Data Characteristics in this Category
Home healthcare clinical trial pre-screening data primarily consists of multi-modal information generated by patients using devices at home. Data types include physiological parameters (e.g., systolic pressure, diastolic pressure, heart rate from blood pressure monitors; blood glucose level, measurement time from glucometers), device logs (e.g., usage duration, error codes), and patient-filled questionnaires (e.g., symptom description, medication adherence). Data update frequency varies based on device type and trial protocol, typically multiple times daily or several times per week. Data formats are diverse, including structured CSV/JSON reports, unstructured text descriptions, and limited image data (e.g., skin condition photos). Field names and units must strictly adhere to medical device industry standards; for example, blood pressure units are mmHg, and blood glucose units are mmol/L or mg/dL.
Constraints Imposed by These Features on Tool Calling and Plugins
The multi-modal nature of home healthcare data requires tool calling plugins to process different data input types, including structured data parsing and unstructured text analysis. The real-time nature of data updates requires plugins to trigger data synchronization and processing promptly, preventing inaccurate pre-screening results due to data lag. Error codes or abnormal values in device logs require specific tools for interpretation and correlational analysis to assess patient adherence or device operational status. Unstructured text from patient-filled questionnaires, particularly symptom descriptions, often contains vague or colloquial expressions, necessitating plugins with strong semantic understanding capabilities for standardization and structuring. Additionally, strict unit specifications require plugins to perform unit validation and conversion during data processing, avoiding calculation errors or misjudgments caused by unit inconsistencies.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
toolChoice | auto | Allows the model to automatically select appropriate tools based on user requests and available tool list, accommodating varied user inputs. |
maxContext | 4096 tokens | Ensures sufficient context for judgment by accommodating multi-day patient physiological data, device logs, and questionnaire text. |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Accounts for potentially large home device data files, providing ample time for parsing. |
Chunk size | 800–1000 characters | Suitable for patient-filled questionnaire text, ensuring semantic completeness and improving recall accuracy. |
Similarity threshold | 0.75 | Achieves high recall while reducing interference from irrelevant information, especially for matching symptom descriptions. |
tool_schema_version | v1.2 | Maintains consistency with the latest tool definition protocol, ensuring compatibility with new features and data fields. |
Three Common Pitfalls
- Symptom: After an external system calls an API, critical fields like
患者IDorequipment serial numberare empty in the returned data. Reason: The unique identifier field from the home healthcare device data source was not correctly mapped or passed in the API request parameters. - Symptom: After the application calls a plugin, the model's pre-screening results do not match expectations; for example, normal blood pressure values are identified as abnormal. Reason: The plugin did not perform correct unit conversion when processing
blood pressure unit(mmHgorkPa), leading to misinterpretation of values. - Symptom: Uploaded patient device log files time out during processing, preventing analysis results from being obtained. Reason: The
PARSE_FILE_TIMEOUT_SECONDSconfiguration is too low and does not cover the parsing time for large log files generated by some home healthcare devices.
How to Confirm Correct Configuration
- Simulate uploading a home healthcare device data package containing various data types via the API interface. Check if all key physiological parameters (e.g.,
blood glucose, mmol/L,heart rate) are extracted correctly and their units are accurate in the returned results. - Construct a patient questionnaire containing vague
症状描述. Verify if, after the application calls the plugin, the model can accurately identify and standardize these descriptions into predefined medical terminology. - Call a tool that relies on an external data source. Check if it successfully retrieves corresponding error explanations and suggested handling solutions when processing
Device error codes, and returns them to the model.
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.