Tool Calling and Plugins for Real-World Evidence Products

Real-World Evidence Data Characteristics

Real-World Evidence (RWE) product data originates from Electronic Health Records (EHR), insurance claims databases, disease registries, wearable device data, and patient-reported outcomes (PROs). This data is highly heterogeneous. It includes structured data (e.g., diagnosis codes ICD-10, treatment regimens ATC, lab results) and extensive unstructured text (e.g., clinical notes, imaging reports, genetic sequencing reports). Data update frequencies vary. Some high-value data sources may update quarterly or annually, while real-time monitoring data streams may update every minute. Document structures are complex, often involving multiple standards and terminology systems like SNOMED CT and LOINC. Field names differ significantly, and units are mixed, requiring extensive standardization and cleaning.

Constraints on Tool Calling and Plugins from These Characteristics

The high heterogeneity and unstructured nature of RWE data impose specific requirements on tool calling and plugin design. First, diverse data sources necessitate support for multiple data interfaces and protocols, such as FHIR API and HL7, and the ability to parse data in different formats. Second, inconsistent data update frequencies require tools to flexibly schedule data fetching tasks and manage incremental and full updates. The complexity of unstructured text limits the effectiveness of traditional keyword matching plugins, requiring integration of advanced Natural Language Processing (NLP) tools, such as Named Entity Recognition (NER) and Relation Extraction (RE) plugins. These tools accurately extract key information like symptoms, medications, dosages, and times from clinical text. Furthermore, domain-specific terminology and mixed units demand robust ontology mapping and unit conversion capabilities in plugins to ensure data consistency and query accuracy.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`maxContext`	`8192 token`	Most RWE documents have moderate length, balancing model processing capability and cost.
`Chunk size` (Segment Length)	`500-700 characters` (characters)	RWE text has high semantic density; shorter segments help preserve local context.
`Recall count` (Recall Count)	`Top 10 entries` (top 10)	Ensures sufficient contextual information coverage, preventing critical evidence omission.
`Similarity threshold` (Similarity Threshold)	`0.75-0.85`	Domain terminology has high similarity; increasing the threshold reduces false positives.
`Rerank result count` (Reranked Return Count)	`Top 5 entries` (top 5)	Further refines results, improving the accuracy and relevance of the final output.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Processing large clinical or genetic sequencing reports can be time-consuming.

Common Pitfalls

A 404 Not Found error when calling a tool typically indicates an incorrect API endpoint configuration or insufficient access permissions.
Key fields in tool return results may be empty or incorrectly formatted. This occurs when data cleaning and standardization plugins do not fully cover the complexity of all heterogeneous data sources, or when terminology mapping is inaccurate.
The model may encounter issues rendering base64 encoded images, resulting in images not displaying correctly. This can relate to Markdown renderer limitations for specific base64 formats or image sizes.

Configuration Verification

Check call logs to confirm all configured tools and plugins executed successfully without error codes.
Randomly select multiple RWE data samples from different sources. Run queries and verify whether the model output accurately extracts key entities and values, and confirm unit consistency.
For queries involving unstructured text, check if the model correctly uses information extracted by NER and RE plugins to answer questions, and confirm high relevance between cited original text snippets and the answer content.

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.