DESIGN OF DECOUPLED CLINICAL DECISION SUPPORT FOR SERVICE-ORIENTED ARCHITECTURES
Bibliographic record
Abstract
Computer-based clinical decision support (CDS) contributes to cost savings, increased patient safety and quality of medical care. Most existing CDS systems are stand-alone products (first generation) or part of complete electronic medical record packages (second generation). Experience shows that creating and maintaining CDS systems is expensive and requires effort that should be economized by sharing them among multiple users. It makes good economic sense to share CDS service installations among a larger set of client systems. The paradigm of a service-oriented architecture (SOA) embraces this idea of sharing distributed services. Some attempts making CDS services available to distributed health information systems exist. However, these approaches have not gained much adoption. We argue that they do not provide a sufficient level of decoupling between client and CDS in order to be broadly reusable in SOAs. In this paper, we present a new CDS service component called EGADSS, which has been designed and implemented with the declared objective to minimize the coupling between client and CDS server. We present our key design decisions, which are guided by empirical research in SOA development. We evaluate our result theoretically by measuring the level of decoupling achieved compared to existing CDS approaches. Furthermore, we report on an empirical evaluation of the resulting design, integrating the EGADSS service with an example client system.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".