ healthcare standards and clinical terminology systems to achieve semantic interoperability between distributed electronic medical record (EMR) systems. A real world case study for integration of a Clinical Decision Support System (CDSS) with the EMR of a specialist will be presented.
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 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 | 0.038 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| 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".