Data Characteristics
Hospital operational policy data comes from various sources. These include internal management documents, regulations, Standard Operating Procedures (SOPs), and emergency plans. It also includes laws and regulations from national and local health authorities. Documents are typically in PDF, Word, or Excel formats.
Update frequency varies. Core policies, such as medical quality management methods or infection control guidelines, update less frequently, perhaps annually or when policies change. Specific departmental SOPs or emergency plans might have minor revisions quarterly or semi-annually, based on actual operations or new equipment.
Document structures often follow a chapter format, including an introduction, general principles, specific clauses, division of responsibilities, and supervision/assessment sections. Fields and units commonly include approval date, effective date, revision version number, and responsible department. Performance assessment may involve percentages or scores, but content is primarily text-based.
Constraints Imposed by These Characteristics on Workflow Orchestration
The diverse document formats for hospital operational policies require the knowledge base pre-processing stage to support parsing various file types, ensuring complete content extraction.
Varying policy update frequencies mean the workflow needs different knowledge base synchronization and update strategies. Core policies can use regular full or incremental updates, while frequently changing SOPs may require more granular version management and immediate update mechanisms.
The chapter-based document structure influences text splitting strategies. Overly long chapters can lead to semantic loss, so splitting by paragraph or key headings should be considered.
The predominantly text-based nature demands strong understanding and reasoning capabilities from the large language model. Especially in question-answering scenarios involving multi-department collaboration and responsibility delineation, the workflow needs additional logic to handle ambiguous or overlapping information. This might involve multi-turn conversations to clarify user intent or integrating external tools for judgment.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters | Hospital policy documents often have long paragraphs. This length helps preserve contextual semantics and prevents semantic fragmentation. |
Recall count (Recall Count) | Top 8–12 items | Policy questions often require synthesizing multiple regulations for a complete answer. Increasing recall appropriately improves accuracy. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Policy texts demand high professionalism. A high threshold reduces interference from irrelevant content and ensures precise recall. |
Rerank result count (Reranked Return Count) | Top 5 items | After reranking, taking the top few most relevant results as the final input reduces redundant information for the large language model. |
Knowledge Base Specified File Set | Divided by department or policy type | For policy queries specific to different departments, this limits the search scope, improving retrieval efficiency and relevance. |
temperature | 0.3–0.5 | Policy questions require rigorous and factually accurate answers. A lower temperature value helps reduce model creativity and maintains objective responses. |
Common Pitfalls
- Batch execution nodes fail to complete a process when called via API, but work fine during online debugging. This might be due to session state loss or resource limitations in API calls, causing internal loops to break.
- After changing AI dialogue to "variable reference," parameters like temperature cannot be set. In variable reference mode, parameter control transfers to the variable itself. Adjusting parameters requires modifying the variable's value.
- After calling a workflow via an interface, the conversation log shows empty runtime data. This might be due to uncaught exceptions during workflow execution or improperly configured data reporting mechanisms, leading to incomplete log records.
How to Verify Configuration
- Select typical policy questions from various departments. Call the workflow via API and compare the model's output with standard answers to assess if accuracy meets expectations.
- Simulate high-concurrency scenarios. Observe if batch execution nodes can stably complete tasks. Check logs for timeouts or interruption errors and verify the completion rate.
- Randomly select policy documents from the knowledge base. Test if key clauses can be accurately recalled. Verify if the recall count and similarity threshold meet the expected settings.
- Check queries for different departments or policy types. Verify if the
Knowledge Base Specified File Seteffectively limits the search scope and prevents cross-domain interference.
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.