Workflow Orchestration for Phase I Clinical Trial

Data Characteristics

Data sources for Phase I clinical trial registration dossiers primarily include study protocols, ethics committee approvals, informed consent forms (ICFs), case report forms (CRFs), laboratory test reports, imaging reports, adverse event reports, data management plans and reports, and statistical analysis plans and reports. Data types are mainly structured text (e.g., CRF data), semi-structured text (e.g., laboratory reports, imaging reports), and unstructured text (e.g., ICFs, protocol amendment history). Data update frequency is high, especially during an ongoing study, with continuous generation of CRF data and adverse event reports. Document structures typically adhere to ICH GCP guidelines and national drug regulatory authority guidelines, such as the CTD format. Fields and units are highly specialized. For example, dose units are commonly mg/kg or μg/mL, and time units are hours, days, or weeks. The data often involves medical terminology, abbreviations, and coding systems (e.g., MedDRA, WHODRUG).

Constraints Imposed by Data Characteristics on Workflow Orchestration

The multi-source and heterogeneous nature of Phase I clinical data requires robust document parsing capabilities during the data ingestion phase. The workflow must handle various formats like PDF, Word, and Excel, accurately identifying and extracting key information. High data update frequency necessitates support for incremental updates and version management to ensure the real-time accuracy of the knowledge base content. Strict document structures and specialized fields demand high accuracy in information extraction and entity recognition, requiring customized named entity recognition models or rules. The presence of specialized terminology and coding systems means the semantic understanding module in the workflow must integrate medical dictionaries or ontologies for precise knowledge matching and inference. Furthermore, due to the regulatory requirements for submission dossiers, the workflow's output must generate structured documents according to predefined templates and perform compliance validation.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk size` (Chunk Length)	800–1200 characters	Phase I clinical documents are often long, specialized texts. Maintaining contextual completeness aids semantic understanding.
`Overlap Length`	100–200 characters	Ensures the relevance of key information across paragraphs, reducing information loss.
`Recall count` (Recall Count)	Top 5–8	Covers a sufficient number of relevant clinical data snippets while maintaining response speed.
`Similarity threshold` (Similarity Threshold)	0.75–0.85	Balances recall precision and generalization ability, preventing interference from irrelevant information.
`Rerank result count` (Reranked Return Count)	Top 3	After reranking, focuses on the most relevant few pieces of information, improving the quality of the final answer.
`PARSE_FILE_TIMEOUT_SECONDS`	600 seconds	Addresses potentially long parsing times for large PDF or Word documents.

Common Pitfalls

Document parsing failure or timeout, with logs showing "File Content Parsing Exception" (file content parsing exception). This usually occurs because scanned PDFs have insufficient clarity or files contain complex charts that the parser cannot recognize.
Generated question classification results do not match expectations. For example, "drug interaction" is classified as "adverse event." This happens when the classification model's training data is insufficient to cover the specific detailed classification standards or medical terminology unique to Phase I clinical trials.
Information retrieved from the knowledge base lacks critical numerical values or units, appearing as "dose not mentioned" or "time unit unclear" in the answer. This is because the text extraction node failed to correctly match regular expressions for medical professional fields when processing semi-structured data.

How to Verify Configuration

Select Phase I clinical documents of varying types and complexity (e.g., protocols, CRFs, laboratory reports). Upload each document and check its parsing results to ensure key information (e.g., drug names, dosages, study endpoints) is accurately extracted.
Test the workflow with typical registration submission questions. Compare the output answers with the original document content to evaluate the completeness and accuracy of key facts, data, and conclusions.
Simulate potential queries in actual submission scenarios. Check whether the workflow can consistently recall high-quality relevant knowledge snippets when handling ambiguous queries or queries with multiple combined conditions. Adjust the Recall count (Recall Count) and Similarity threshold (Similarity Threshold) based on actual recall performance.

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.