Workflow Orchestration for Pharmacovigilance Clinical Trial

Data Characteristics in this Domain

Pharmacovigilance data primarily originates from Adverse Drug Reaction (ADR) reports. Healthcare professionals, patients, and pharmaceutical companies typically submit these reports. Data updates frequently, especially during the post-market phase of a drug. Report documents vary in structure. They include free-text descriptions, structured fields (e.g., patient demographics, medication history, ADR onset time, severity, outcome), and laboratory test results. Field content involves medical terminology such as ICD-10 disease codes, ATC drug classifications, and various dose units (mg, g, ml, IU) and time units (days, hours, minutes). Some data may include scanned images or PDF versions of original reports.

Constraints Imposed by These Characteristics on Workflow Orchestration

High-frequency ADR report updates require the workflow to support real-time or near real-time data processing. This ensures timely pre-screening. Document structural diversity dictates that the workflow integrates various parsing tools. Examples include OCR technology for scanned documents and Natural Language Processing (NLP) models for extracting key information from free text. The complexity of medical terminology and coding systems means that knowledge base retrieval and entity recognition within the workflow must rely on specialized medical dictionaries and ontologies. Diverse dose and time units require the workflow to perform unit conversion and standardization. This prevents misjudgments due to inconsistent units. Furthermore, the privacy sensitivity of original reports demands strict data anonymization and access control.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`4000 characters`	Accommodates typical ADR report text length, reduces truncation
`Chunk size` (Segment Length)	`500 characters`	Balances semantic completeness with efficient vectorization
`Recall count` (Recall Count)	`Top 10`	Increases relevant information coverage, reduces underreporting risk
`Similarity threshold` (Similarity Threshold)	`0.75`	Balances recall precision and recall rate, reduces false positives
`Rerank result count` (Reranked Return Count)	`Top 3`	Optimizes the accuracy and conciseness of the final output
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Handles complex PDF reports or time-consuming OCR scenarios

Three Common Pitfalls

Workflow execution times out or some tools are not invoked. System logs show Workflow execution timeout or Tool call skipped. This occurs when a single tool in the workflow takes too long to execute or when inter-tool dependencies are configured incorrectly. This prevents subsequent tools from starting as expected.
AI responses contain fixed statements or templates unrelated to the knowledge base. The output text always appends non-core content at the end. This happens when the model's base instructions or system preset prompts are not effectively overridden or modified.
Clinical trial pre-screening results show significant accuracy fluctuations. Batch processing exhibits notable changes in false positive or false negative rates. This results from outdated knowledge bases or improper vector database index rebuilding strategies. This prevents the model from accessing the latest pharmacovigilance knowledge.

How to Verify Correct Configuration

Select a batch of typical ADR reports containing known adverse events. Run the workflow and verify if the pre-screening results match expectations, especially for key field extraction and classification.
Observe the average workflow execution time under different network loads and data volumes. Ensure processing efficiency meets real-time requirements. Record the time taken for each stage as a performance baseline.
Randomly sample multiple processed reports. Check if the output text contains any fixed phrases unrelated to the business. Confirm the purity of the model's output.
Regularly compare workflow processing results with expert human judgment. Calculate recall and precision rates to evaluate pre-screening effectiveness. Use this as a basis for adjusting Similarity threshold (Similarity Threshold) and Rerank result count (Reranked Return Count).

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.