Real-World Evidence Data Characteristics
Real-World Evidence (RWE) product data primarily originates from Electronic Health Records (EHR), medical claims databases, registries, wearable devices, and patient-reported outcomes (PROs). Data update frequencies vary; EHR data might update in real-time, while claims data typically updates in monthly or quarterly batches. Document structures are diverse, including unstructured clinical notes, semi-structured medical reports, and structured laboratory results and diagnostic codes. Fields often include patient identifiers, diagnostic codes (e.g., ICD-10), treatment plans, drug dosages, follow-up records, and various biomarker indicators. Units are complex; for example, drug dosages might be in milligrams (mg) or micrograms (μg), blood pressure in millimeters of mercury (mmHg), and blood test results in moles per liter (mol/L) or grams per liter (g/L).
Constraints Imposed by These Characteristics on Workflow Orchestration
The diversity and unstructured nature of RWE data demand robust text parsing and entity extraction capabilities within the workflow. Varying update frequencies necessitate a hybrid model supporting periodic data ingestion and event-driven real-time processing. The complexity of document structures, especially the large volume of unstructured text, increases the difficulty of data cleaning and standardization. This requires integrating Natural Language Processing (NLP) components for semantic understanding and information extraction. The heterogeneity of fields and units places high demands on data transformation and normalization modules within the workflow, ensuring effective integration and comparison of data from different sources and with different units. These constraints collectively dictate that RWE workflows must integrate various data processing, analysis, and validation components and support flexible orchestration to address data quality challenges.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
chunkOverlap | 50 characters | Ensures context continuity and prevents critical information loss at segment boundaries, especially when processing clinical notes. |
maxContext | 4096 tokens | Balances the model's ability to understand long texts with computational resource consumption, adapting to medical reports of varying lengths. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Most RWE documents (e.g., clinical trial reports in PDF format) are large, requiring longer parsing times. |
recall_top_k | top 10 | Increases the probability of recalling relevant information from vast RWE data, covering more potential associations. |
similarity_threshold | 0.75 | Filters out irrelevant recall results, improving the accuracy and specificity of consultation responses. |
rerank_top_n | top 3 | Further optimizes ranking based on initial recall through reranking, prioritizing the most relevant RWE evidence. |
Three Common Pitfalls
- During workflow debugging, a
workflow error {"message":"Dangerous behavioprompt typically indicates that a component's input parameters are not as expected, triggering an internal security policy. - When clicking a button to retrieve a process variable, an empty or old variable value occurs because of component execution order or data flow update delays; the variable has not been assigned a value when accessed.
- Adding new parameters to an existing component causes workflow execution failure, likely because the new parameters are not correctly registered or handled in the component code, leading to a runtime error.
Validation Steps
- Execute the end-to-end workflow and check if the final RWE consultation report contains all expected data points and analysis results.
- Review workflow logs to verify key component inputs and outputs, confirming that data transformation and standardization steps, such as unit unification to international standard units, are completed as expected.
- Select specific patient cases and compare AI-generated consultation responses with expert human judgments to assess the accuracy and completeness of responses. Use this comparison to calibrate acceptable thresholds for parameters like
similarity_threshold. - Simulate data update scenarios to observe if the workflow correctly processes new or modified EHR records and reflects them in query results in a timely manner.
The values given 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.