## 2 Materials and Methods
### System Architecture
#### 2.1.2 Workflow Integration  
All the above components work in concert as follows for a typical triage scenario:  
1. **Low-latency Data Capture. ** A patient arrives at the emergency department. The nurse records initial observations and vitals in the EHR. The Data Ingestion Layer immediately streams these new entries into the CDSS pipeline. \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**FAIR Step**} & \multicolumn{1}{c}{**Implementation Detail**} \\ \hline Persistent identifiers & DOI minted via hospital-hosted DataCite node \\ \hline Machine-readable metadata & FHIR DocumentReference resources with JSON-LD context \\ \hline Vocabulary alignment & OMTS maps local codes to SNOMED CT concepts \\ \hline License tagging & SPDX license field added to each dataset manifest \\ \hline Provenance capture & W3C PROV statements emitted by ETL jobs \\ \hline Access control & OAuth 2.0 tokens with “purpose-of-use” claims \\ \hline Quality checks & FAIR-Metrics “R1.3” automated via FAIR-Evaluator CI job \\ \hline \hline \end{tabular}
\end{table} 
Table 2: FAIR data-stewardship steps and their concrete implementation within the proposed CDSS. Figure 3: Updated FAIRification workflow for health data. 2. **Preprocessing and Analysis. ** The vitals of the patient are normalized and checked for extremes, and the free-text note is parsed by the LLM layer, which might extract \"chest pain for 2 h, history of hypertension\" as key points. Meanwhile, the Big Data layer retrieves the past records of this patient (if any) and relevant medical knowledge (e.g., known risk factors for chest pain). 3. **Decision Support Generation. ** The DSE takes the structured data (e.g., blood pressure = 90/60, heart rate = 120) and the LLM annotations (e.g., chest pain characteristics) and runs them through its triage model/rules. Suppose the model, based on learned patterns, outputs a high risk score suggesting this is likely a heart attack. The DSE sets the triage recommendation to Level 1 (Immediate/Emergent). It asks the LLM layer to generate an answer in natural language. The LLM might produce: \"Recommendation: Level 1 (Immediate). Rationale: The hypotension, tachycardia, and chest pain characteristics of the patient are indicative of a possible acute coronary syndrome. Similar past cases required immediate intervention.\"
4. **UI Notification. ** Within seconds, the nurse sees an alert on the triage dashboard that shows both the recommended level and the rationale behind it. The nurse reviews the patient and the input of the system. If the recommendation matches the assessment of the nurse, it is accepted and the patient is fast-tracked for treatment, with the decision documented as CDSS-supported. If the nurse judges--based on appearance, repeat vitals, or new history--that the case is urgent but not at the highest resuscitation tier, they can override the suggestion by downgrading the triage category from Level 1 (Immediate) to Level 2 (Emergent) and enter a brief reason (e.g., \"stable vital signs after repeat measurement\"). The system records both the adjusted level and feedback from the nurse for audit and future model refinement. 5. **FL Update (in background). ** At the end of the day (or a federated training cycle), the data from this case (now labeled with the final outcome and decisions) stays in the database of the hospital. The CDSS at this hospital will use it to update the local model when the next training round occurs. FL will ensure the global model gradually learns from such real cases across all deployment sites, constantly refining the triage predictions. The architecture is engineered to bring cutting-edge AI (LLMs and federated deep learning) into a clinically practical and legally compliant CDSS. Its modular design enables each component to evolve independently--for example, allowing future integration of more powerful LLMs or new privacy technologies such as secure enclaves in the federated layer without requiring complete system overhaul.