Workflow Orchestration for DTP Pharmacy Clinical Trial

Data Characteristics

Data for DTP pharmacy clinical trial pre-screening originates from patient medical records, prescription logs, medication adherence data, and patient self-reported information. This data is often unstructured or semi-structured, including PDF medical imaging reports, scanned handwritten doctor's notes, CSV drug sales records, or structured electronic health records. Data updates frequently, especially after patient follow-ups or medication purchases. Document structures are complex, containing numerous medical terms, abbreviations, and numerical values. Examples include WBC, HGB in complete blood count reports, ALT, AST in liver function reports, and reference ranges and measured values for various indicators, often with units like mmol/L, g/L, IU/L.

Constraints Imposed by These Characteristics on Workflow Orchestration

The highly unstructured nature of DTP pharmacy data requires robust document parsing capabilities during data ingestion. Workflows must accurately extract text from various formats, including PDFs and images. High update frequency necessitates real-time or near real-time trigger mechanisms. This includes receiving new data via API interfaces or processing incremental data in batches on a schedule, ensuring the timeliness of pre-screening results. The complex medical terminology and numerical values in documents demand advanced knowledge base construction and query capabilities, requiring specialized medical dictionaries and numerical recognition rules. Furthermore, given the involvement of patient private information, workflows must strictly adhere to data security and compliance requirements during data processing and output, ensuring sensitive information is anonymized or encrypted to prevent leakage.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`500–800 characters` (characters)	Accommodates medical text paragraph length, balancing semantic completeness and recall efficiency.
`Recall count` (Recall Count)	`Top 10–15 entries` (top 10–15 items)	Ensures coverage of key information related to clinical trial inclusion/exclusion criteria, reducing false negatives.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Balances recall accuracy and recall rate, preventing misjudgments due to semantic deviation.
`Rerank result count` (Rerank Return Count)	`Top 3–5 entries` (top 3–5 items)	Improves the relevance of the final results, reducing the burden on the large language model from processing irrelevant information.
`MAX_TOKENS`	`2048–4096`	Accommodates the level of detail in medical reports, ensuring complete context input to the large language model.
`Parsing Timeout`	`600 seconds` (seconds)	Addresses potentially long parsing times for large or complex PDF documents.

Common Pitfalls

AI conversation node output is empty. This might occur if variable names referenced in the prompt do not match the actual passed variable names, or if variable values are not correctly assigned during transmission.
Knowledge base search results do not meet expectations. This could be due to the knowledge base search node's Similarity threshold (Similarity Threshold) being set too high, filtering out relevant documents.
Workflow experiences a timeout error when processing specific patient data. This usually happens because the Parsing Timeout is set too low, failing to process medical reports containing many images or complex tables.

How to Verify Configuration

Select multiple real DTP pharmacy patient datasets, run the workflow, and compare pre-screening results with manual assessments.
Check workflow logs to confirm that data input and output for each node are as expected. Pay special attention to whether the Recall count (Recall Count) and Similarity threshold (Similarity Threshold) for knowledge base searches are effective.
Verify that sensitive patient information is correctly anonymized or encrypted during workflow processing, complying with privacy protection requirements.
Simulate high-concurrency scenarios to test workflow stability and its ability to complete tasks within the Parsing Timeout.

Note: The values provided are common starting points. Measure against specific samples to determine optimal settings.

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.