Follow-up Reminders for Private Domain Consultation

Data Characteristics for This Category

In private domain consultation conversion scenarios within the biomedical field, follow-up reminder data typically originates from CRM systems, internal physician workstations, or patient management platforms. This data updates frequently, potentially hourly or daily, with new entries or status changes. Reminder information is usually structured, including fields such as patient ID, consultation type, last interaction time, planned reminder time, reminder content template, and responsible person ID. Key fields include patient_id (unique patient identifier), consultation_type (e.g., "medication inquiry," "follow-up reminder"), next_contact_date (next contact date, format YYYY-MM-DD), reminder_status (reminder status, e.g., "pending," "sent"), drug_name (medication name involved), and dosage_unit (dosage unit, e.g., "mg," "tablet").

Constraints Imposed by These Characteristics on Tool Calling and Plugins

High-frequency data updates require tool calling to have real-time or near real-time triggering capabilities to ensure timely reminders. For example, when a patient's consultation status changes from "initial consultation" to "pending follow-up," the system should promptly trigger the reminder creation process. Structured data means plugin parameters can map directly to database fields, reducing data parsing complexity. However, this also demands stricter parameter validation from plugins to prevent invalid data from causing call failures. For instance, the next_contact_date field must be a date format, and dosage_unit must be a predefined enumerated value. Furthermore, specialized fields like medication names and dosage units require the plugin to have semantic understanding capabilities and the ability to call external knowledge bases. This ensures the tool accurately identifies and processes biomedical terminology, for example, by validating or supplementing information through a drug information database.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`maxContext`	`2000 characters`	Accommodates patient consultation history and medication information, while preventing excessive context from degrading model performance.
`Recall Count`	`Top 5`	Focuses on recent relevant interaction records and key reminder information, improving recall efficiency.
`Similarity Threshold`	`0.75`	Balances recall precision and comprehensiveness, filtering historical data highly relevant to the current follow-up reminder.
`Plugin Timeout`	`60 seconds`	Accounts for response times of external CRM systems or drug databases, allowing sufficient processing time to avoid call failures due to network latency.
`Retry Policy`	`3 times`	Addresses temporary external system failures, improving the success rate of tool calls.
`Parameter Validation Rules`	`Strict Mode`	Ensures input parameters conform to predefined data types and formats, such as `patient_id` as a number and `next_contact_date` as a date.

Common Pitfalls

Knowledge base searches returning "no relevant content found" indicates that the knowledge base index lacks the latest patient interaction records or medication information, leading to empty queries.
Plugin parameters failing to retrieve values from specified variables, or retrieving null values, indicates a mismatch between variable names in the workflow and the plugin's expected parameter names, or incorrect assignment by upstream nodes.
API output not including thought content, requiring a wait for thought completion before output, indicates that streaming output configuration is not enabled, or API interface design limitations necessitate an additional call to the get_thought_process interface.

Verification of Configuration

Simulate various patient consultation scenarios to test if follow-up reminders trigger as expected and if generated reminder content includes key information like patient_id and next_contact_date.
Examine tool call logs to confirm correct parameter passing, external system status codes of 200 or 201, and the absence of timeout errors.
Validate the recall quality of knowledge base search results, ensuring that historical consultation records and medication instructions relevant to the current follow-up reminder are effectively retrieved, and check if similarity_score is within a reasonable range.
For anomalous data (e.g., incorrect date format, misspelled drug names), test if tool calls correctly capture and trigger failure handling logic, such as returning a 400 error code or initiating manual intervention.

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.