Model Access and Configuration for Real-World Evidence

Data Characteristics in This Category

Real-world evidence (RWE) data for registration and submission documents come from diverse and heterogeneous sources. These include electronic health records (EHR), medical insurance claims databases, disease registries, adverse drug reaction reporting systems, and some wearable device data. Data update frequencies vary from real-time (e.g., some adverse reaction reports) to quarterly or annually (e.g., large medical insurance databases). Document structures are complex, containing both structured data (e.g., diagnostic codes, medication records) and extensive unstructured text (e.g., handwritten clinical notes, radiology report descriptions). Fields and units differ significantly; for example, drug dosages may be expressed in milligrams or milliliters, time units range from seconds to years, and disease severity assessments often include qualitative descriptions and numerical scores. Data volumes are typically large, with numerous missing values, outliers, and inconsistent terminology.

Constraints Imposed by These Characteristics on "Model Access and Configuration"

The heterogeneity and complexity of real-world evidence data impose specific requirements on model access and configuration. The large volume and multi-source nature of the data demand robust ETL (Extract, Transform, Load) capabilities during the data preprocessing phase. This addresses varying data formats and encoding standards, and enables effective deduplication and integration. Frequently updated data sources necessitate incremental learning or periodic retraining mechanisms to maintain knowledge base timeliness. The presence of unstructured text makes the selection of Natural Language Processing (NLP) models critical, especially models capable of processing medical terminology, abbreviations, and unique expressions found in medical records. Non-standardized fields and units mean that knowledge graph construction or vector database indexing requires more refined entity recognition and standardization. For example, unifying different dosage units ensures the model accurately understands and compares information. Furthermore, missing values and noise in the data require configuring appropriate filtering and imputation strategies to prevent low-quality data from negatively impacting model performance.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`maxContext`	`8192` tokens	Real-world medical records are often long; sufficient context window captures complete information.
`Chunk size` (Segment Length)	`800–1200` characters	Balances semantic completeness with recall efficiency, prevents context loss from over-segmentation.
`Recall count` (Recall Count)	`Top 10–15` items	Real-world data associations are complex; increasing recall probability of hitting relevant information.
`Similarity threshold` (Similarity Threshold)	`0.75–0.82`	Registration and submission require high precision; filters low-relevance results while ensuring recall.
`PARSE_FILE_TIMEOUT_SECONDS`	`600` seconds	Processing large EHR or medical insurance data files can be time-consuming; prevents parsing timeouts.
`Rerank result count` (Reranked Return Count)	`Top 5` items	Reranking more accurately presents the most relevant information, improving final result quality.

Three Common Pitfalls

A 401 Unauthorized error during model testing typically indicates an incorrect or expired API_KEY configuration.
After uploading a large real-world research dataset, empty or incomplete query results may occur if PARSE_FILE_TIMEOUT_SECONDS is set too short, preventing full file parsing.
Frequent misunderstandings of medical terminology in model responses usually result from not selecting a language model pre-trained for the medical domain or a lack of domain-specific terminology in the knowledge base.

How to Confirm Proper Configuration

Upload typical real-world medical records and clinical trial reports. Check that file parsing status is normal, with no timeouts or errors.
Pose complex questions involving medical terminology for specific diseases or drugs. Verify the model's ability to accurately understand and recall relevant passages from the knowledge base.
Randomly select multiple RWE data samples. Test the model's ability to extract and standardize key fields (e.g., dosage, diagnosis). Compare results against manual verification.

The values provided are common starting points. Measure performance against your own data samples to determine optimal settings.

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.