Tool Calling and Plugins for Infection Control Management Regulations

Data Characteristics

Infection control management data originates from hospital internal regulations, Standard Operating Procedures (SOPs), national and local health authority guidelines for infection control, medical waste management regulations, and detailed rules for infectious disease prevention and control. This data typically exists as unstructured documents, such as PDFs, Word documents, scanned images, or internal knowledge base pages. Update frequency varies: national and local policies may be revised annually or released ad-hoc, while hospital internal SOPs are updated quarterly or semi-annually based on policy adjustments or operational needs. Document structures are often chapter-based or clause-based, containing extensive specialized terminology, abbreviations, and cited provisions. Common fields include regulation name, publication date, enforcing department, scope of application, specific measures, risk level, disposal procedures, and responsible person. Units frequently involve time (e.g., hours, days), percentages (e.g., qualification rate), and quantities (e.g., number of cases, bed count).

Constraints on Tool Calling and Plugins from Data Characteristics

The unstructured nature of infection control management documents makes it challenging to extract structured parameters directly from text. For example, an SOP on hand hygiene might describe washing steps, disinfectant concentrations, and duration in different paragraphs. This information requires accurate identification and extraction as parameters for tool calls. The uncertain update frequency necessitates version management capabilities in the tool calling pipeline to ensure query results are always based on the latest effective version of regulations. Complex document structures and specialized terminology demand higher accuracy in semantic understanding and entity recognition to prevent parameter extraction errors due to ambiguous terms or contextual misunderstandings. Furthermore, regulations often contain procedural descriptions, such as "If A occurs, then execute B; otherwise, execute C." This requires tool calling to handle conditional logic and trigger different external systems or plugins as needed.

Configuration Settings

Common Pitfalls

• Tool calls return a 401 Unauthorized error code. This occurs when the API key or authentication token required by the external system is not correctly configured or passed.
• The fields extracted by the model in tool call parameters are empty or do not conform to the expected format. This happens when specific parsing rules for expressions in infection control regulations, such as dates or dosage units, are not finely defined.
• Tool calls succeed, but the execution results do not match expectations. This may be because the model misunderstood the user's intent, called the wrong tool, or passed incorrect parameter values, failing to accurately reflect regulatory requirements.

Verification Checklist

• Design a series of test cases with different phrasings for core infection control regulation clauses. Verify that tool calls accurately identify intent and extract parameters.
• Check tool call logs to confirm that the actual plugin name and parameter values called match expectations, and that the external system returns a success status code.
• Simulate a regulation update scenario. Upload a new version of an SOP and test whether query results reflect the latest content, prioritizing the new version's data.
• Use complex queries containing specialized terminology and abbreviations. Verify that the model correctly understands these terms and can guide to the appropriate regulation clauses or tools.

Note: The values provided are common starting points. Measure performance against your own samples to determine optimal configurations.

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.