Workflow Orchestration for Phase II-III Clinical Trial

Data Characteristics

Phase II-III clinical trial pre-screening data originates from Investigator's Brochures (IB), Clinical Study Protocols (CSP), Informed Consent Forms (ICF), Case Report Form (CRF) templates, and relevant medical literature. Data updates typically align with trial progress, such as protocol revisions or interim reports on subject screening and enrollment. Documents are complex, containing extensive unstructured text like inclusion/exclusion criteria, safety assessment metrics, and Adverse Event (AE) descriptions. Data fields encompass subject demographics, baseline characteristics, disease diagnoses, concomitant medications, laboratory test results, imaging data, and medical history. Units vary, for example, mg/dL, mmol/L, kPa, mm, and many medical term abbreviations are present.

Constraints Imposed by These Characteristics on Workflow Orchestration

The high complexity and diversity of Phase II-III clinical pre-screening data impose specific requirements on workflow orchestration. Interpreting unstructured text demands strong natural language processing capabilities to accurately extract key information, such as identifying specific numerical ranges or disease states from inclusion/exclusion criteria. The phased nature of data updates means the workflow must support periodic or on-demand data synchronization and processing, ensuring pre-screening logic relies on the latest information. Complex document structures require flexible file parsing capabilities within the workflow to handle various document formats and layouts. Standardizing numerous medical terms and units is critical; the workflow needs to integrate medical terminology mapping services to unify different expressions and prevent pre-screening errors due to inconsistent terminology. The richness of data fields and diversity of units necessitate that conditional judgment nodes in the workflow precisely handle numerical comparisons and unit conversions.

Configuration Settings

Configuration Item	Suggested Value	Rationale
`maxContext`	`3000` characters	Documents like clinical trial protocols are content-dense. A longer context window ensures a complete understanding of complex inclusion/exclusion criteria and medical descriptions.
`PARSE_FILE_TIMEOUT_SECONDS`	`600` seconds	Parsing large PDF Investigator's Brochures and clinical trial protocols takes time. Increasing the timeout prevents parsing interruptions.
`Chunk size` (Chunk Length)	`800–1200` characters	Considering the coherence and complexity of medical text, a longer chunk length helps maintain contextual integrity and reduces the risk of critical information being split.
`Similarity threshold` (Similarity Threshold)	`0.78`	Pre-screening requires a high degree of matching between patient characteristics and criteria. A higher similarity threshold helps filter for more suitable subjects and reduces misjudgments.
`Rerank result count` (Reranked Top K)	`Top 10 entries` (Top 10)	Pre-screening involves multi-dimensional considerations. Increasing the number of reranked results provides more comprehensive candidate information for subsequent decision nodes, such as multiple potentially relevant medical test results.
`Database Connection Timeout` (Database Connection Timeout)	`30` seconds	When connecting to external Laboratory Information Systems (LIS) or Electronic Health Record (EHR) databases to retrieve patient data, network fluctuations or complex queries can cause delays. Extending the timeout improves stability.

Common Pitfalls

A Failed to connect to jyfkk:1433 error during workflow execution often indicates an incorrect database connection string configuration or a closed firewall port 1433.
An empty result from a knowledge base query node, where the response field is empty, typically means background knowledge documents were not correctly vectorized after upload, leading to recall failure.
Numerous misjudgments in pre-screening results, such as marking subjects who do not meet inclusion criteria as compliant, often occur because medical terms in conditional judgment nodes are not standardized, preventing accurate system comparison.

Verification Steps

Upload a simulated subject case report, run the workflow, and check the final pre-screening results to confirm consistency with human judgment above a set threshold.
Use FastGPT's debugging features to inspect query and context content at critical knowledge base query nodes, ensuring the model receives complete and accurate contextual information.
Integrate a logging node into the workflow to monitor external database connection status and query times. This ensures the Database Connection Timeout parameter is appropriately set, avoiding frequent connection failures or prolonged waits.
Randomly select multiple test documents containing complex medical terminology. Execute the file parsing node and check if the parsed text accurately retains all key fields and values, especially laboratory test results with units.

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.