Multi-turn Conversations and Prompts for Hospital Operations

Data Characteristics

Registration and declaration documents for hospital operations originate from internal hospital management systems, medical record systems, financial systems, and external regulatory standards. This data typically exists as a mix of structured (e.g., database records, spreadsheets) and unstructured (e.g., Word documents, PDF reports, scanned images) formats. Update frequencies vary; for example, medical record data updates daily, financial reports update monthly or quarterly, and policy regulations change according to their release cycles. Document structures are complex, containing numerous specialized terms, abbreviations, and industry-specific norms. Field names include medical_record_number, length_of_stay, total_medical_expenses, primary_diagnosis, surgical_code. Units include yuan, days, times, cases. The data often involves International Classification of Diseases (ICD-10) and International Classification of Procedures in Medicine (ICD-9-CM-3) codes.

Constraints Imposed by Data Characteristics on Multi-turn Conversations and Prompts

The highly mixed structured and unstructured nature of hospital operations data requires multi-turn dialogue systems to have robust heterogeneous data parsing capabilities. Varying data update frequencies necessitate regular synchronization of information from different sources to ensure conversations are based on the latest, most accurate materials. Specialized terminology and coding systems in documents demand higher accuracy and generalization from prompts. The model must understand and correctly associate these professional concepts. Furthermore, registration and declaration documents are highly compliance-driven. Dialogue results must be traceable to specific sources. This requires the system to clearly identify cited documents in multi-turn conversations. Prompt design must emphasize fact-checking and source confirmation to avoid generating inaccurate or non-compliant content.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`maxContext`	`8192` tokens	Balances context length and computational cost, supporting detailed traceability for complex declaration materials.
`Chunk size` (Chunk Size)	`800–1200` characters	Adapts to the paragraph structure of medical reports and policy regulations, ensuring semantic completeness.
`Recall count` (Recall Count)	`10` items	Increases retrieval breadth, improving the probability of recalling relevant specialized terms and regulatory clauses.
`Similarity threshold` (Similarity Threshold)	`0.75`	Filters low-relevance content, enhancing retrieval result precision and reducing noise.
`Rerank result count` (Reranked Return Count)	`5` items	Selects the most relevant document snippets, optimizing model input and reducing the model's comprehension burden.
`temperature`	`0.3`	Controls model output determinism, ensuring rigor and accuracy in declaration document preparation.

Common Pitfalls

The dialogue request interface returns an empty citation ID. This occurs if knowledge base citation settings are incorrect or the knowledge base chunking strategy is too coarse. This prevents recalled text snippets from effectively linking to original documents.
Multi-turn conversations fail to remember previous questions, leading to subsequent responses losing context. This happens if the maxContext parameter is too small, limiting the model's ability to process long dialogue histories, or if prompts do not explicitly instruct the model to consider historical dialogue information.
Dialogue records do not display the model's thought process. This occurs if the agent service does not return the thought process as key fields like tool_code or tool_input, or if the frontend interface is not configured to parse and display these fields.

Verification Steps

Conduct multi-turn dialogue tests. Verify if the model accurately cites specific sections or clauses from declaration documents during conversations. Check if the cite field contains valid document IDs.
Test with questions of varying complexity. Ensure the model understands and correctly processes queries containing medical terminology and ICD codes. Evaluate the accuracy and professionalism of responses.
Simulate a declaration document review scenario. Input specific compliance questions. Verify if the model provides legally compliant answers based on the knowledge base and offers clear source traceability paths.

The values provided are common starting points. Measure them against your own samples to determine optimal configurations.

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.