Telemedicine pharmacovigilance and adverse reaction handling present unique requirements for FastGPT's HTTP interface and external system integration. This page explores data characteristics, interface integration constraints, configuration recommendations, and common issue resolution in telemedicine scenarios. The goal is to help engineers efficiently implement relevant functionalities.
Data Characteristics
Telemedicine pharmacovigilance data comes from diverse sources. These include patient self-reports, Electronic Health Records (EHR), wearable device data, remote monitoring reports, and doctor consultation notes. This data updates frequently. During acute adverse reactions, data can flow in real-time. Document structures typically include unstructured patient descriptions, structured medication history and allergy history, and semi-structured vital signs. Field specifics include medical terminology such as drug generic names, batch numbers, dosage units (mg, IU, ml/h), administration routes (oral, intravenous injection), adverse reaction terms (MedDRA codes), and event timestamps.
Constraints from HTTP Interface and External Systems
The high update frequency of telemedicine data requires FastGPT's HTTP interface to have high throughput and low latency. This ensures timely processing and alerting for adverse reaction information. Unstructured patient descriptions, such as colloquial symptoms generated via speech-to-text, require FastGPT to have robust natural language processing capabilities to accurately extract key medical entities. Structured and semi-structured data require the interface to flexibly adapt to field mappings from different external systems. For example, medication_name in EHR needs to map correctly to FastGPT's internal drug_concept_name. Medical terminology, such as MedDRA codes, requires FastGPT to perform terminology standardization or mapping through external API calls to ensure information accuracy. The distributed nature of telemedicine services means the interface must handle data requests from various terminals and geographical locations, ensuring data transmission security and compliance (e.g., HIPAA).
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 3000 Tokens | Telemedicine consultation records and patient self-reports can be long, requiring a sufficiently large context window to capture complete information. |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures recalled knowledge snippets are highly relevant to patient symptom descriptions, preventing false positives or negatives. |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds (120 seconds) | Remotely transmitted medical records can be large, requiring a longer parsing timeout. |
Chunk size (Chunk Length) | 600 characters (600 characters) | Balances semantic completeness and indexing efficiency, suitable for medical text characteristics. |
Recall count (Recall Count) | Top 8 entries (Top 8) | Increases recall count to improve the likelihood of finding potential correlations in complex cases. |
EXTERNAL_API_RETRY_COUNT | 3 times (3 times) | External medical terminology services or drug databases may experience transient network fluctuations; increasing retries improves stability. |
Common Pitfalls
- The drug adverse event fields returned by the interface are empty. This is due to incorrect configuration of field mapping between the external system and FastGPT's internal data model.
- Patient self-reported symptom descriptions are not correctly recognized as medical entities. This is due to a lack of integration with specialized medical Named Entity Recognition (NER) services or corresponding dictionaries.
- During peak periods, data processing requests sent by the telemedicine platform time out. This is due to a lack of proper concurrency optimization or load balancing for FastGPT's HTTP interface.
Verification Steps
- Simulate submitting patient reports containing common drugs and adverse reactions. Check if FastGPT's results accurately identify and classify relevant medical entities.
- Call the FastGPT interface and ensure the external system receives correct alerts or processing results. This verifies end-to-end data flow and logic.
- Check FastGPT logs for successful external system API calls, especially for interfaces related to medical terminology standardization or drug information queries.
- Submit long text descriptions with complex conditions in the telemedicine platform. Observe FastGPT's response time and the accuracy of recalled knowledge to ensure stable operation in various scenarios.
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.