Data Characteristics in this Domain
Pharmacoeconomic data for clinical trial pre-screening primarily originates from clinical trial databases, medical claims databases, national medical insurance catalogs, drug pricing strategy reports, and related literature. Update frequencies vary; clinical trial databases might update monthly or quarterly, while medical insurance catalogs typically adjust annually. Document structures are complex, often containing semi-structured text reports and structured tabular data. Fields include drug generic names, indications, treatment plans, treatment effects (e.g., QALY, DALY), cost components (drug procurement costs, hospitalization fees, outpatient fees), utility values, and payer information. Units involve currency (USD, RMB), time (years, months, days), utility units (QALYs), and ratios (ICER). Data is characterized by its multi-source heterogeneity, often with missing values or ambiguous descriptions.
Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"
The multi-source and heterogeneous nature of pharmacoeconomic data requires FastGPT's HTTP interface to flexibly adapt to various data formats, such as supporting JSON, XML, and even CSV file parsing, to handle data output from different source systems. Varying update frequencies necessitate configuring different data synchronization strategies: high-frequency clinical trial data might require scheduled pulling, while annually updated medical insurance catalogs could use manual uploads or low-frequency synchronization. Document complexity, especially the presence of semi-structured text, challenges the interface's data preprocessing capabilities, requiring NLP techniques to extract key fields from unstructured text. The specificity of fields and units, such as QALYs and ICER, demands strict adherence to their definitions during data transmission and storage to avoid conversion errors, and clear unit specification in interface responses for subsequent calculations or display.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
externalApi.timeout | 60000 ms | Pharmacoeconomic data sources are often external. Large data volumes or network latency can cause request timeouts. Extending the timeout reduces failure rates. |
knowledgeBase.maxChunkSize | 800–1200 characters | Pharmacoeconomic literature contains extensive specialized terminology and contextual relationships. An appropriate chunk size helps maintain semantic integrity. |
retrieval.topK | 5 items | The pre-screening phase requires comprehensive consideration of relevant literature. Increasing the recall count helps cover more potential influencing factors. |
dataIngestion.parseFileTimeout | 600 seconds | Processing large pharmacoeconomic reports or multi-dimensional tabular data can take a long time, requiring a longer parsing timeout. |
http.maxRetries | 3 times | External systems may fail due to transient network fluctuations or service overload. Multiple retries improve data retrieval success rates. |
api.metadata.enable | true | Allows passing the metadata field to tag key information like data source and update time, facilitating traceability and management. |
Common Pitfalls
- The HTTP interface returns a 200 status code, but critical business fields are empty. This happens when external system data sources have missing data or the JSON structure returned by the interface does not match expectations.
- Data synchronization tasks frequently report timeout errors. This occurs when the amount of data pulled at once is too large, or the external interface responds slowly, without batch processing or concurrency optimization for large datasets.
- Knowledge base retrieval results contain a large number of irrelevant or duplicate pharmacoeconomic documents. This is due to an improper text segmentation strategy that fails to effectively identify logical boundaries within documents, or a similarity threshold set too low.
Verification of Configuration
- Simulate actual pre-screening scenarios by calling FastGPT's API. Check if the returned pharmacoeconomic data is complete and accurate, and verify the values and units of key fields.
- Monitor data synchronization task logs. Confirm that every external system data retrieval and processing is error-free and that processing time is within an acceptable range, setting thresholds based on historical data retrieval durations.
- Upload typical pharmacoeconomic reports to the knowledge base. Conduct retrieval tests through the FastGPT interface to verify that highly relevant literature snippets can be recalled. Adjust the recall count and similarity threshold based on business requirements.
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.