Tool Calling and Plugins for Rehabilitation Device Clinical

Data Characteristics

Rehabilitation device clinical trial data originates primarily from Hospital Information Systems (HIS), Electronic Health Records (EHR), and device-embedded sensors. Data typically exists in a mixed format, including structured data (e.g., patient demographics, diagnoses, treatment plans, efficacy scale scores) and semi-structured data (e.g., physician notes, rehabilitation therapist assessment reports). Update frequency varies by trial stage and device type; early feasibility studies might update weekly, while large-scale multi-center trials often update daily or in real-time. Document types include trial protocols, informed consent forms, Case Report Forms (CRFs), device manuals, and maintenance records. Key fields include patient ID, device model, treatment duration, efficacy metrics (e.g., ROM values, muscle strength grading), and adverse event reports. Units involve millimeters, degrees, Newtons, seconds, and percentages. Note that different device manufacturers may use varying standards or abbreviations.

Constraints from "Tool Calling and Plugins"

The mixed structure and diverse sources of rehabilitation device data require tool calling to have robust parsing capabilities, handling JSON, XML, and unstructured text simultaneously. High-frequency data updates demand real-time plugin functionality, requiring support for Webhooks or scheduled tasks to ensure pre-screening logic operates on the latest data. Device-specific fields and unit discrepancies necessitate strict data standardization and mapping before tool calls to prevent judgment errors due to inconsistent units. For example, Range of Motion (ROM) data from different devices might be expressed in degrees or percentages. Furthermore, the rigor of clinical trials requires tool calling to be transactional, ensuring the integrity and traceability of data submissions or status changes. Failure handling mechanisms must be granular, down to individual records, and provide clear error codes and retry strategies. For sensitive patient data, tool calling must adhere to strict data security and privacy protection protocols.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`max_tokens`	`2048`	Accommodates most assessment report text lengths, reducing truncation risk
`temperature`	`0.3`	Ensures stability of pre-screening logic, reducing randomness
`tool_request_timeout_seconds`	`60 seconds`	Balances data source interface response speed and user waiting experience
`max_retries_on_failure`	`3`	Addresses temporary network fluctuations or external API rate limits causing call failures
`data_standardization_schema`	`JSON Schema v2.0`	Ensures unified structure and field types for data from different devices
`callback_url_on_error`	`https://yourdomain.com/error_handler`	Notifies backend services promptly to handle call failures, supporting asynchronous retries

Common Pitfalls

External API calls to api/v1/chat/completions return a CORS policy error. This typically occurs because the frontend does not correctly configure cross-origin headers, or the backend does not include CORS-related headers like Access-Control-Allow-Origin in its responses.
The model returns message: chat:llm-model-response-empty. This usually happens when the tool function returns an empty result or a format that the model does not expect, preventing the model from parsing it.
A large number of misjudgments appear in clinical trial patient screening results. This is due to insufficient handling of differences in field naming or units from various device manufacturers during data standardization, leading the model to make judgments based on incorrect data.

Verification Steps

Simulate toolchain calls. Verify that all external APIs return results within the tool_request_timeout_seconds setting and confirm that the timeout error handling logic triggers as expected.
Submit test cases containing typical rehabilitation device data. Check if the model's pre-screening output matches expectations and confirm that data_standardization_schema has been successfully applied by reviewing logs.
In an integrated environment, intentionally trigger external API failures or abnormal data returns. Observe if callback_url_on_error receives error notifications and check if the retry behavior under max_retries_on_failure configuration is as expected.

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.