Workflow Orchestration for Clinical Trial Pre-screening in

Data Characteristics in this Domain

Clinical trial pre-screening data in health management primarily originates from physical examination reports, wearable device records, Electronic Health Records (EHR), and user-completed health questionnaires. Data update frequency varies by source. Physical examination reports typically update annually or semi-annually. Wearable device data may update hourly or daily. EHR data generates in real-time with medical visit records. Document structures are complex, potentially including unstructured doctor's diagnostic text, semi-structured laboratory and examination result tables, and structured personal physiological indicators (e.g., blood pressure in mmHg, blood glucose in mmol/L, heart rate in bpm). Fields are diverse, involving medical terminology, physiological indicators, and lifestyle descriptions. Unit conversion and standardization present common challenges.

Constraints Imposed by These Characteristics on Workflow Orchestration

Heterogeneous data sources require robust multi-format parsing capabilities in the data ingestion stage, supporting document types like PDF, XML, and JSON. High-frequency data updates (e.g., wearable device data) necessitate workflow support for streaming or high-frequency batch processing to prevent data lag from impacting pre-screening accuracy. The presence of unstructured text demands higher recall and comprehension capabilities from Natural Language Processing (NLP) modules, requiring features like keyword extraction and entity recognition. Diverse fields and inconsistent units in structured data require additional standardization and normalization steps in the data preprocessing stage to ensure accuracy in subsequent rule-based judgments and model inference. For example, blood pressure data may be recorded in mmHg or kPa; the workflow must uniformly convert it to a standard unit.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for Recommendation
`maxContext`	`2000 token`	Ensures accommodation of critical descriptive text from physical examination reports, balancing processing efficiency with information completeness.
`Similarity threshold` (Similarity Threshold)	`0.75`	Balances recall and precision, reducing false positives and false negatives, suitable for fuzzy matching of medical concepts.
`Recall count` (Retrieval Count)	`Top 10 entries` (Top 10)	Retrieves sufficient relevant medical guidelines and standards from the knowledge base to support complex downstream decisions.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (600 seconds)	Processing large physical examination reports or EHR documents can be time-consuming; this avoids parsing timeouts that lead to task failure.
`Chunk size` (Segment Length)	`500 characters` (500 characters)	Optimizes long text chunking, ensuring each segment contains complete semantic information, improving embedding quality.
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Accommodates the file size of physical examination reports that include multiple scanned documents or detailed examination results.

Common Pitfalls

A Failed to create post presigned url error during file upload typically indicates insufficient object storage (OSS) configuration permissions or network connectivity issues, preventing the generation of a pre-signed URL.
Knowledge base search module results that do not meet expectations, such as recalling irrelevant information or missing critical knowledge, often stem from improper variable reference configuration, failing to correctly map user health data to query parameters.
Workflow execution timeouts, especially when processing large amounts of unstructured text, may occur if the PARSE_FILE_TIMEOUT_SECONDS parameter is set too low, not adequately accounting for the time required to parse complex documents.

Verification Steps

Upload simulated health reports in various formats (PDF, TXT, JSON) to observe if the workflow can correctly parse and extract key information. Check log outputs for parsing errors.
Run the pre-screening workflow against a set of simulated patient data known to meet or not meet clinical trial criteria. Compare the output pre-screening results with expected outcomes.
In the knowledge base search module, test different combinations of health indicators as query variables. Verify that the retrieved medical guidelines or standards are accurate and relevant, and check if the Recall count (Retrieval Count) matches the configuration.
Monitor workflow execution time. Ensure all steps complete within an acceptable timeframe when processing normal data volumes, avoiding timeout states.

The values provided are common starting points and should be measured against the reader's own 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.