Workflow Orchestration for Pharmacovigilance in Clinical

Data Characteristics in this Category

Clinical decision support in pharmacovigilance primarily uses data from adverse drug reaction (ADR) reporting systems, electronic health record (EHR) systems, medical literature databases, and drug prescribing information. This data updates frequently. ADR reports might be submitted in real-time, and literature data updates according to journal publication cycles. Document structures are mostly semi-structured and unstructured. ADR reports often include free-text descriptions, basic patient information, medication history, detailed adverse event information, and more. Fields include drug generic name, batch number, dosage, administration route, ADR symptom description, diagnostic results, treatment measures, and outcomes. Units cover dosage units (mg, g), time units (hours, days), and frequency units (times/day). Some data also involves medical terminology codes, such as ICD-10 and MedDRA.

Constraints Imposed by These Characteristics on Workflow Orchestration

High-frequency data sources require workflows with real-time or near real-time data ingestion capabilities to ensure timely decision support. A high proportion of semi-structured and unstructured data means workflows must integrate advanced text processing nodes, such as entity recognition, relation extraction, and medical terminology standardization, to convert free text into structured, analyzable features. Integrating heterogeneous data from multiple sources demands high standards for data cleaning, deduplication, and merging. For example, patient medication records and ADR reports might link via patient IDs, but inconsistent data formats or field names require data transformation nodes within the workflow for mapping. Additionally, data involving medical coding requires workflows to call external APIs or internal knowledge bases for code conversion and semantic matching to ensure analytical accuracy. The rigor and interpretability of output results also necessitate adding multi-round verification or manual review steps before final decision generation.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`20000 characters`	Complex medical reports and multi-document context analysis require a larger context window.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large files (e.g., clinical trial reports) can be time-consuming; this prevents processing interruptions due to timeouts.
`Recall Count`	`Top 15`	Ensures enough relevant ADR cases or literature evidence are recalled in pharmacovigilance scenarios.
`Similarity Threshold`	`0.75–0.85`	Balances recall and precision, avoiding irrelevant information while not missing potential associations.
`Rerank Return Count`	`Top 8`	Selects the most relevant few pieces of information for the user, improving decision efficiency.
`DingTalk Webhook`	`Triggered by event`	Real-time push notifications for ADR alerts or review notices ensure timely information delivery to relevant personnel.

Three Common Pitfalls

After an HTTP node retrieves a BLOB object, it cannot directly provide a download link in the chat interface. This is because the default chat interface does not directly render or parse BLOB data into downloadable links. The BLOB data needs to be uploaded to an external storage service, and a publicly accessible URL generated.
After a knowledge base search in a workflow, the reply only contains knowledge base chunk data. This occurs when the reply node is not configured to extract information from the knowledge base q field, or the reply template does not specify q as an output parameter.
Advanced orchestration features are unavailable, and the interface displays "insufficient permissions." This means the current account role is not authorized to use the advanced orchestration module. Contact an administrator to elevate permission levels.

How to Verify Configuration

Simulate submitting an ADR report containing free-text descriptions. Observe if the workflow correctly identifies and extracts key entities like drug names, ADR symptoms, and dosages. Verify if the extraction results meet expectations.
Set up test data in the workflow to trigger the knowledge base search node. Check if the returned Recall Count and Rerank Return Count match the configuration. Evaluate if the Similarity of the returned content meets business requirements.
Configure a DingTalk Webhook node. When the workflow processes a specific type of ADR event, check if the DingTalk group receives notification messages in the predefined format.

The values provided are common starting points and should be measured against specific 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.