Data Characteristics for This Category
Pharmacovigilance data for rehabilitation devices primarily originates from post-market surveillance reports, clinical trial data, internal quality management system records from manufacturers, and risk alerts issued by regulatory bodies. Data update frequencies vary. Post-market surveillance reports may be aggregated quarterly or annually, while clinical trial data is typically entered once after a trial concludes, but safety events trigger immediate updates. Document structures are diverse, including unstructured free-text reports (e.g., patient descriptions, physician diagnoses), semi-structured tabular data (e.g., symptoms, occurrence dates, severity in adverse event report forms), and structured coded information (e.g., generic medical device names, event codes like MedDRA). Beyond common patient information and event descriptions, fields also include specific details such as device model, serial number, usage duration, and fault codes. Units involve dosage units, time units (days, hours), and device operating parameter units (e.g., volts, amperes).
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The diversity of rehabilitation device data sources demands robust multi-source heterogeneous data ingestion capabilities in the workflow to handle various document formats. The irregular data update frequency means the workflow's trigger mechanism must support a combination of scheduled tasks and event-driven responses. For example, it should periodically scan for new surveillance reports while also responding to real-time submissions of urgent adverse events. Documents contain extensive unstructured text, requiring high accuracy for keyword extraction and entity recognition, necessitating dedicated natural language processing modules. Specific fields like device model and fault codes are crucial for adverse event correlation analysis; the workflow must ensure these key variables are accurately identified and passed to subsequent analysis steps. Additionally, since rehabilitation devices may involve software updates or firmware upgrades, the workflow must consider the dynamic nature of version information to avoid data misinterpretation or failed correlations due to version discrepancies.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 3000 characters | Rehabilitation device adverse event reports often contain detailed user descriptions and device conditions; this ensures capture of critical information. |
Chunk size | 500 characters | Balances semantic completeness of text with retrieval efficiency, avoiding excessive fragmentation or overly large single segments. |
Recall count | 8 entries | Ensures sufficient potentially relevant documents are covered in the initial retrieval phase, improving the accuracy of subsequent re-ranking. |
Similarity threshold | 0.75 | Filters out low-relevance documents, focusing on information highly matched to rehabilitation device adverse events. |
Rerank result count | 3 entries | After re-ranking, returns the most relevant core information, reducing the burden on downstream modules and improving response speed. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Some rehabilitation device-related documents (e.g., clinical reports) are lengthy; this allows ample time for parsing, preventing data loss due to timeouts. |
Extraction Variable | Calibrated by actual measurement, e.g., device model: (.*?) | Ensures accurate extraction of key structured information like device model, serial number, and fault codes from free text for subsequent correlation and analysis. |
Three Common Mistakes
- Symptom: Intermediate modules in the workflow cannot obtain the device serial number passed from preceding modules, leading to failed correlation analysis. Cause: Global variables are not correctly configured or are subject to scope limitations, preventing proper variable transmission between different modules.
- Symptom: During adverse event report processing, some critical symptom terms are not recognized or extracted. Cause: The keyword extraction model is not optimized for specific medical terminology and user descriptions unique to rehabilitation devices, resulting in insufficient vocabulary coverage.
- Symptom: Newly uploaded regulatory alert documents do not timely trigger the risk assessment process. Cause: The workflow's trigger mechanism relies solely on scheduled scans, lacking real-time listening and response to specific events (e.g., file uploads).
How to Confirm Correct Configuration
- Select rehabilitation device adverse event reports containing key information such as device model and fault codes. Verify the workflow can accurately extract all predefined variables and correctly reference them in subsequent modules.
- Upload rehabilitation device-related documents in various formats (PDF, Word, plain text). Observe whether the file parsing module successfully processes them and generates usable text content.
- Simulate an urgent adverse event. Trigger the workflow via API or a specific interface. Check if the entire process, from data ingestion to initial risk classification, completes within the expected timeframe.
- Use the logging system to trace workflow execution. Verify the input and output of each module, confirming correct data flow, with particular attention to the transmission status of global variables.
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.