Data Characteristics in this Category
Real-World Study (RWS) data for clinical trial pre-screening primarily originates from Electronic Health Records (EHR), medical insurance claims databases, disease registries, and patient-reported outcomes (PROs). This data typically exists as unstructured text, semi-structured tables, and structured numerical values. Update frequencies vary from daily (e.g., hospitalization records) to quarterly or annually (e.g., some follow-up data). Document structures are diverse. For example, EHRs may contain physician notes, lab and imaging reports, and medication orders. These are often free text or text with specific codes. Fields and units differ significantly across sources. For instance, laboratory test results may have varying units depending on the hospital or time. Disease diagnoses commonly use ICD codes, while medication records involve generic names, dosages, and frequencies.
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The diverse and unstructured nature of RWS data places specific demands on workflow orchestration. First, multi-source heterogeneous data requires flexible data ingestion and preprocessing modules to handle different data formats and update frequencies. For example, EHR free text needs Natural Language Processing (NLP) for entity recognition and relationship extraction. Second, inconsistencies in fields and units necessitate data standardization and cleaning steps within the workflow to ensure subsequent analysis accuracy. An example is unifying laboratory indicators with different units to standard units. Asynchronous data updates require workflow designs with incremental update and version management capabilities to avoid reprocessing historical data. Additionally, due to data sensitivity, workflows must integrate strict data anonymization and privacy protection mechanisms. During pre-screening, implementing complex clinical logic requires workflows to orchestrate multi-step conditional judgments and rule engines for precise patient screening based on inclusion criteria.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale for this Value |
|---|---|---|
maxContext | 3000 | Accommodates longer patient records and diagnostic reports in EHRs, ensuring context completeness. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances textual semantic completeness and retrieval efficiency, avoiding noise from overly long segments. |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures precise matching of clinical terms and disease features, reducing false positives. |
Recall count (Number of Retrieved Items) | Top 10 entries (top 10) | Ensures sufficient relevant information is covered for complex queries while controlling processing volume. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Addresses parsing requirements for large or complex documents (e.g., PDF medical reports). |
Knowledge Base Data Update Frequency | daily incremental synchronization | Real-world data updates frequently, ensuring pre-screening is based on the latest patient information. |
Three Common Pitfalls
- Knowledge base retrieval nodes fail to obtain preset global variable values. This occurs because variable scope is not configured correctly, preventing the node from accessing expected parameters during execution.
- Model configuration errors occur during workflow execution. A common cause is that the selected model does not support the input format or length limits for the current data processing task, such as a model unable to process overly long text sequences.
- After the data standardization step, some critical field values remain empty or incorrectly formatted. This is typically due to regular expressions or mapping rules failing to cover all data variations or edge cases.
How to Confirm Correct Configuration
- After each critical node in the workflow, check output logs to confirm data format, field values, and quantities match expectations.
- Run the workflow using a set of simulated patient data known to meet and not meet pre-screening criteria to verify the accuracy of the final screening results.
- Monitor workflow execution time and resource consumption. Ensure performance meets requirements when processing large-scale data, and compare against set timeout thresholds.
- Periodically sample and inspect data processed by the workflow to verify the effectiveness of data cleaning, standardization, and anonymization rules.
The values provided are common starting points. Measure them against your 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.