Data Characteristics for This Category
Clinical Decision Support (CDS) systems for clinical trial pre-screening primarily process data from Electronic Health Records (EHR), medical imaging reports, laboratory test results, and genomic data. Data update frequencies vary; EHR data may update in real-time, while genomic data is relatively static. Document structures are diverse, including unstructured physician notes, semi-structured imaging report text, and structured laboratory metrics. Fields involve patient demographics, diagnostic codes (e.g., ICD-10), medication records, vital signs, and various biomarker values. Units cover common International System of Units (SI) like mmol/L, mg/dL, specific test units such as U/L, pg/mL, and even gene locus information.
Constraints Imposed by These Characteristics on Workflow Orchestration
Data source diversity requires workflows to flexibly integrate various data interfaces. For example, use API nodes for EHR data and file upload nodes for imaging report text. The presence of unstructured text necessitates integrating text parsing and Named Entity Recognition (NER) models into the workflow to extract structured information for subsequent decisions. Differences in data update frequency influence trigger mechanism design. Real-time pre-screening for new inpatients may require event-driven triggers, while periodic screening for review patients can use scheduled tasks. Furthermore, field and unit standardization is critical. Workflows must include data cleaning and transformation steps to ensure uniform format and units across different data sources before logical judgments, preventing errors from unit mismatches.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 4000 tokens | Ensures the AI model can process the full patient medical record context, preventing information truncation. |
similarityThreshold | 0.75 | Balances recall and precision, filtering out irrelevant clinical guidelines or trial criteria. |
maxRetrieveCount | 5 | Limits the number of retrieved knowledge base entries, reducing model processing load and focusing on core information. |
chunkSize | 800 characters | Optimizes text chunk size, ensuring each chunk contains sufficient context for retrieval. |
timeoutSeconds | 60 seconds | Allows sufficient time to process complex medical record data and model inference, preventing task interruption. |
variableMapping | 患者ID -> patient_id | Ensures correct field name mapping between different systems, enabling seamless data flow. |
Three Common Pitfalls
- Symptom: The workflow stops executing after a user selection node, and subsequent nodes do not respond. Cause: The
User Selectionnode is not correctly configured for output variables, or subsequent nodes do not correctly reference this output, leading to data flow interruption. - Symptom: Some critical fields in the AI model's output are null, even though the input data contains the corresponding information. Cause: The text parsing or entity recognition model fails to accurately extract the required fields, or variable update operations on the model's output within the workflow are incorrect, failing to overwrite old values.
- Symptom: Clinical trial pre-screening results do not match expectations; many patients are incorrectly excluded or included. Cause: The
similarityThresholdis set improperly, or the granularity of trial criteria documents in the knowledge base is too coarse, leading to imprecise matching.
How to Verify Configuration
- For typical case data, run the workflow step-by-step in debug mode. Check if the output variables of each node meet expectations, especially data format and numerical ranges.
- Validate data cleaning and unit conversion nodes. Randomly sample data from different sources and verify if converted field values and units are consistent and correct.
- Use a set of test cases with known screening results. Run the workflow and compare its pre-screening conclusions with actual outcomes. Adjust judgment logic and thresholds based on actual business requirements.
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.