Can GRID services provide answers to the challenges of national health information sharing
Bibliographic record
Abstract
It has been widely recognized that one of the keys to cost reduction and service improvement in national health care lies in the integration of medical information system. Integration of information can not only improve care delivery today, but it can also help build research bases to enhance future care delivery. The question is how to achieve such integration? Imposing a single client software solution or common clinical terminology does not appear likely to happen. That lack of single software solutions and common terminologies means that solutions to integrate medical information will have to use, and somehow solve, the many heterogeneous data sources that currently exist. We believe the GRID computing paradigm has the potential to provide viable solutions to this problem. In collaboration with researchers in Health Information Science and practitioners in Health Care, we have developed a prototype for the Health Information Grid, a middleware technology that supports inexpensive mediation of medical information among rapidly evolving, heterogeneous medical information sources. This paper makes three main contributions: (1) it discusses issues and requirements arising from constructing a global health information network in Canada from a software engineering point of view, (2) it presents the concepts of a technology developed to address these issues, and (3) it presents a prototype of this technology along with a case study developed in collaboration with domain experts and practitioners.
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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.007 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".