Data Characteristics in This Category
CRO regulatory submission document preparation involves a wide range of data types. These primarily include clinical trial protocols, research reports, raw data, statistical analysis reports, ethics approvals, and informed consent forms. This data often exists in multiple formats, such as PDF, Word, Excel, and scanned images. Clinical trial data is continuously generated and updated. Phased and final reports have specific deadlines. Document structures adhere to strict regulatory guidelines from bodies like ICH GCP and NMPA, exhibiting high standardization and templating, for example, CRF forms and SAE reports. Fields and units strictly follow medical and pharmaceutical professional standards, such as drug dosage units (mg/kg), time units (days, weeks, months), and biological indicator units (mmol/L, U/L). Accuracy and consistency requirements are extremely high.
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The data characteristics of CRO regulatory submissions impose specific requirements on workflow orchestration. First, multi-format documents require robust file parsing and content extraction capabilities, especially accurate OCR recognition for scanned PDFs. Second, high update frequency and phased reports necessitate workflow support for incremental updates and version management to ensure processing of the latest data. Strict document structures and standardized fields mean the workflow must precisely identify and extract specific information, such as adverse event (AE) information from clinical trial reports or P-values from statistical analysis reports. The rigor of medical professional fields and units demands that workflows correctly understand and apply professional terminology and measurement units during data validation, knowledge base construction, and LLM prompt design. This prevents information deviation due to unit confusion or terminology misunderstanding. Furthermore, compliance requirements dictate that workflows must be traceable and meet audit standards during data processing and information generation.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 500–800 characters (characters) | Regulatory submission documents are professional and information-dense. Shorter chunks help maintain contextual relevance and prevent key information from being truncated. |
Overlap Size | 50–100 characters (characters) | Ensures sufficient overlap between chunks to handle professional terms and compound concepts that span across chunks, enhancing RAG recall completeness. |
Recall count (Recall Count) | Top 5–8 entries (top 5–8 items) | Retrieval for regulatory submission documents typically requires high precision. Recalling more relevant information helps the LLM make comprehensive judgments and reduces omissions. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | The medical and pharmaceutical fields demand extremely high information accuracy. A higher similarity threshold filters out irrelevant document segments, improving recall quality. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Regulatory submission documents contain many large PDFs and scanned files, which can take a long time to parse. Sufficient timeout duration is necessary. |
maxContext | 32k tokens | Complex medical reports and analysis results often require the LLM to understand a longer context to generate accurate summaries or analyses. Increasing the context window is beneficial. |
Three Common Mistakes
- When processing scanned documents, OCR recognition results in numerous typos or garbled characters, leading to subsequent information extraction failures. This usually occurs because the OCR engine has insufficient recognition capabilities for low-quality scanned documents or is not optimized for specific medical terminology.
- The workflow encounters an
HTTP 400 Bad Requesterror when calling an API, and logs show that the knowledge base ID or document ID in the request parameters is empty. This often happens when parameters are not passed correctly or dynamic acquisition fails when the workflow references a knowledge base or document variable. - LLM-generated content shows deviations in professional terminology or measurement units, for example, incorrectly writing
mg/kgasg/kg, or confusing "adverse event" with "serious adverse event." This typically occurs because the LLM lacks sufficient domain-specific training or the prompt does not explicitly emphasize the accuracy requirements for professional terminology, leading to an inability to precisely distinguish subtle semantic differences.
How to Confirm Correct Configuration
- Select a typical regulatory submission document containing various data types (PDF, Word, Excel, scanned files). Execute the workflow and check if all documents are successfully parsed and indexed by the knowledge base.
- For a specific question, such as "What are the most common adverse events for a certain drug in clinical trials?", query the workflow. Check if the LLM's returned results are accurate, complete, and reference the correct original document segments.
- Select several documents containing key professional fields (e.g., dosage, frequency, P-value). Extract these fields via the workflow and compare them with the original documents to confirm the accuracy of the extraction results and the consistency of units.
- Simulate an incremental data update by uploading a new version of a clinical trial report. Verify if the workflow can detect the update and correctly process the new and old versions of the data.
The values given are common starting points and should be measured against the reader's 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.