Data Characteristics
Phase I clinical trial pre-screening data originates from subject recruitment systems, Electronic Health Record (EHR) systems, Laboratory Information Management Systems (LIMS), and demographic databases. This data combines structured and unstructured formats. Structured data includes demographics (age, gender), historical disease codes (ICD-10), vital signs (blood pressure, heart rate), and lab results (blood count, liver/kidney function indicators). This data typically stores as CSV, JSON, or database records. Unstructured data includes physician diagnostic notes, progress notes, imaging reports (DICOM metadata), and patient self-reported questionnaires. This data primarily consists of free text or PDF documents. Data update frequency is high during the recruitment period, potentially multiple times daily during subject screening. Field units strictly adhere to international standards; for example, blood pressure units are mmHg, and lab indicators use units like mmol/L, g/dL.
Constraints Imposed by Data Characteristics on Workflow Orchestration
Phase I clinical pre-screening data characteristics impose specific constraints on workflow orchestration. Diverse data sources require the workflow to flexibly integrate various data interfaces. Examples include database connectors, HTTP API call nodes, and file upload parsers. The mix of structured and unstructured data means the workflow must include AI nodes for text extraction, entity recognition, and structured data parsing to unify data formats. High update frequency demands near real-time processing capabilities in the workflow. This can be achieved through message queue triggers or high-frequency scheduled tasks to ensure screening logic uses the latest data. Strict unit standards and medical terminology require AI models to precisely understand specialized vocabulary when processing natural language. They must also perform unit conversions and validations to prevent screening errors due to misinterpretation. Clinical data sensitivity mandates strict adherence to data security and privacy protection protocols during data transmission and processing. Examples include data anonymization and access control.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Data Source Connection Type | HTTP API, Database Connector, File Upload | To accommodate EHR, LIMS system interfaces, and patient questionnaire files |
Text Segmentation Strategy | 800–1200 characters, split by sentence or paragraph | Balances contextual completeness with model processing efficiency; avoids information loss or overload |
Recall Count | Top 5 | Ensures screening logic covers key information while avoiding irrelevant information interference |
Similarity Threshold | 0.75 | Balances accuracy and recall rate; reduces the risk of false positives and false negatives |
File Parsing Timeout | 600 seconds | Allows sufficient time for parsing large PDF medical records or imaging report metadata |
AI Model Version | e.g., GPT-4o or ERNIE-4.0 | Ensures the model has sufficient understanding of medical terminology and complex logic |
Common Pitfalls
- AI conversation nodes return null values or errors. This occurs when large models fail to parse specialized terminology or complex logic due to excessively long input or unclear instructions.
- Chart tools generate blank charts after invocation. This happens when the data processing stage in the workflow fails to correctly extract numerical data required for the chart, resulting in an empty dataset.
- HTTP requests return image URLs that do not render directly in the Q&A interface. This is because the frontend rendering logic is not configured to parse and display image URLs of specific MIME types.
Verification Steps
- Upload a typical medical record PDF document in the workflow. Check if the knowledge base segmentation preview function correctly identifies and extracts key medical entities and laboratory indicators.
- Execute a pre-screening simulation that includes AI screening logic. Verify if the system accurately determines subject eligibility based on predefined inclusion/exclusion criteria. For example, compare model output with human judgment results for consistency.
- Check the status logs of each data connector (e.g., database, API) in the workflow. Confirm that data synchronization and transmission are error-free, and verify that field units are correct.
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.