Workflow Orchestration for Quality Documentation in Mental

Data Characteristics in This Category

Quality documentation in mental health originates from diverse sources. These include clinical trial reports, pharmacovigilance data, patient follow-up records, updated diagnostic standards, and relevant policy documents. Data update frequencies vary. Clinical trial data may release in stages, while pharmacovigilance data accumulates continuously. Document structures are often unstructured or semi-structured, such as PDF clinical research reports, Word SOP files, and more structured Excel spreadsheets. Documents frequently contain specialized terminology, disease classification codes (e.g., ICD-10/11), drug dosage units (mg, μg), efficacy evaluation metrics (PANSS, HAM-D), and time units (weeks, months). Data characteristics include high specialization, multi-modality, and stringent requirements for timeliness and accuracy.

Constraints Imposed by These Characteristics on Workflow Orchestration

The specialized and multi-modal nature of mental health quality documents demands robust text recognition and information extraction capabilities during the document parsing stage. Unstructured documents require OCR technology for text conversion and structured information extraction. High-frequency data sources, such as pharmacovigilance reports, necessitate workflow support for scheduled triggers and incremental updates to ensure knowledge base timeliness. Specific codes and units within documents, like ICD codes and various scale scores, require standardization or mapping during data preprocessing to guarantee accurate retrieval and inference. Due to data sensitivity, workflows must integrate strict data anonymization and access control mechanisms. The high demands for timeliness and accuracy make error handling and rollback mechanisms crucial within the workflow to address data parsing failures or knowledge update anomalies.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size`	`500–800 characters`	Mental health documents have strong contextual links; balances retrieval efficiency and semantic integrity.
`Overlap Length`	`80–120 characters`	Ensures context at segment boundaries is not lost, improving recall quality.
`Recall count`	`8–12 entries`	Complex queries may involve multiple aspects of information; increasing recall improves coverage.
`Similarity threshold`	Calibrate based on actual measurements	Adjust based on the business scenario's balance between precision and recall.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Prevents parsing timeouts when processing large PDF reports or scanned documents.
`EnabledTimed Update`	`Every 24 hours`	Addresses the update frequency of pharmacovigilance data and policies, ensuring knowledge base timeliness.

Three Common Pitfalls

Symptom: Workflow execution stops, logs show "Text parsing failed, unsupported file format." Cause: A scanned image PDF was uploaded, but the OCR pre-processing component was not enabled.
Symptom: Retrieval results contain a large amount of irrelevant information or miss critical details. Cause: Chunk size is too large or too small, leading to semantic units being broken or too much noise being introduced.
Symptom: Workflow triggers but remains unresponsive for an extended period, eventually reporting "Task timeout." Cause: The file size exceeds the UPLOAD_FILE_MAX_SIZE limit, or PARSE_FILE_TIMEOUT_SECONDS was not adjusted for poor network conditions.

How to Confirm Correct Configuration

Select representative mental health documents, manually upload them, and run the parsing workflow. Check logs to ensure no abnormal errors.
Test knowledge base retrieval results for queries of varying complexity. Evaluate the semantic relevance and completeness of returned documents to determine a reasonable range for Similarity threshold.
Simulate the release of new clinical trial reports or policy updates. Observe if the scheduled update workflow triggers as expected and verify the availability of newly added knowledge.

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.