Technical and Architectural Issues in Deploying Electronic Health Records (EHRs) over the WWW
Bibliographic record
Abstract
In this paper technical and architectural issues are described in deploying electronic health records (EHRs) over the WWW. The project described involved deployment of EHRs that have been designed to serve in the education of health professionals and health/biomedical informaticians. In order to allow for ubiquitous access to a range of EHRs remotely an architecture was designed with three layers: (a) the "Internet" or remote user access layer (2) the "Perimeter Network", or middle firewall security and authentication layer (3) the "HINF EHR Network", consisting of the internal servers hosting EHR applications and databases. The approaches allow for a large number of remote users running a range of operating systems to access the educational EHRs from any location remotely. Virtual machine (VM) technology is employed to allow multiple versions and platforms of operating systems to be installed side-by-side on a single server. Security, technical and budgetary considerations are described as well as past and current applications of the architecture for a number of projects for the education of health professionals in the area of electronic health records.
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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".