Workflow Orchestration for Imaging Equipment Pharmacovigilance

Data Characteristics

Imaging equipment pharmacovigilance data primarily comes from Adverse Event (AE) reports and Medical Device Report (MDR) submissions from healthcare institutions. These reports are typically structured (e.g., XML, CSV) or semi-structured (e.g., tables and free-text descriptions within PDF documents). Data update frequencies vary; urgent events may be reported in real time, while routine reports are often summarized monthly or quarterly. Document structures are complex, containing fields such as device model, serial number, event occurrence time, de-identified patient information, adverse reaction descriptions, intervention measures, and device fault codes. Units involve time (date, hour), counts (instances), and physical quantities (voltage, current, radiation dose). Free-text descriptions often include extensive medical terminology, abbreviations, and imaging-specific vocabulary.

Constraints from Workflow Orchestration

The complexity of imaging equipment data imposes specific requirements on workflow orchestration. Parsing semi-structured data requires advanced text extraction and entity recognition capabilities, such as identifying device serial numbers or fault codes from PDF reports. Data updates are both real-time and batch-based, requiring flexible workflow scheduling to respond to urgent real-time reports and process periodic batch data imports. Domain-specific dictionaries are necessary to accurately understand the severity and causality of adverse reactions from specialized terms and abbreviations in free text. Reports may also lack critical fields, such as patient age or adverse reaction severity; workflows need to design corresponding missing value handling logic or fallback mechanisms. These constraints determine the complexity of data preprocessing, information extraction, and rule-based judgment stages within the workflow.

Configuration Settings

Common Pitfalls

• Workflow execution fails, logs show JSON Parse Error. This occurs when free text in some imaging equipment reports contains non-standard characters or formatting, leading to JSON structure parsing errors.
• The critical field device serial number in an adverse event report is empty. This happens due to OCR recognition errors in PDF reports or field extraction rules not covering all report template variations.
• AI conversations cannot retrieve the latest device recall information. This is because the knowledge base synchronization strategy is not configured for real-time updates, or the API access token for the external data source has expired.

Verification Steps

• Select ten imaging equipment adverse event reports from different sources and formats. Process them through the workflow and check if the extraction accuracy of key fields like device model, event type, and occurrence date in the output meets expectations.
• Simulate submitting a report containing a known severe adverse reaction. Verify if the workflow correctly triggers the preset alert or escalation process and check if the alert message includes the report number.
• Integrate a time query tool into the workflow. Ask for the current time to verify if the tool call is successful and returns the correct timestamp, confirming the external tool integration path is clear.
• Compare data before and after processing. Check the standardization of medical terminology in free-text descriptions, for example, if heart attack is correctly converted to myocardial infarction, ensuring terminology consistency.

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.