Data Characteristics
Patient Assistance Program (PAP) data originates from pharmaceutical companies, charitable foundations, hospital pharmacies, and patient self-submissions. Update frequencies vary. Pharmaceutical companies and foundations typically update patient enrollment and medication status monthly or quarterly. Hospital pharmacy data may update daily. Patient self-submissions are real-time or irregular. Document structures are diverse, including standardized Electronic Health Record (EHR) snippets, unstructured medical reports, medication records, and financial status proofs. Fields include patient basic information (e.g., patient_id, diagnosis_code), disease diagnosis (e.g., ICD-10 codes), medication history (e.g., drug_name, dosage, frequency), laboratory test results (e.g., lab_test_name, value, unit), and patient financial status (e.g., income_level, insurance_status). Units for drug dosage are often milligrams (mg) or grams (g), while laboratory indicators have specific units like mmol/L or ng/mL.
Constraints Imposed by These Characteristics on Workflow Orchestration
The heterogeneous nature of patient assistance data requires workflows to integrate multiple data ingestion methods. It also requires pre-processing for cleaning and standardization. For example, unstructured medical records need text parsing nodes to extract key information and ensure consistent diagnosis_code formats. Varying update frequencies demand flexible workflow triggers, supporting both real-time data streams and periodic batch processing. Diverse document structures, especially large volumes of unstructured text, place high demands on information extraction and entity recognition capabilities within the workflow. This requires configuring specialized AI nodes to process free-text medical records. Specific fields and units, such as inconsistent dosage units or lab_test_value range checks, require data validation nodes in the workflow to ensure accurate numerical comparisons and prevent misjudgments due to unit mismatches. Strict patient privacy protection requirements mandate that workflow design includes data anonymization and access control to prevent sensitive information leakage during processing.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 3000 characters | Balances medical record text length and model processing efficiency. Prevents context overflow. |
Chunk size (Segment Length) | 500–800 characters | Accommodates varying section lengths in medical records. Improves recall accuracy. |
Similarity threshold (Similarity Threshold) | 0.75 | Increases matching strictness. Reduces false positives, especially for disease feature matching. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Addresses parsing time for large or complex PDF medical records. Prevents timeout failures. |
Recall count (Number of Retrieved Items) | Top 8 entries (Top 8) | Ensures coverage of multi-dimensional medical information. Provides sufficient evidence for pre-screening. |
Rerank result count (Number of Re-ranked Items) | Top 3 entries (Top 3) | Focuses on the most relevant key information. Reduces noise for model processing. |
Common Pitfalls
- An AI conversation node cannot provide image content when processing image-based medical records. This occurs when the multimodal model does not correctly identify the input as an image type, or the API key's model version does not support image recognition.
- Tool call results are still output to the screen after the tool call ends in the workflow. This happens when the tool call end node is not correctly configured to suppress output, or subsequent connected nodes default to passing upstream results.
- The frontend experiences a 404 error and the document parsing node cannot access file content after deployment. This usually results from incorrect server-side static resource path configuration, preventing the parsing service from accessing uploaded files.
Validation Steps
- Upload structured JSON data containing typical diagnostic information and medication records. Check if the workflow correctly parses all key fields and performs initial screening according to predefined logic.
- Upload an unstructured PDF medical record with complex descriptive conditions. Verify if text parsing and entity recognition nodes accurately extract disease diagnoses, key laboratory indicators, and their units.
- Run the workflow with simulated patient data that meets enrollment criteria. Confirm the final output is "meets preliminary enrollment conditions" and lists key evidence snippets supporting this judgment.
- Run the workflow with simulated patient data that does not meet enrollment criteria (e.g., age out of range or diagnosis mismatch). Confirm the final output is "does not meet enrollment conditions" and specifies the reasons for non-compliance.
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.