Workflow Orchestration for SMO Products

Data Characteristics for SMO Products

SMO (Site Management Organization) product data originates from the daily operations of clinical trial sites. This includes investigator information, subject recruitment and management data, ethics approval documents, study protocols, Case Report Forms (CRFs), training records, quality control documents, and financial settlement information. Data updates frequently, especially during clinical trials, with subject follow-up data and adverse event reports updating in real-time or daily. Document structures vary, encompassing structured data (e.g., database records), semi-structured data (e.g., XML reports), and unstructured data (e.g., PDF ethics approvals, investigator brochures, image-based medical records). Fields and units are highly specialized, such as "Subject ID," "Visit Date," "Vital Signs (blood pressure in mmHg, heart rate in beats/min)," and "Drug Dosage (mg)," often involving medical terminology and abbreviations.

Constraints Imposed by Data Characteristics on Workflow Orchestration

The highly specialized nature and diverse document structures of SMO product data impose specific requirements on workflow orchestration. For example, real-time subject follow-up data requires data extraction nodes in the workflow to support high-frequency triggers and incremental update capabilities. Unstructured documents, such as PDF ethics approvals, require OCR capabilities or intelligent parsing nodes to extract key information. Data from multiple heterogeneous sources makes data integration and cleaning critical steps in the workflow, necessitating data transformation and validation nodes to ensure data consistency and accuracy. Furthermore, many fields involve specialized medical terminology, demanding high semantic understanding and entity recognition capabilities, which affects the accuracy of knowledge base construction and question-answering retrieval. Workflows must flexibly handle different data types and update frequencies, and support complex business logic, such as triggering subsequent approval processes or alert notifications based on specific events.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`4000 token`	Accommodates typical SMO consultation scenarios with multi-turn conversation history and relevant knowledge snippets, balancing cost and performance.
`Chunk size` (Segment Length)	`500 characters` (characters)	Adapts to lengthy SMO documents like study protocols and ethics approvals, ensuring semantic completeness.
`Recall count` (Recall Count)	`Top 8 entries` (top 8)	Covers multiple potential knowledge points in SMO queries, improving recall rate.
`Similarity threshold` (Similarity Threshold)	`0.78`	Filters out irrelevant recall results, enhancing answer accuracy and reducing noise interference.
`Rerank result count` (Rerank Return Count)	`Top 3 entries` (top 3)	Selects the most relevant items from recall results for direct answer generation, preventing the model from processing excessive redundant information.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Addresses parsing requirements for large PDF documents (e.g., investigator brochures), preventing processing failures due to timeouts.

Common Pitfalls

Form input nodes fail to correctly reference variables, resulting in empty default values or incorrect assignments. This occurs due to misunderstandings of variable scope or mismatches in variable naming and referencing.
Key information extraction is incomplete or incorrect after document upload. This happens when there is no customized pre-processing or OCR configuration for SMO-specific document formats (e.g., scanned CRFs, complex tables).
DB node query results are not parsed as expected, leading to subsequent node processing failures. This occurs when the JSON or JSON array structure returned by the DB node is not correctly recognized, or subsequent code nodes lack corresponding parsing logic.

Verification of Configuration

Conduct end-to-end testing using typical SMO consultation questions covering various data types (structured, unstructured) and business scenarios. Verify that the workflow runs stably and provides correct answers.
Review workflow logs to confirm that the input and output of each node (e.g., data extraction, cleaning, knowledge base query, large model generation) meet expectations, especially whether error handling branches trigger as designed.
Compare workflow-generated answers with standard answers. Evaluate information completeness, accuracy, and language fluency, paying particular attention to the correct expression of medical terminology and specialized data. Adjust Similarity threshold (Similarity Threshold) and Rerank result count (Rerank Return Count) based on evaluation results.

Note: 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.