Multi-Turn Conversations and Prompts for Clinical Trial Pre-screening in Medical Insurance Claims

Data Characteristics

Medical insurance claims data originates from Hospital Information Systems (HIS) and medical insurance settlement platforms. It typically appears as electronic medical records, settlement statements, and expense details. Data updates frequently, on a daily, weekly, or monthly basis. Document structures are primarily semi-structured or structured, such as XML expense lists, PDF medical record cover pages, or spreadsheets. Key fields include patient basic information (age, gender, diagnosis), visit information (admission date, discharge date), expense information (item code, item name, unit price, quantity, total amount, medical insurance payment ratio, out-of-pocket amount), and disease diagnosis codes (ICD-10). Amounts are usually precise to the cent. Drug dosages use various units, such as milligrams, tablets, or milliliters.

Constraints Imposed by These Characteristics on Multi-Turn Conversations and Prompts

The semi-structured nature of medical insurance claims data requires multi-turn dialogue systems to accurately match specific field values when parsing user intent. High update frequency necessitates continuous synchronization of knowledge base content to ensure retrieval results are timely. Documents contain numerous professional terms and codes. This demands high precision and professionalism in prompt engineering to avoid semantic ambiguity. The complexity of expense details, especially the calculation of medical insurance payment ratios and out-of-pocket amounts, means multi-turn conversations may involve complex logical judgments and numerical calculations. The presence of sensitive patient information requires strict adherence to data security and privacy protection principles in prompt design, preventing sensitive information leakage.

Configuration Settings

Three Common Mistakes

• During multi-turn conversations, the model provides an incorrect amount when the user asks about medical insurance payment ratio calculations. This occurs because the prompt does not explicitly instruct the model to perform numerical calculations, or the relevant calculation rules in the knowledge base are incomplete.
• The model returns empty or irrelevant content when a user asks about the medical insurance reimbursement scope for a specific disease. This happens because the association between disease diagnosis codes (ICD-10) and medical insurance policy terms in the knowledge base is insufficient, leading to retrieval failure.
• During a conversation, the system frequently asks the user to repeat information such as patient age or diagnosis. This is due to an excessively small maxContext parameter, which prevents the model from effectively remembering key entities in multi-turn dialogues, leading to context management failure.

How to Confirm Correct Configuration

• Select typical medical insurance claim scenarios and simulate multi-turn conversations. Verify the model's understanding of core information such as patient diagnosis and expense details.
• For questions involving complex medical insurance reimbursement rules, check if the model accurately cites policy terms from the knowledge base and provides compliant explanations.
• Test dialogue scenarios containing sensitive information. Confirm that the model, under prompt constraints, does not directly repeat or generate patient private data that should not be disclosed.

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.