Data Characteristics for This Category
In the context of private domain consultation conversion for biomedicine, follow-up reminder data primarily originates from CRM systems, online consultation platforms, and patient education communities. This data typically exists in structured or semi-structured formats. Structured data includes patient profiles (name, contact information, disease type, consulted products), consultation records (time, content summary, consultant), drug or service recommendation records, and follow-up plans. Semi-structured data may contain free-text patient feedback and chat logs from consultation processes. Data update frequency depends on business processes; for example, new consultation records generate in real-time, follow-up plans update periodically, and patient feedback occurs randomly. Document structures commonly use JSON or CSV formats, with fields such as patient_id, consultation_id, follow_up_date, reminder_content, and status. For units, time fields often use Unix timestamps or ISO 8601 format, and disease types and drug names frequently adopt standard medical terminology codes like ICD-10 or NDC.
Constraints Imposed by These Features on Workflow Orchestration
The diversity and real-time nature of follow-up reminder data impose specific requirements on workflow orchestration. First, heterogeneous data sources necessitate multi-source data integration within the workflow, such as connecting to CRM systems via API or pulling database data via scheduled tasks. Second, real-time requirements demand that the workflow responds quickly to new events, for instance, immediately triggering the follow-up reminder process when a new consultation record is generated. The semi-structured nature of document content implies the need for text parsing capabilities during data processing, such as extracting key information from chat logs to populate structured fields. Field and unit standardization constrains data cleaning and transformation logic, ensuring data consistency when passed between different components. For example, the follow_up_date field must be a valid date format, and drug names in reminder_content must match standard names in the internal knowledge base. These constraints collectively determine the configuration details for data acquisition, processing, logical judgment, and execution output stages of the workflow.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Data Source Type | API or Database | Select based on actual data sources to ensure real-time or near real-time data acquisition. |
Trigger Mode | Event Trigger | Trigger immediately upon new consultation record or follow-up plan updates to ensure timely reminders. |
Text Parsing Model | fastgpt-3.5-turbo-16k | Process free-text patient feedback, extract key information, balancing cost and performance. |
Max Concurrent Tasks | 20 | Handle high concurrent consultation volumes, prevent task backlog, and ensure timely reminder delivery. |
Retry Interval | 60 seconds | Allow reasonable retry intervals for occasional external system failures (e.g., SMS service) to prevent missed reminders. |
Timeout | 30000 ms | Ensure the workflow completes within a reasonable time, preventing prolonged blocking. |
Common Pitfalls
- The workflow fails to retrieve the latest consultation data after starting, typically due to incorrect data source connection configurations or insufficient permissions.
- Follow-up reminder content appears garbled or has missing information because the text parsing component failed to correctly identify and extract key fields from semi-structured text.
- Batch reminder sending fails or experiences severe delays, often due to excessively low workflow concurrency limits or external sending services reaching rate limits.
Verification Steps
- Simulate creating a new private domain consultation record. Observe if the workflow triggers successfully and if it generates follow-up reminders with the expected content.
- Review the data flow in the workflow logs. Confirm that critical field values like
patient_idandfollow_up_dateare correct. - Test patient feedback texts of varying complexity. Verify that the text parsing component consistently extracts the key information required for
reminder_content. - Perform stress tests or simulate high-concurrency scenarios. Evaluate the workflow's performance when processing a large number of follow-up reminders to ensure no significant delays or errors.
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.