Document Parsing and Chunking for Real-World Evidence

Data Characteristics in Real-World Evidence (RWE)

Real-World Evidence (RWE) products integrate diverse data sources. These include Electronic Health Records (EHR), medical insurance claims, patient registries, wearable device data, and various clinical observational study reports. Update frequencies vary from real-time (e.g., some wearable devices) to quarterly or annually (e.g., large medical insurance databases). Document structures are complex. They may contain extensive free-text descriptions, structured tabular data (e.g., patient demographics, diagnoses, medication records), medical imaging reports, and study protocols with statistical analysis reports. Fields involve medical terminology, generic drug names, dosage units, and ICD codes. Unit systems must strictly adhere to medical standards, such as mg/kg, mmol/L, and mmHg.

Constraints from These Characteristics on Document Parsing and Chunking

The diversity of RWE data imposes high demands on document parsing. Medical terms and abbreviations in free text require specialized dictionaries for accurate identification and semantic understanding. Structured tabular data in formats like PDF may suffer from misalignment. The parser must have robust table recognition and reconstruction capabilities. Inconsistent data source update frequencies mean the parsing system needs to support incremental updates and version management. This avoids redundant processing and data duplication. The strictness of fields and units requires the parsing process to extract values and correctly associate them with their corresponding units and context. This prevents misinterpretation due to unit confusion. For example, incorrect parsing of drug dosage units can lead to severe medication errors. Furthermore, RWE documents are typically lengthy and have multi-level chapter structures. This requires a fine-grained chunking strategy for subsequent Retrieval-Augmented Generation (RAG) systems to effectively locate relevant information.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`Chunk size`	`800–1200 characters`	RWE documents typically have high information density. Longer chunks help retain context, reduce information fragmentation, and improve recall accuracy.
`Chunk Overlap Length`	`100–200 characters`	Appropriate overlap ensures critical information across chunks is not lost. This is especially important for medical texts with complex logical relationships.
`Table Parsing Mode`	`Smart Mode`	Prioritize AI-based intelligent table recognition to handle common table misalignment and complex layouts in PDFs.
`Custom Dictionary`	`Enabled，And Import Medical Terminology Dictionary`	Improves the accuracy of identifying medical terminology, drug names, and disease codes, reducing misinterpretation.
`ParsingTimeout`	`600 seconds`	RWE reports, especially large clinical study reports, have large file sizes and complex structures, requiring longer parsing times.
`OCR Enabled`	`Enabled`	Ensures content from scanned or image-based reports can also be recognized and parsed, covering a wider range of data sources.

Common Pitfalls

Table content appears misaligned in previews or during retrieval after document upload. This occurs because PDF table recognition algorithms are not optimized for complex medical report layouts.
Key medical terms or drug names are incorrectly identified when citing document content in conversations. This happens due to missing or outdated custom medical dictionaries.
Large RWE report files experience long response times or parsing failures after upload. This is because file size or parsing complexity exceeds the default PARSE_FILE_TIMEOUT_SECONDS limit.

How to Verify Configuration

Upload a typical RWE report. Check the parsed document preview to ensure table structures are intact and content is not misaligned.
Use queries containing specific medical terms or abbreviations. Verify the accurate identification of these terms in the retrieval results.
Upload multiple RWE files from different sources and in various formats. Observe the completion time and status of parsing tasks to ensure no timeouts or failures.
Cross-reference key fields (e.g., dosage, units) to confirm they are correctly extracted and consistent with the original document content.

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.