Data Characteristics in This Category
Quality documents in biomedical clinical trial pre-screening include study protocols, informed consent forms, ethics approvals, investigator brochures, case report form (CRF) templates, and Standard Operating Procedures (SOPs). These documents are typically unstructured, existing as PDFs, Word files, or scanned images. Key information, such as investigational drug names, indications, inclusion/exclusion criteria, dosages, administration routes, follow-up plans, and adverse event reporting procedures, may be embedded as tables or specific fields within these documents. Sponsors, Contract Research Organizations (CROs), or internal research center systems are the primary data sources. Updates are infrequent, occurring mainly when clinical trial protocols are revised, ethical reviews are approved, or SOPs are updated. Document structures are highly standardized, but field values are diverse. For example, inclusion/exclusion criteria may involve medical terminology, numerical ranges, and logical conditions.
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The unstructured nature of quality documents requires powerful document parsing capabilities within the workflow. This ensures accurate extraction of key information from PDFs or Word files, such as inclusion/exclusion criteria text from clinical trial protocols or specific operational steps from SOPs. Since document updates are infrequent, the data source pulling mechanism in the workflow should not be high-frequency. On-demand or periodic checks (e.g., monthly) for updates are more appropriate. The specialized nature of document content, particularly medical terminology and numerical ranges, demands higher accuracy from information extraction models. This may necessitate customized entity recognition models or enhanced dictionary matching. Furthermore, logical connections between documents, such as ensuring informed consent forms align with study protocols, require comparison or validation steps within the workflow to maintain information consistency and compliance.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Quality documents are often large and contain complex charts and text. A longer parsing time prevents parsing failures due to timeouts. |
maxContext | 8000 characters | Documents like clinical trial protocols are information-dense. A larger context window is needed to capture key information completely, such as full descriptions of inclusion/exclusion criteria. |
Chunk size (Segment Length) | 1000–1200 characters | This ensures a single segment can contain a complete logical unit, such as an entire SOP step or a specific description of an inclusion/exclusion criterion. |
Similarity threshold (Similarity Threshold) | 0.78 | Increasing the similarity threshold ensures that recall results are highly relevant to the query intent, reducing irrelevant or ambiguous matches, especially when comparing medical terms. |
Recall count (Number of Retrieved Items) | Top 8 | Quality document content is rigorous. Increasing the number of retrieved items helps cover more comprehensive relevant information, providing sufficient basis for pre-screening. |
Rerank result count (Number of Reranked Items) | Top 5 | After retrieval, reranking further optimizes the results, ensuring that the most relevant core information is presented first, improving engineer review efficiency. |
Three Common Pitfalls
- The workflow starts but remains unresponsive for an extended period or throws an error
Cannot read properties of undefined, manifesting as a frozen interface. This may occur if the document parser runs out of memory or times out when processing large files, preventing subsequent components from obtaining the expected data objects. - Key fields (e.g., numerical ranges in inclusion/exclusion criteria) are extracted as empty or inaccurately. This happens when document structures are complex or text expressions are diverse, and general parsing models fail to effectively identify specific formats of medical information.
- Updated documents do not trigger the workflow, or updated information is not reflected in downstream components. This can be due to improper configuration of file monitoring or data source synchronization components, such as incorrect file path settings or disabled periodic checks.
How to Verify Correct Configuration
- Upload a typical clinical trial protocol PDF file. Check if the workflow successfully parses and extracts key information like investigational drugs, indications, and primary inclusion/exclusion criteria. Verify the accuracy of the extracted content.
- Modify an SOP document and re-upload it. Observe if the workflow is triggered and confirm if the SOP version number or revision date is updated in downstream components.
- For a CRF template containing complex tables, verify if the workflow accurately identifies and extracts table field names and their expected value ranges, such as dosage units or visit time points.
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.