Data Characteristics in this Category
CDMO (Contract Development and Manufacturing Organization) clinical trial pre-screening data primarily originates from sponsor-provided raw clinical protocols, investigator brochures, subject inclusion/exclusion criteria, historical study data, and internal LIMS (Laboratory Information Management System) data. This data typically includes unstructured documents (e.g., clinical protocols in PDF format, investigator brochures in Word format) and semi-structured data (e.g., laboratory results, case report forms in Excel or CSV format). Data updates are frequent during the initial project setup phase, with bulk updates triggered by protocol revisions. Document structures are complex, containing extensive specialized terminology and abbreviations. Fields and units are specific to the biomedical domain, such as biochemical units like "ng/mL" and "IU/L," and specific disease diagnostic criteria fields like ECOG_Performance_Status and NYHA_Class.
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The diversity and complexity of CDMO clinical trial pre-screening data impose specific requirements on workflow orchestration. Unstructured documents require prior OCR recognition and content parsing to extract key information. Semi-structured data needs flexible data cleaning and standardization steps, especially to handle inconsistent units, missing data, or format errors. Specialized terminology and abbreviations demand that AI models within the workflow possess strong domain knowledge understanding; prompt design must account for accurate recognition of professional vocabulary. The non-periodic nature of data updates means the workflow must support manual triggers and incremental update mechanisms to avoid re-processing already parsed data. Furthermore, due to sensitive clinical data involvement, data anonymization and access control steps must be embedded in the workflow to ensure compliance.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800 characters | Clinical documents have strong contextual relevance; small segments risk semantic loss, while overly large segments increase AI processing burden. |
overlapSize | 100 characters | Ensures contextual continuity, especially when extracting key information across paragraphs. |
maxContext | 16384 tokens | Processes longer clinical protocols and investigator brochures, ensuring sufficient input length for the AI model. |
similarityThreshold | 0.75 | Clinical pre-screening requires high accuracy in information recall to avoid misjudgment. |
maxRetrieve | 10 items | Recalls enough relevant document snippets to provide comprehensive context for AI questioning. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Clinical documents are often large and complex, requiring more parsing time, preventing interruptions due to timeouts. |
Three Common Pitfalls
- The AI Q&A node does not receive the complete output from an upstream text concatenation node: This usually occurs because the input field of the AI Q&A node is not correctly mapped to the output variable of the text concatenation node, preventing the variable from being passed correctly.
- During an API call, the variable part in text concatenation or specified responses is not returned: This phenomenon may be due to the API request body not including or incorrectly passing the context variables required by the workflow, preventing the backend from populating them correctly.
- The frontend crashes during concurrent workflow execution, but backend resources are normal: This may relate to the stability of frontend WebSocket connections or state management under high concurrency, or frontend rendering logic not adequately accounting for concurrent request response handling.
How to Verify Configuration
- Use workflow debugging mode to observe the output of each node, especially text concatenation and AI Q&A nodes, ensuring data flows as expected and content is complete.
- Perform end-to-end testing with different types of clinical documents (PDF, Word, Excel) to verify the accuracy of OCR recognition, data extraction, and AI Q&A.
- Simulate high-concurrency scenarios by repeatedly executing the workflow via API calls. Check system response times, error logs, and the consistency of final output results to ensure system stability.
The values provided are common starting points and should be measured against specific 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.