Data Characteristics
Pharmacovigilance data for home medical devices originates from user reports, medical institution feedback, after-sales service records, and post-market surveillance reports. Data updates are event-driven, not fixed. New data appears when an adverse event or product performance issue occurs. The update cycle ranges from hours to weeks. Document structures vary. They include unstructured user feedback text (e.g., free text describing symptoms, usage, product malfunctions), semi-structured standard report forms (e.g., fields for adverse event type, occurrence time, device model, batch number, patient age, gender), and structured device log data. Field content involves specific medical terminology, device technical parameters, and usage environment descriptions. Units include time (hours, days), quantity (times, units), and measured values (e.g., blood glucose mmol/L, blood pressure mmHg).
Constraints Imposed by These Characteristics on Workflow Orchestration
The event-driven nature and irregular update frequency of home medical data require flexible workflow triggering mechanisms, independent of fixed time scheduling. The diversity of documents, especially the large volume of unstructured text, necessitates advanced natural language processing tools for entity recognition and sentiment analysis to structure raw data. The presence of semi-structured report forms requires the workflow to parse specific document formats effectively. Device log data is often large and contains time-series information, requiring data cleaning and anomaly detection during preprocessing. Fields involving specific medical terminology and technical parameters demand higher professionalism and accuracy in terminology matching from the knowledge base to ensure accurate classification and correlation analysis of adverse events. Diverse unit systems require unit standardization or conversion during data processing and results presentation.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Trigger Mode (Trigger Method) | Event-driven | Home medical adverse event data updates irregularly; process events as they occur. |
Text Chunk Size (Text Chunk Length) | 500-800 characters (500-800 characters) | Balances the semantic completeness of user feedback text with model processing efficiency. |
Knowledge base recall count (Knowledge Base Retrieval Count) | Top 10 entries (Top 10 entries) | Ensures coverage of relevant device information, historical adverse events, and medical terminology. |
Similarity threshold (Similarity Threshold) | 0.75 | Balances retrieval accuracy and relevance, reducing interference from irrelevant information. |
Entity Recognition Model | Medical Domain-specific | Accurately extracts professional entities such as symptoms, device models, and drug names. |
Data Cleaning Frequency | On-demand trigger | Cleans newly entered or updated data, avoiding unnecessary resource consumption. |
Common Pitfalls
- Symptom: After workflow initiation, some tools do not execute, or execution order is chaotic. Reason: Tool dependencies are not explicitly defined, leading to parallel execution or tools being skipped when preconditions are not met.
- Symptom: Extracted adverse event information fields are empty or inaccurate, such as device model or occurrence time. Reason: Unstructured text parsing tools have insufficient recognition capabilities for specific formats or expressions, or the knowledge base lacks corresponding terminology mappings.
- Symptom: Workflow performance degrades, and response times slow down after running for a period. Reason: The knowledge base or cache is not regularly cleared, leading to data redundancy and affecting query efficiency.
Validation Steps
- Perform end-to-end testing on typical adverse event report texts. Check if all key fields (e.g.,
device model,adverse event type,occurrence time) are accurately extracted and structured. - Simulate different types of data input, including structured tables and free text. Verify the workflow's trigger mechanism sensitivity and if all branch paths execute as expected.
- Review workflow logs. Confirm the execution status and time consumption of each tool, paying close attention to error codes or timeout messages, and compare them against expected thresholds.
- Randomly sample processed data. Manually compare it with raw data to assess the accuracy and completeness of information extraction. Determine acceptable thresholds based on actual business requirements.
The values provided are common starting points and should be measured against your 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.