Workflow Orchestration for Smart Triage Products

Data Characteristics for Smart Triage Products

Smart triage products primarily use data from biomedical public literature, clinical guidelines, drug inserts, disease diagnostic standards, and professional medical databases. This data updates frequently. New drug approvals, clinical trial results, and revised treatment protocols can significantly change the data. Document structures often combine structured and unstructured information. Examples include ICD-10 codes for diseases, symptom descriptions, differential diagnoses, treatment plans, drug ingredients, indications, contraindications, and adverse effects. Fields involve medical terminology, drug dosage units (e.g., mg, ml), time units (e.g., days, weeks), and numerical ranges for various clinical indicators. The data volume is large and highly specialized. This requires high precision in knowledge representation and retrieval.

Constraints from Data Characteristics on Workflow Orchestration

High-frequency data updates require flexible data synchronization and knowledge base update mechanisms in workflows. This ensures the timeliness of triage information. The mix of structured and unstructured data means knowledge bases must support multimodal data indexing. Workflows need to combine structured queries with semantic matching during retrieval. Complex and ambiguous medical terminology demands more from Natural Language Understanding (NLU) components in workflows. Contextual understanding and terminology standardization are necessary to reduce ambiguity. Precise numerical values for drug dosages and clinical indicators limit the applicability of fuzzy matching. Workflows must perform exact numerical comparisons and range checks. User-entered symptom descriptions may be non-standard. Workflows need preprocessing steps for medical term recognition and standardization to effectively match the knowledge base.

Configuration Settings

Configuration Item	Recommended Value	Rationale for Recommendation
`maxContext`	`3000 tokens`	Ensures sufficient contextual information for complex cases, preventing loss of critical details.
`Recall count` (Recall Count)	`Top 15 entries` (Top 15)	Given the rigor of medical information, increasing recall count improves the hit rate of relevant documents.
`Similarity threshold` (Similarity Threshold)	`0.78`	A higher threshold helps filter for highly relevant professional medical knowledge, reducing the risk of misdiagnosis.
`Rerank result count` (Reranked Return Count)	`Top 5 entries` (Top 5)	After a large recall, precise reranking prioritizes the most relevant diagnostic or treatment suggestions.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (600 seconds)	Allows ample file parsing time for large medical literature or clinical guidelines.
`Knowledge Base Variable`	`knowledgeSearch`	Allows dynamic passing of knowledge base IDs, adapting to knowledge base switching for different disease or drug consultation scenarios.

Common Mistakes

Workflows do not immediately reference the latest data after a knowledge base update. This leads to triage results based on outdated information. This occurs when the knowledge base synchronization mechanism is not decoupled from workflow execution or when synchronization frequency is set improperly.
The system returns "no relevant documents found" or a generic answer after a user enters symptoms. This happens when the workflow fails to correctly parse medical terms, or when the knowledge base search's Similarity threshold (Similarity Threshold) is too high, filtering out potentially relevant but differently expressed documents.
The knowledge base ID is passed incorrectly or not at all during API calls to the workflow. This prevents the workflow from accessing specific knowledge bases. This occurs when the Knowledge Base Variable configuration does not match API call parameters, or when global variable settings are not effective.

Verifying Configuration

Select typical disease and drug consultation scenarios. Input symptoms or questions using different phrasing. Check if triage results are accurate, comprehensive, and reference the latest medical literature or guidelines.
Simulate a knowledge base update. Immediately execute relevant queries. Verify if the system's diagnostic suggestions or drug information reflect the latest data.
Use workflow logs or the debugging interface. Check if the Recall count (Recall Count), Similarity threshold (Similarity Threshold), and Rerank result count (Reranked Return Count) for each knowledge base query match the expected configuration. Validate the source and content of the referenced documents.

The values provided are common starting points. Measure them against your own samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.