What the data for this use case looks like
Data sources include the national medical security information platform, hospital HIS and medical insurance interfaces, medical insurance catalog and coding library, and cross-provincial medical settlement platform. The update frequency is daily. Daily processed documents include medical insurance settlement lists, expense details, medical record homepages, prescriptions, rejection lists, and settlement reconciliation statements. Fields cover diagnosis and treatment item codes, pharmaceutical and consumable codes, expense amounts, insured person information, diagnosis and treatment time, and other structured content. It also associates unstructured medical record text and prescription images.
Constraints imposed by these characteristics on model access and configuration
The daily updated medical insurance catalog coding library requires model access configuration to synchronize and load the latest coding data. This prevents fund overpayment or rejected payment disputes caused by using expired codes. Mixed processing of multiple document types requires the configured context window to accommodate batch settlement details and associated medical records and prescription texts. This stops core audit fields from being truncated. The combination of structured fields and unstructured text requires the model request format to adapt to both code matching and text verification needs. Batch audit scenarios require configuration to support stable request processing, to ensure audit efficiency for multiple settlement sheets.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total text length of batch settlement lists, associated prescriptions and medical record homepages, avoiding truncation of core audit fields |
maxTokens | 2000–3000 token | Covers the document drafting length for a single audit result, meeting the output requirements for rejection appeal materials |
temperature | 0.1–0.3 | Reduces random generation probability, ensuring consistent results for code matching and expense verification |
toolChoice | auto | Automatically calls the built-in code matching tool, adapting to the associated verification of medical insurance catalog and settlement data |
vision | Enabled | Supports parsing image-based materials such as prescriptions and settlement sheets, adapting to unstructured image document scenarios |
AIPROXY_API_ENDPOINT | Fill in the actual interface address according to the deployment environment | Adapts to domestic large models or custom-deployed model call paths |
AIPROXY_API_TOKEN | Authentication key generated by the deployment party | Ensures model call security, preventing unauthorized access |
Three common misconfigurations
- Phenomenon: The model returns empty values. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or the request format does not match the input requirements of custom-deployed models such as vllm. - Phenomenon: Domestic large models such as Tongyi Qianwen cannot be selected. Cause: The access path and authentication information of the corresponding model are not added in the deployment configuration, so the platform cannot recognize the optional model list.
- Phenomenon: Parsing of image-based settlement materials fails. Cause: The
visionconfiguration is not enabled, or the incoming image data format does not meet the model requirements (such as not using base64 encoding).
How to confirm the configuration is correct
- Send a test request for a single settlement sheet, verify that the audit document returned by the model includes the code matching result of the corresponding diagnosis and treatment item, and confirm that the context configuration does not truncate core content.
- Switch to the domestic large model option, check whether the target model appears in the platform's optional model list, and confirm that the API endpoint and key configuration are correct.
- Submit a prescription or settlement material containing images, check whether the model can correctly parse key information in the images, and confirm that the
visionconfiguration is enabled. - View the model call log, verify the request format and field integrity of the returned results, and troubleshoot issues such as empty returns or format abnormalities.
The values provided are common starting points and should be evaluated 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 2026-09-28.