Workflow Orchestration for Pharmacovigilance in Infection

Data Characteristics

Pharmacovigilance data in infection control originates from hospital information systems (HIS), electronic medical records (EMR), laboratory information systems (LIS), and manually reported adverse event forms by healthcare professionals. Data updates frequently. For example, patient medication records update in real time, lab results typically generate within hours, and adverse event reports are entered immediately upon discovery. Data structures are typically structured or semi-structured. For instance, medication orders include fields like Drug Name, Dosage, Administration Route, and Administration Time. Lab reports contain Test Item, Result Value, Reference Range, and Unit (e.g., mg/dL, U/L). Adverse event reports often include free-text descriptions but also have structured fields such as Event Type, Occurrence Time, and Involved Medication.

Constraints Imposed by Data Characteristics on Workflow Orchestration

The multi-source nature and high update frequency of infection control pharmacovigilance data require workflows to support real-time or near real-time triggering to capture potential risks promptly. The coexistence of structured and semi-structured data means that the workflow's data ingestion stage needs to support multiple parsers. This includes structured data parsing for standard HIS data tables and natural language processing capabilities for free text in adverse event reports. Furthermore, the diverse units and numerical ranges in the data demand higher precision from decision nodes and tool call nodes. This requires accurate unit conversion and threshold comparison logic to prevent misjudgments due to unit mismatches or incorrect numerical types. For example, processing blood drug concentration monitoring data must strictly differentiate therapeutic window units for various drugs.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`Trigger Type`	Message Trigger / Scheduled Trigger	Real-time monitoring of patient medication and lab result changes. Scheduled triggers can batch analyze historical data.
`Document Parser`	JSON Parser / Text Parser	HIS/LIS data is often in JSON format. Adverse event reports are mostly free text.
`Chunk size` (Chunk Size)	`500–800 characters` (characters)	Balances semantic completeness and model processing efficiency, suitable for adverse event reports.
`Similarity threshold` (Similarity Threshold)	`0.75`	Ensures recall relevance and avoids interference from irrelevant information, suitable for symptom and drug association analysis.
`Tool Call Timeout`	`60 seconds` (seconds)	Most external systems (e.g., drug insert databases) respond within this timeframe.
`Global Variable Type`	`Boolean` / `String`	Used to flag specific risk statuses or process branches, facilitating logic control in decision nodes.

Common Configuration Mistakes

Decision nodes misinterpreting boolean global variable states, leading to unexpected workflow branching. This occurs when boolean global values are incorrectly treated as strings, causing the decision logic to fail.
The AI chat node responds with "无法提供图片内容" (Unable to provide image content) after image recognition is enabled and an image is uploaded. This might be due to the image recognition feature not being correctly enabled on the model service side, or the image data format in the request not meeting API requirements.
After frontend packaging and deployment, the document parsing node fails to parse uploaded files, reporting a 404 error. This usually happens because the frontend file upload path configuration is invalid in the server environment, preventing the backend from locating the file resource.

How to Verify Configuration

Simulate submitting various data types (structured data, free-text adverse event reports) to observe if the workflow triggers as expected and to check the output of each node.
Upload a file containing an image to verify if the multimodal AI node correctly identifies the image content and generates a relevant response, confirming no "无法提供图片内容" (Unable to provide image content) prompt.
Check workflow logs to confirm that all tool call nodes execute correctly and return accurate results, and compare tool call results with expected outputs.
In the frontend deployment environment, upload a test file to ensure the document parsing node successfully parses the file content without reporting a 404 error.

Note: 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.