Can GRID services provide answers to the challenges of national health information sharing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it