Multiturn Conversations and Prompts for Hospital Operations

Data Characteristics

Hospital operations policy data originates from internal hospital management systems, policy compilations, operating manuals, and announcements. These documents are typically PDFs, Word files, or internal knowledge base pages. Update frequency is relatively low, usually quarterly or annually, but temporary updates occur during policy changes or emergencies. Document structure varies. Some policy documents strictly follow chapter and article divisions, while others contain extensive free-text descriptions. Fields often include department names, job responsibilities, process nodes, approval authorities, risk levels, and time requirements. Units cover time (days, hours, minutes), quantity (persons, copies), and monetary values (yuan).

Constraints on Multiturn Conversations and Prompts

The low update frequency of hospital operations policy documents means full re-indexing is not frequently required for knowledge base construction. However, the incremental update mechanism must effectively identify and process revised versions. Complex document structures require the RAG process to balance precise matching with contextual understanding during retrieval. This prevents critical policy clauses from being missed due to simple keyword matching. For example, a user asking about "operating room infection control procedures" may involve responsibilities from multiple departments and detailed operating steps. The model must integrate information from several relevant documents. The specificity of fields and units, such as "emergency triage time must not exceed 5 minutes," requires prompt design to guide the model to focus on numerical values and units and accurately cite them in responses, ensuring strict adherence to policies. In multiturn conversations, users may progressively refine questions, for example, from "outpatient registration process" to "specific requirements for expert appointment booking." The conversation system must maintain context and gradually narrow the focus.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`6`	Covers typical user follow-up questions (3-4 rounds) with some buffer.
`Chunk size` (Segment Length)	`500 characters` (characters)	Balances policy clause completeness with model processing efficiency, avoiding cutting off critical information.
`Recall count` (Number of Retrieved Items)	`8`	Ensures coverage of multiple potentially relevant policy documents, improving information comprehensiveness.
`Similarity threshold` (Similarity Threshold)	`0.75`	Balances retrieval precision and coverage, reducing interference from irrelevant content.
`Rerank result count` (Number of Reranked Items)	`3`	Focuses on the 3 most relevant policy contents, reducing the model's processing burden.
`prompt`	Calibrate based on actual testing	Must include instructions like "Please answer based on hospital operations policy documents, providing specific clauses or process steps."

Common Mistakes

Phenomenon: User asks about "emergency triage process," and the system's response includes "inpatient visiting regulations." Reason: The Similarity threshold (Similarity Threshold) is set too low, leading to the retrieval of semantically irrelevant but keyword-overlapping document segments.
Phenomenon: In a multiturn conversation, the user asks for subsequent steps for "operating room infection control," but the system starts explaining "surgical preparation procedures" from scratch. Reason: maxContext is set too low, causing the model to lose context from previous turns and fail to understand the continuity of the user's intent.
Phenomenon: User uploads the latest revised "pharmacy management policy" PDF, but the system still answers based on the old version. Reason: The document parsing module failed to correctly identify and load the latest version, or the old version was not promptly removed.

How to Confirm Proper Configuration

Select 5-10 typical hospital operations policy questions. Conduct multiturn conversation tests to verify if the model accurately understands the context and provides coherent answers.
Check if the system's answers accurately cite specific clauses, process steps, or numerical units from policy documents. For example, "According to Article X of the 'Emergency Department Management Policy,' triage time must not exceed 5 minutes."
Simulate a policy update scenario. Upload a new version of a policy document and ask relevant questions. Confirm that the system's answers have switched to the latest version.
Randomly sample user conversation logs. Evaluate the accuracy, completeness, and relevance of responses. Adjust Similarity threshold (Similarity Threshold) and Rerank result count (Number of Reranked Items) based on the evaluation results.

The values provided are common starting points. Measure them against your 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.