Workflow Orchestration for Rational Drug Use Policies

Data Characteristics for This Category

Rational drug use policy data primarily originates from internal medical institution documents. These include regulations, Standard Operating Procedures (SOPs), drug directories, clinical pathways, expert consensuses, and relevant legal and regulatory documents. Documents are typically in PDF, Word, or scanned image formats, with varying degrees of structure. Update frequency varies: large medical institutions may revise policy documents several times a year, while drug directories or national regulations update quarterly or annually. Document content often contains extensive medical terminology, generic drug names, dosage units (e.g., mg, IU, ml/h), indications, contraindications, and adverse reactions. Non-textual information like flowcharts and tables may also be included.

Constraints Imposed by These Characteristics on Workflow Orchestration

The update frequency of rational drug use policy documents requires the knowledge base's indexing or incremental update mechanisms to support automation and high frequency. Complex medical terminology and dosage units demand high-precision entity recognition in the text processing module to ensure accurate information extraction. Large amounts of unstructured or semi-structured text, along with image information like flowcharts, challenge document parsing capabilities, potentially requiring OCR technology. Furthermore, hierarchical relationships and cross-references between policies necessitate multi-source retrieval and context correlation mechanisms within the workflow. This avoids one-sidedness from a single document and ensures comprehensive and accurate answers.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`Chunk size` (Chunk Size)	`800–1200 characters`	Rational drug use policy texts often have long paragraphs with multiple medical concepts. Longer chunks help maintain contextual completeness.
`Recall count` (Retrieval Count)	`5–8 items`	Policy questions require covering multiple relevant clauses. Increasing the retrieval count improves coverage.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Precise matching of medical terminology is crucial. A higher threshold filters out irrelevant retrieval results.
`Rerank result count` (Rerank Return Count)	`3 items`	This filters for the most relevant few pieces of information, reducing the model's processing burden and improving response speed.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing large PDFs or scanned images can be time-consuming. This allows sufficient parsing time.
`MAX_FILE_SIZE_MB`	`100 MB`	This accommodates policy documents with many charts or high-resolution scans, ensuring successful file uploads.

Three Common Pitfalls

Incorrect drug names or dosage units appear in answers. This occurs because the document parsing failed to correctly identify specific medical entities, leading to inaccurate knowledge base indexing.
The system is unresponsive for an extended period or returns a 504 Gateway Timeout error after a user query. This happens when file parsing or vector embedding processing of large files exceeds the PARSE_FILE_TIMEOUT_SECONDS setting.
A query about one policy returns results from unrelated policy documents. This indicates the Similarity threshold (Similarity Threshold) is set too low, causing generalized retrieval results.

How to Confirm Proper Configuration

Upload ten rational drug use policy documents with varying complexity. Check the file parsing status and the number of knowledge base chunks to ensure they meet expectations.
For the uploaded policy documents, design queries containing common drug names, dosages, and indications. Validate the accuracy and relevance of the retrieved results.
Simulate high-concurrency access scenarios. Observe if the workflow's response time is within an acceptable range. Check system logs for out of memory errors.
Test with complex questions that include ambiguous semantics and multiple conditions. Evaluate the model's ability to understand context and integrate information from multiple sources. Adjust the Similarity threshold (Similarity Threshold) based on actual performance.

The values given 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.