Model Integration and Configuration for Medical Insurance Claim Pre-screening in Clinical Trials

Data Characteristics

Medical insurance claim data primarily originates from medical insurance centers and HIS/settlement systems of designated medical institutions. Update frequency is typically monthly or quarterly, with some high-frequency business data updating weekly. Core data is mainly structured and semi-structured. It includes patient basic information, diagnosis codes (e.g., ICD-10), treatment plans, drug lists (ATC classification), medical service item costs, settlement status, and reimbursement ratios. Document types cover electronic medical record summaries, expense lists, and scanned settlement vouchers. Key fields include patient_id, diagnosis_code, drug_code, service_item_code, claim_amount, and reimbursement_ratio. Monetary values are usually in "yuan," while quantities are expressed in "times," "boxes," or "milligrams."

Constraints on Model Integration and Configuration

The periodic updates of medical insurance claim data require the model training and knowledge base synchronization mechanisms to support incremental learning and regular full refreshes. This ensures pre-screening logic relies on the latest policies and directories. The extensive use of structured data and coding systems means knowledge base construction must focus on mapping between codes and natural language descriptions. For example, the association between ICD-10 codes and disease descriptions affects how the Embedding model understands semantic similarity. Extracting key information from semi-structured documents, such as the claim_amount field from expense lists, requires configuring specific preprocessing rules to handle different formats. Data sensitivity mandates considering data anonymization and access control during model integration to ensure compliance, such as anonymizing the patient_id field. The large data volume often places high demands on knowledge base chunking strategies, recall efficiency, and model inference speed.

Configuration Settings

Common Pitfalls

• Model tests return a 404 Not Found error. This occurs when the model name in OneAPI does not match the model_name configured in FastGPT, preventing correct routing.
• Knowledge base query results show low relevance. Clinical trial recommendations do not strongly correlate with patient medical insurance data. This happens when the Embedding model fails to effectively capture the semantic relationship between medical insurance codes and clinical terminology.
• Uploading large electronic medical records or settlement lists results in a request timeout. This is due to PARSE_FILE_TIMEOUT_SECONDS being set too short, preventing file parsing from completing within the allotted time.

Verification Steps

• Perform a series of queries including ICD-10 codes, generic drug names, and medical service item names. Check if recall results accurately match relevant clinical trial information. Analyze the similarity score distribution to determine if the similarity threshold is appropriate.
• Upload a typical medical insurance settlement file containing multiple settlement records and diagnostic information. Check if the knowledge base correctly parses and extracts key fields like diagnosis_code and claim_amount. Field values should match the original document.
• Use FastGPT's debugging interface to observe token consumption and response time during model inference. Ensure typical queries can be processed within the maxContext limit and that response time meets business requirements.

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.