Data Characteristics
Market access pharmacovigilance involves diverse data sources. These include policy documents from national healthcare authorities (drug catalogs, negotiation details, payment standards) and internal corporate data (drug registration files, clinical trial reports, real-world study data). Policy documents are typically PDFs or Word files. Their update frequency is irregular, with quarterly or annual adjustments, and occasional ad-hoc releases for urgent events. Internal corporate data is often structured, such as CSV or Excel files. These files contain fields like generic drug names, brand names, indications, dosages, adverse event rates, and severity. Adverse event descriptions frequently use medical terminology, ICD codes, or MedDRA codes. Dosage units and time units (e.g., mg/kg, days) require strict standardization. Document lengths are often substantial; a single policy document can exceed 100 pages, and clinical trial reports are even longer.
Constraints on Workflow Orchestration
The unstructured nature of market access policy documents requires the workflow to include document parsing and information extraction. This identifies key drug information, adverse event clauses, and payment restrictions from large text volumes. Uncertain update frequencies mean the workflow needs to support manual triggers or event-based automatic triggers (e.g., new policy releases) to ensure information timeliness. Large data volumes and complex formats demand robust file preprocessing and segmentation capabilities within the workflow to prevent model context overflow. The presence of medical terminology and codes requires the workflow to standardize terminology and perform entity recognition. This maps non-standard descriptions to a unified coding system, improving subsequent analysis accuracy. The strictness of drug dosage and time units means the workflow must validate or convert units after data extraction to prevent misjudgments due to unit inconsistencies.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 32000 token | Accommodates lengthy healthcare policy documents and clinical reports, ensuring most document content can be processed. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances information completeness with model processing efficiency, avoiding overly large or small segments. |
Recall count (Recall Count) | Top 10 entries (top 10 items) | Ensures enough relevant document snippets are recalled to cover potential key information. |
Similarity threshold (Similarity Threshold) | 0.75 | Filters out irrelevant document snippets, improving recall precision. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Large PDF file parsing can be time-consuming; this provides sufficient time to avoid timeout errors. |
UPLOAD_FILE_MAX_SIZE | 100 MB | Accommodates the potentially large file sizes of healthcare policy documents and clinical reports. |
Common Pitfalls
- Key fields are empty after document parsing because parsing rules for unstructured policy documents are not optimized for specific layouts.
- Workflow execution times out because insufficient time is allocated for file parsing or model inference when processing large clinical trial reports or multiple policy documents simultaneously.
- Adverse event analysis results are inaccurate because drug adverse event descriptions from different sources are not standardized into a unified medical coding system within the workflow.
Validation Steps
- Upload a typical healthcare market access policy PDF file. Check that key fields like generic drug names, indications, and payment scopes are complete and accurate in the parsed results.
- Submit a clinical trial report containing various adverse event terms. Verify that the workflow correctly identifies and standardizes these terms.
- Execute a multi-step workflow. Check that the output of each intermediate step meets expectations, for example, whether document segmentation is reasonable, information extraction is precise, and the final analysis report is generated.
Note: The values provided are common starting points. Measure performance against your own samples to determine optimal settings.
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.