Data Characteristics
CSOs (Contract Sales Organizations) primarily handle patient recruitment databases, de-identified Electronic Health Record (EHR) data, medical imaging reports, genomic data, and Clinical Study Protocol documents for clinical trial pre-screening. This data combines structured formats (database records, CSV, JSON) and unstructured formats (PDF medical reports, Word protocol documents). Data update frequencies vary: patient recruitment databases update in real-time, EHR data synchronizes in batches, and protocols update upon revision. Unstructured documents are rich in medical terminology, abbreviations, and specific formatting. For example, medical reports may include fixed sections like "Chief Complaint," "History of Present Illness," "Physical Examination," and "Diagnosis," but specific content varies. Fields and units are highly specialized, such as "Platelet Count" (unit: 10^9/L), "Creatinine Clearance" (unit: mL/min), "Tumor Size" (unit: cm). Multilingual content and mixed encoding standards (e.g., ICD-10, SNOMED CT) are also common.
Constraints Imposed by Data Characteristics on Workflow Orchestration
The highly heterogeneous nature of CSO pre-screening data demands robust data preprocessing capabilities in the workflow. Complex formats and medical terminology in unstructured documents make traditional keyword matching inefficient. Integrate advanced NLP models for entity recognition and relationship extraction to accurately parse inclusion/exclusion criteria. Data heterogeneity and inconsistencies across multiple sources, such as differing naming conventions for the same metric in various EHR systems, require data cleaning and standardization steps within the workflow design. High-frequency patient recruitment data and low-frequency clinical protocol documents necessitate differentiated data ingestion strategies. For instance, patient data triggers real-time processing, while protocol documents undergo periodic batch processing. The specialized nature of fields and units requires precise variable type definitions and numerical comparisons in the workflow to prevent misjudgments due to unit mismatches. Furthermore, when handling sensitive patient data, the workflow must strictly adhere to data security and privacy regulations, incorporating de-identification and access control during orchestration to ensure compliance.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 4000 characters | Balances understanding long document segments with model processing efficiency, avoiding redundancy or truncation from overly long contexts. |
Chunk size (Segment Length) | 800–1200 characters | Ensures each segment contains sufficient contextual information for language models to understand the complete semantics of medical text. |
Recall count (Recall Count) | 8–15 items | Increases recall coverage to reduce omissions, while avoiding excessive irrelevant information that burdens the model. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Balances recall precision and recall rate, reducing false positives and ensuring high relevance of recall results to inclusion/exclusion criteria. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Addresses complex parsing demands for large PDF medical records or protocol documents, preventing timeouts. |
Rerank result count (Rerank Return Count) | 3–5 items | Refines the final results, presenting the most relevant document segments or entity information to downstream decision-making. |
Common Mistakes
- Phenomenon: Workflow execution interrupts with an "API call failed, model returned error code
400." Reason: The text content extraction node fails to parse unstructured medical records containing extensive medical terminology and complex grammatical structures due to improper prompt design or selection of a model unsuitable for specialized domains. - Phenomenon: The pre-screening results output by the workflow show unit confusion or comparison errors for numerical indicators like patient age or weight. Reason: The data cleaning and standardization stage fails to uniformly convert units for numerical fields extracted from different data sources, leading to subsequent comparisons based on inconsistent units.
- Phenomenon: The knowledge base search node cannot retrieve specific inclusion/exclusion criteria mentioned in clinical trial protocols, leading to missed screenings. Reason: During knowledge base construction, PDF protocol documents are not effectively extracted and segmented, or the segmentation strategy is too coarse, causing critical information to be split or lost.
How to Verify Configuration
- Select representative clinical trial protocols and patient medical record data. Simulate workflow execution. Check if the final pre-screening conclusions align with human judgment and verify the accuracy of key inclusion/exclusion criteria identification.
- At intermediate workflow nodes, such as after "Text Content Extraction" and "Entity Recognition," sample the output results. Confirm that medical entities (e.g., diseases, drugs, examination indicators) and their attributes (e.g., values, units) are correctly identified and structured.
- For different data sources, validate the data ingestion and standardization stages. Ensure that field names, data types, and units from patient recruitment databases, EHRs, and other systems are unified and conform to predefined specifications before entering the core processing flow.
- Track workflow execution logs. Check the time taken by each node, especially knowledge base search and model inference nodes. Ensure they complete within acceptable timeframes and do not frequently experience timeouts or errors.
Note: The values provided are common starting points. Measure performance 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.