Tool Calling and Plugins for Medical Insurance Claim

Data Characteristics

Medical insurance claim data originates from various medical insurance bureaus, healthcare institutions, and third-party payment platforms. It typically exists as structured databases or semi-structured files. Update frequencies vary significantly by region and policy; some areas achieve T+1 updates, while others may update monthly or quarterly in batches. The data structure is complex, containing core fields such as patient demographics, ICD-10 diagnostic codes, CPT/HCPCS treatment codes, generic drug names, cost details, payment types, reimbursement ratios, and settlement statuses. Field units are diverse; for example, amounts are in "CNY", quantities in "times" or "boxes", and timestamps are precise to "seconds". The data often includes numerous coded values that require mapping against official dictionaries.

Constraints Imposed by These Characteristics on Tool Calling and Plugins

The complexity and diversity of medical insurance claim data impose strict requirements on tool calling and plugins. First, dispersed data sources and inconsistent update frequencies necessitate that plugins support multi-source data integration and asynchronous processing to ensure timely pre-screening results. Second, the abundance of coded values and non-standard fields makes data preprocessing a critical step; plugins must be able to call external services for code conversion and data cleansing. The high sensitivity of medical insurance data mandates strict data security and compliance for tools, restricting the use of some cloud services. Furthermore, real-time query demands for dynamic fields like settlement status and reimbursement ratios require plugins to accurately pass complex query parameters during tool calls and handle various potential response states, such as HTTP 403 or HTTP 404 error codes.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`toolCallTimeout`	`600 seconds`	Medical insurance data queries involve long chains and multiple system integrations; sufficient response time is needed to prevent timeouts.
`maxContext`	`2000 characters`	Medical insurance rules and patient information descriptions can be lengthy; this ensures complete context transmission.
`pluginRetryCount`	`3`	External services may experience occasional network fluctuations or transient high loads; retries improve success rates.
`authHeaderName`	`X-Auth-Token`	Medical insurance data interfaces typically use Token authentication; this is a common and secure header field name.
`responseSchema`	Defined by the actual API JSON Schema	Precisely parse key fields such as medical insurance claim details and reimbursement status, ensuring structured data extraction.
`dynamicParamMapping`	`patientId -> medicalSystemPatientID`	Maps FastGPT's internal patient identifier to a parameter name recognizable by the medical insurance system, such as `patientIdentifier`.

Common Pitfalls

Tool calls return HTTP 500 or an empty response body, resulting in missing pre-screening results or Internal Server Error messages. This often occurs due to incorrect input parameter formats for the medical insurance API or the omission of a mandatory field like policyNumber.
After plugin activation, clinical trial pre-screening results show incorrect reimbursement ratios or inaccurate cost calculations. This happens when coded values in medical insurance data are not correctly converted to understandable text, such as ICD-10 codes not being mapped to specific diagnostic names, leading to model misinterpretation.
After a long wait, the tool call eventually returns a Timeout error, but medical insurance system logs show no request received. This could be due to improper http_proxy network proxy configuration or firewall rules blocking outbound requests.

Verification Steps

Use FastGPT's tool debugging interface to simulate a call to the medical insurance claim query plugin. Check if the returned HTTP Status Code is 200 and confirm the response body contains expected fields, such as totalAmount and reimbursementStatus.
Select a patient case with a known reimbursement outcome. Input their information into the system and observe if the pre-screening results for reimbursement ratio and settlement amount match the actual situation or test data.
Review FastGPT's runtime logs for any tool-call-related error messages, especially warnings or errors concerning parameter parsing, API authentication, or network connectivity.
Verify all dynamicParamMapping source and target fields in the plugin configuration to ensure correct one-to-one mapping between FastGPT's internal data structure and the medical insurance API requirements.

The values provided are common starting points and should be measured against specific 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.