Deployment and Upgrade for SMO Clinical Trial Pre-screening

Data Characteristics

SMO (Site Management Organization) clinical trial pre-screening primarily handles patient recruitment information, medical history records, initial physical examination reports, laboratory test results, and informed consent forms. Data sources are diverse. These include structured data (e.g., CSV, XML) exported from Hospital Information Systems (HIS) and Electronic Medical Record (EMR) systems, as well as scanned paper documents (e.g., PDF lab reports, handwritten doctor's notes). Data updates frequently, especially during patient screening cycles, with new test results or disease progression potentially appearing daily. Document structures often contain extensive medical terminology. Field names can vary across hospitals or laboratories (e.g., "WBC" or "White Blood Cell Count" for white blood cell count). Units may also differ (e.g., "mmol/L" vs. "mg/dL").

Constraints on Deployment and Upgrade

The variety of SMO data sources requires robust multi-format file parsing capabilities, especially for identifying and extracting structured data from unstructured PDF documents. High update frequency necessitates an efficient incremental update mechanism for the knowledge base, avoiding resource consumption from full rebuilds. The specialized nature of medical terminology and inconsistent field names demand advanced embedding models and retrieval strategies. Models must understand semantic differences and perform effective normalization. Furthermore, data may contain sensitive patient privacy information. Deployment must strictly adhere to data security and compliance requirements, ensuring data isolation and access control. During upgrades, consider new version compatibility with existing data indexes. Plan for smooth migration of old configurations and models. This prevents data parsing failures or abnormal query results due to upgrades.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Accommodates large PDFs containing images or scanned documents.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Prevents timeouts when parsing large or complex PDF documents.
`maxContext`	`1024 Token`	Ensures completeness of critical contextual information in medical reports, improving retrieval accuracy.
`Chunk size`	`500 characters`	Adapts to the paragraph structure of medical texts, maintaining semantic coherence.
`Similarity threshold`	`0.75`	Precisely matches patient medical history with trial inclusion/exclusion criteria, reducing misjudgments.
`Recall count`	`Top 10 entries`	Covers a broader range of relevant information, providing sufficient candidates for subsequent reranking.

Common Pitfalls

Uploading large PDF files returns an HTTP 413 Payload Too Large error. This happens when UPLOAD_FILE_MAX_SIZE is too small for the actual file size.
After a knowledge base update, some patient laboratory test results are not correctly identified, appearing as empty key fields. This is due to changes in the file parser's logic for specific PDF table formats in the new version. Adjust parsing rules or update the parsing model.
After a version upgrade, previously functional retrieval queries return irrelevant results. This occurs because the embedding model or index structure changed with the upgrade, making old vector data incompatible with the new model. Rebuild the index or recompute vectors.

Verification of Configuration

Upload files of various formats (PDF, CSV, TXT) and sizes. Check if all parse and ingest successfully. This confirms UPLOAD_FILE_MAX_SIZE and PARSE_FILE_TIMEOUT_SECONDS are effective.
Randomly select a batch of documents containing complex medical terminology. Query for key information within them. Compare retrieval results with the original documents. Evaluate if Chunk size and Similarity threshold accurately capture semantics.
After an upgrade, use typical query statements validated before the upgrade. Observe the accuracy and consistency of query results. This ensures the new version's compatibility with existing data.

Note: The values provided are common starting points. Measure 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.