Database and Operations for Real-World Study R&D Document

Data Characteristics

Real-World Study (RWS) R&D document data originates from clinical treatment records, electronic health records, medical claims data, patient registries, wearable device data, and patient-reported outcomes. Update frequencies vary from real-time (e.g., some wearable device data) to quarterly or annually. Document structures are diverse, including unstructured free-text medical records, semi-structured tabular data (e.g., lab results, medication records), and structured diagnostic codes. Fields and units are highly specialized, involving medical terminology, disease codes (e.g., ICD-10), drug dosage units (mg, μg/kg), timestamps (yyyy-MM-dd HH:mm:ss), and biomarker values. Data volumes are typically large and heterogeneous.

Constraints on Database and Operations

The heterogeneous nature of RWS data requires a flexible document model in the database to accommodate free-text and semi-structured data storage, avoiding frequent schema changes. High-frequency data sources (e.g., real-time physiological parameters) demand strong write performance and concurrent processing capabilities from the database. Large data volumes necessitate consideration of storage capacity, indexing strategies, and data sharding to ensure query efficiency. The specialized nature of medical terminology and coding makes the choice of tokenizer and embedding model critical; they must support domain-specific vocabulary recognition. Efficient archiving and querying of historical data, along with data version control, are crucial for tracing research processes and validating results. Operationally, a robust monitoring system is essential to track database connections, storage space, CPU utilization, memory consumption, and query latency, allowing for timely detection and resolution of potential issues.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	RWS documents often include many images or scanned PDFs, leading to large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Converting large PDFs or images to text can be time-consuming, requiring a longer parsing timeout.
`maxContext`	`32000`	RWS reports are detail-rich; a long context window helps capture more related information.
`Chunk size`	`800–1200 characters`	Balances semantic completeness and embedding model processing efficiency, avoiding splitting key medical concepts.
`Recall count`	`Top 10–15 entries`	Ensures sufficient relevant information snippets are covered in complex RWS queries.
`Similarity threshold`	`0.78–0.85`	Requires a higher threshold for precise matching and semantic association of medical terms to ensure recall accuracy.

Common Pitfalls

AI reply interruptions result in incomplete chat history saves to the database. This occurs when the client directly closes the SSE connection, preventing the server from completing the transaction commit.
Docker containers show up, but the service is inaccessible, and logs indicate Mongo connection failures. This often stems from incorrect database addresses or credentials in the MONGO_URI configuration.
Concurrent API calls experience performance bottlenecks or timeouts, manifesting as high response latency or HTTP 504 errors. This can be due to an undersized database connection pool or improper indexing.

Verification Steps

Upload and parse an RWS report PDF containing tables and free text. Check if File Parsing Progress shows completion and if the segmentation results retain key medical information.
Simulate multiple concurrent users querying the RWS knowledge base via API. Monitor CPU Utilization and Memory Consumption to confirm system resources fluctuate within acceptable ranges.
Manually log into the database and inspect the chat collection. Verify that conversation records, including interrupted operations, are fully saved and that the status field correctly reflects the conversation state.
Test the knowledge base's Recall count (Recall Count) and Similarity threshold using queries containing specific disease codes and drug names. Validate that the returned results match expected medical concepts.

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.