Workflow Orchestration for Clinical Trial Pre-screening in

Data Characteristics in This Category

Data within an e-pharmacy platform for clinical trial pre-screening originates from various sources. These include user health questionnaires, electronic medical record summaries, medication records, genetic testing reports, and internal platform data such as drug purchase history and browsing behavior. This data typically exists in structured formats (e.g., JSON user profiles, ICD-10 disease codes, ATC drug codes) and semi-structured formats (e.g., PDF lab reports, doctor's diagnoses). Data update frequency is high. User questionnaires may be submitted in real-time, medication records update instantly with purchase behavior, while electronic medical records and genetic reports might be uploaded in batches or on demand. Document structures vary, and field names and units differ across data sources. For example, WBC in a complete blood count report might use 10^9/L as its unit, while SNP locus information in a genetic report is identified by rsID.

Constraints Imposed by These Features on Workflow Orchestration

The diversity of e-pharmacy data introduces specific requirements for workflow orchestration. Multi-source heterogeneous data necessitates pre-processing modules for standardization and structural transformation. An example is extracting text from PDF reports and mapping it to predefined disease or symptom fields. High-frequency data updates require workflows to support real-time or near real-time processing, ensuring pre-screening results are based on the latest information. Complex document structures and inconsistent fields and units mean that knowledge base queries and rule evaluations within the workflow need more refined matching logic, moving beyond simple keyword reliance. For instance, different descriptions of "hypertension" from various sources require configuring synonym or hypernym matching rules. Furthermore, handling sensitive data involving user privacy mandates that workflows comply with data security and compliance requirements during data transmission and storage.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale for This Value
`maxContext`	`8192` characters	Ensures the complete user medical history and key medical information can be accommodated, preventing truncation that could lead to incomplete judgments.
`Chunk size`	`500` characters	Balances fine-grained semantic detail of knowledge base documents with contextual completeness, improving recall accuracy.
`Recall count`	`10` items	Covers more potentially relevant clinical trial criteria, reducing misjudgments due to omissions.
`Similarity threshold`	`0.75`	Balances recall and precision, filtering out weakly related information to focus on core matching elements.
`Rerank result count`	`3` items	Highlights the most relevant clinical trial projects, reducing user reading burden and improving matching efficiency.
`PARSER_TIMEOUT_SECONDS`	`120` seconds	Addresses parsing of complex, multi-page medical documents, preventing processing failures due to timeouts.

Three Common Mistakes

Global variables set in a session are empty in the next session. This occurs because workflows do not persist variables between sessions by default. Persistence requires dedicated storage or re-fetching variables at the start of each session.
When a workflow calls an external mcp interface that requires multiple parameters, but the workflow fails to prompt the user for all necessary parameters, the interface call fails, resulting in a 400 Bad Request error in the console.
The same input yields inconsistent results between the workflow debugging environment and the actual frontend testing environment. Debugging results are correct, but frontend results show significant discrepancies. This is due to differences in session context or user permission configurations between the frontend and debugging environments.

How to Verify Correct Configuration

Simulate various typical user cases, including healthy individuals, those with common chronic diseases, and patients with rare diseases, to verify if the pre-screening workflow correctly identifies eligible clinical trial projects.
Upload and test the parsing accuracy of various medical documents (e.g., lab reports, diagnostic certificates) contained in the knowledge base. Check if key fields are correctly extracted and structured.
Monitor logs and tracking data to observe workflow execution time, error rates, and the success rate of external interface calls. This ensures system stability and response speed meet requirements.
Randomly sample a proportion of pre-screening results and compare them with human expert judgments. Evaluate the precision and recall of the matching results, and adjust parameters like Similarity threshold based on the comparison.

Note: The values provided are common starting points. They should be measured against specific samples and adjusted as needed.

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.