Knowledge Base Retrieval and Recall for Real-World Evidence

Data Characteristics in this Domain

Real-World Evidence (RWE) data primarily originates from Electronic Health Records (EHRs), insurance claims databases, patient registries, wearable devices, and mobile applications. This data is highly heterogeneous and often unstructured or semi-structured. Update frequencies vary from daily (e.g., inpatient data) to quarterly or annually (e.g., aggregated insurance claims), and are generally less standardized than Randomized Controlled Trial (RCT) data. Document formats are diverse, including physician handwritten notes, imaging reports, lab results, medication records, and follow-up notes. Fields and units are complex; for example, diagnoses may use ICD codes, and medications may use ATC codes, but dosage and frequency information often exist as free text with inconsistent units. For instance, weight might be recorded in kg or lbs, and drug concentrations in ng/mL or μg/dL, requiring standardization.

Constraints Imposed by these Characteristics on Knowledge Base Retrieval and Recall

The heterogeneity and unstructured nature of RWE data make traditional structured queries ineffective for comprehensive information retrieval. The diversity of document formats requires the knowledge base to handle various file types and extract key information from free text. Inconsistent update frequencies mean the knowledge base needs to support incremental updates and version management to ensure retrieval timeliness. Non-standardized fields and units result in low recall rates for direct matching or exact retrieval, necessitating stronger semantic understanding and fuzzy matching capabilities. Clinical trial pre-screening demands high accuracy and recall. The inherent noise and missing values in RWE data directly impact retrieval quality, requiring more complex pre-processing and post-processing logic, such as entity recognition, relation extraction, and unit normalization.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size`	`500–800 characters`	RWE texts have high information density; shorter segments help preserve context and prevent critical information truncation.
`Chunk overlap`	`100–150 characters`	Ensures semantic continuity between adjacent segments, improving recall for cross-segment information retrieval.
`Similarity threshold`	`0.75–0.85`	Balances recall and precision, preventing the retrieval of too many irrelevant results while ensuring sensitive information is recalled.
`Recall count`	`10–20 entries`	Given the complexity of RWE data, increasing the number of recalled items can improve coverage and provide more candidates for subsequent re-ranking.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	RWE documents often contain large amounts of unstructured content; parsing can be time-consuming. Extending the timeout prevents parsing failures.
`Embedding Model`	Calibrate through testing	Select a model that performs well on relevant corpora, specifically for biomedical terminology and context.

Common Pitfalls

Retrieval results do not reflect the latest data after a knowledge base update, recalling outdated information. This occurs because the knowledge base index is not fully refreshed or the cache is not invalidated.
Retrieval results contain numerous irrelevant or duplicate snippets, obscuring effective information. This typically results from an improper segmentation strategy or a similarity threshold set too low.
Some critical entities or numerical information cannot be accurately identified and recalled, leading to missing important details in query results. This may stem from insufficient entity recognition and unit normalization during pre-processing.

How to Verify Configuration

Select a set of test queries with typical RWE data characteristics. Observe the completeness and relevance of the recalled results, comparing them against a human-annotated gold standard.
For RWE documents from different sources and formats, test the success rate of file upload and parsing. Check logs for errors such as PARSE_FILE_FAILED or FILE_SIZE_EXCEEDED.
Monitor the execution status of knowledge base update tasks. Confirm that incremental data is indexed at the expected frequency and verify that new data is discoverable in retrieval.
Through A/B testing or canary releases, collect user feedback on retrieval result satisfaction. Adjust parameters such as Similarity threshold and Recall count based on feedback.

Note: The values provided are common starting points and should be measured 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.