Making available Clinical Decision Support in Service-Oriented Architectures
Bibliographic record
Abstract
Computer-based clinical decision support (CDS) has great potential for cost savings and for increasing patient safety and quality of care. The cost of owning and particularly maintaining CDS systems is significant. Therefore, it makes good economic sense to share a CDS service installation among a larger set of client systems. The emerging paradigm of serviceoriented architectures (SOAs) embraces the idea of sharing and interaction between loosely coupled, co-operative services. Canada has based its planned architecture for realizing the electronic medical record (EMR) on the SOA paradigm. While CDS components are currently not in the set of services to be constructed for Canada’s health information infrastructure, they seems to be growing interest in adding them in the future, after the more essential services have been implemented. In this paper, we discuss the status of clinical decision support systems today and some challenges of making them available in SOA-based infrastructures. We report on design choices and solutions we have selected during the construction of the EGADSS (Electronic Guideline and Decision Support System) component. Our design decisions are based on domainspecific challenges such as knowledge, data and workflow interoperability as well as on technical considerations about construction high quality services for SOA-based infrastructures. EGADSS has been released under open-source license and is freely available.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".