Data Characteristics in This Category
Real-world evidence (RWE) data for clinical trial pre-screening primarily originates from Electronic Health Records (EHRs), insurance claims databases, disease registries, and patient-reported outcomes (PROs). This data is often heterogeneous, containing both structured information (e.g., diagnostic codes, lab results) and unstructured information (e.g., clinician notes, imaging report text). Data update frequencies vary; EHR data might update daily, while insurance claims data could be aggregated quarterly or annually. Document structures are complex. For example, clinical notes in EHRs are often free text, lacking uniform fields. Insurance claims data, however, uses standardized ICD codes and ATC codes. Field names can include abbreviations or aliases. Unit conversion is a common challenge, such as blood pressure recorded in mmHg or kPa, or weight in kg or lb.
Constraints Imposed by These Characteristics on "Tool Calling and Plugins"
Data heterogeneity requires tools to handle multiple data formats and perform standardized conversions. The high proportion of unstructured data means traditional structured query tools are insufficient, necessitating natural language processing (NLP) plugins for information extraction and entity recognition. Inconsistent data update frequencies demand real-time capabilities and robust caching strategies for tool calls to prevent using outdated data. Complex document structures and variable field names require tools with stronger robustness in parsing and mapping, potentially needing custom parsing rules or pre-trained models. Inconsistent units mandate built-in unit conversion features or plugin support to ensure data accuracy before analysis. Additionally, sensitive patient information requires tool calls to have strict data anonymization and access control capabilities.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
maxContext | 3000 Tokens | Balances long text processing with model call costs, accommodating longer clinical notes in EHRs. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Allows sufficient time for parsing large or complex documents (e.g., full EHR records), preventing timeouts. |
Recall count | Top 10 entries | Ensures coverage of as many potentially relevant records as possible during the initial screening phase. |
Similarity threshold | 0.75 | Balances recall and precision, reducing interference from irrelevant information and improving pre-screening efficiency. |
Rerank result count | Top 3 entries | Focuses on the most relevant few records for subsequent manual review. |
ENABLE_NLP_ENTITY_EXTRACTION | true | Efficiently extracts key entities like diseases, medications, and symptoms from unstructured text, supporting precise screening. |
Three Common Mistakes
- Tool calling workflow validation fails with a message like "Workflow validation failed, please check for missing parameters, missing values, or incorrect connections." This occurs when plugin input parameters do not match actual data fields, or required parameters are unassigned.
- Model call takes too long, with generation times between 1-10 seconds. This might be due to an excessively long context processed by the large model or insufficient concurrency capacity of the called model.
- Query results contain inconsistent units, such as height data appearing in both centimeters and inches. This happens when unit conversion is not performed during data preprocessing or the tool calling stage.
How to Confirm Proper Configuration
- Execute the tool calling workflow with typical case data and verify that key entities are accurately extracted in the output.
- Simulate various query scenarios to confirm that tool call response times are within acceptable limits.
- Check the data conversion plugin logs to ensure all critical field units are unified and no conversion errors occurred.
- Manually review pre-screening results with a small sample to evaluate if recall and accuracy meet expected screening criteria.
Note: The values provided are common starting points and should be measured 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.