Data Characteristics in This Category
Real-World Study (RWS) registration and submission data sources often include Electronic Health Records (EHR), medical insurance claims databases, disease registries, patient-reported outcomes (PRO) data, and various wearable device data. This data is typically heterogeneous and unstructured, with update frequencies varying from daily real-time updates to quarterly or annual batch updates. In terms of document structure, RWS data often exists as clinical study reports, statistical analysis reports, data management plans, and ethical approval documents. Field characteristics include extensive free-text descriptions (e.g., diagnoses, treatment plans, adverse events), timestamp information (e.g., visit dates, medication start/end dates), and standardized codes (e.g., ICD-10, ATC classification codes). Units cover dosages (mg, g), frequencies (times/day), and durations (days, months, years).
Constraints Imposed by These Characteristics on "Tool Calling and Plugins"
The heterogeneous and unstructured nature of RWS data requires powerful data parsing and standardization capabilities for tool calling. For example, extracting key medical concepts from free text relies on Named Entity Recognition (NER) plugins and may require calling medical terminology mapping tools. Varying data update frequencies demand incremental data processing and version control, requiring the toolchain to support regular data synchronization and historical version rollback. Complex document structures mean a single tool cannot cover all processing steps; plugin orchestration is necessary to implement multi-step Extract, Transform, Load (ETL) processes. The large number of fields and diverse units necessitate calling data cleaning and unit conversion tools before data integration and analysis to ensure data consistency, such as unifying drug dosage units from different sources to mg. Additionally, due to patient privacy concerns, calling data de-identification and anonymization plugins is mandatory.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale for This Value |
|---|---|---|
maxContext | 8192 | Accommodates the length of RWS reports, ensuring complete context understanding. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances textual semantic integrity with model processing efficiency, preventing information loss from overly long texts. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Ensures high relevance for recalled medical terms and concepts, filtering out noise. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Handles parsing time for large RWS reports or multi-attachment documents, preventing timeouts. |
pluginCallRetries | 3 times (times) | Improves the stability of complex plugin chain calls, addressing transient external service failures. |
maxOutputTokens | 2048 | Ensures that plugin-returned analysis results or draft reports are sufficiently detailed. |
Three Common Mistakes
- An
unAuthChaterror when calling external APIs typically indicates an incorrectly configured or expired API Key, leading to authentication failure. - When processing lengthy clinical reports, the model's accuracy in scoring or summarizing reports significantly decreases. This is due to
maxContextorChunk sizebeing set too small, causing critical information to be truncated or dispersed. - Scheduled tasks failing to execute as expected, such as AI daily reports not being generated, often result from incorrect timer configuration or uncaught exceptions in the associated toolchain during execution, leading to task interruption.
How to Confirm Correct Configuration
- Check the
statusfield of each plugin in the tool calling chain via FastGPT's log system to confirm all steps return a200orsuccessstatus code. - Randomly select 5–10 typical RWS documents, execute an end-to-end processing flow, and compare model output with expected results. Focus on the accurate extraction of key medical terms, dosage units, and timestamp information.
- In FastGPT's "Tool Management" interface, verify all configured RWS-related plugins, ensuring sensitive parameters such as
API_KEYandENDPOINTare stored encrypted and have correct values. - For incremental data processing scenarios, simulate a data update to observe if the system correctly identifies and processes new or modified data records, and verify the consistency of the output results.
The values provided are common starting points and should be measured against the reader's 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.