Vector Model and Indexing for Clinical Trial Pre-screening

Data Characteristics

Medical insurance claims data originates from Hospital Information Systems (HIS) and medical insurance settlement platforms. This data typically exists as structured or semi-structured electronic medical records, settlement statements, and expense details. Data updates frequently, often daily or weekly. Key fields include patient visit dates, diagnosis codes (e.g., ICD-10), treatment item codes, drug codes, expense amounts, and reimbursement ratios. Settlement statements often present in tabular form, listing multiple expense items with corresponding codes and amounts. Medical record home pages or discharge summaries may contain unstructured text descriptions, such as chief complaints, history of present illness, and treatment processes. Data units are primarily in RMB (Yuan). Coding systems adhere to national medical insurance catalogs and clinical treatment guidelines.

Constraints on Vector Models and Indexing

The structured nature of medical insurance claims data requires vector models to effectively differentiate semantic meanings of various fields during encoding. This prevents the loss of critical coding information due to simple text chunking. High update frequency necessitates efficient incremental update mechanisms for the index, ensuring accuracy and timeliness in clinical trial pre-screening. For example, new settlement records must quickly reflect in the index. The mix of structured codes and unstructured text within documents challenges vector model selection; models must handle multimodal information or effectively encode different information types. Numerical information like expense amounts and reimbursement ratios require special handling during vectorization to avoid semantic ambiguity from direct text encoding. This may involve numerical embeddings or combining with categorical encoding. Patient privacy and data security are core constraints, demanding strict anonymization during data preprocessing and index construction.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk Length`	`500–800 characters`	Balances the item density of settlement statements with the contextual coherence of medical record text, preventing excessive truncation of critical information.
`Chunk Overlap`	`50 characters`	Ensures contextual continuity at chunk boundaries, improving recall and reducing semantic fragmentation.
`Vector Model`	`text-embedding-v3-small` or `bge-large-zh-v1.5`	Considers Chinese semantic understanding, encoding efficiency, and cost, handling both structured codes and unstructured text.
`Index Model`	`bge-large-zh-v1.5`	Ensures encoding consistency with the vector model, improving recall quality.
`Recall Count`	`10–20 items`	Given the complexity and interconnectedness of medical insurance claims data, increasing recall count covers potential matches.
`Similarity Threshold`	Calibrate by testing	Determine through actual testing based on pre-screening precision and recall requirements. An initial value of `0.75` is a common starting point.

Common Mistakes

New medical insurance claims data is not retrievable after an index update. This occurs when incremental indexing tasks are incorrectly configured or fail, preventing timely index refresh.
Clinical trial pre-screening results frequently include irrelevant settlement records. This may be due to a Similarity Threshold set too low, or the vector model failing to effectively differentiate subtle semantic nuances.
When pre-screening patients for specific disease diagnoses, relevant medical record information is often not recalled. This can happen if the Chunk Length is too small, truncating critical diagnostic descriptions and preventing the formation of complete semantic vectors.

Verification Steps

Select representative medical insurance settlement statements and medical record texts. Manually construct queries and check if recall results include expected associations.
Review index model logs within the FastGPT platform for errors or warnings, particularly concerning data import and vectorization.
Randomly sample indexed documents from the FastGPT knowledge base management interface. Examine chunking and vectorization previews to confirm critical fields and codes are processed correctly.
Conduct multiple rounds of pre-screening tests for specific clinical trial conditions. Analyze the precision and recall of recalled records, compare against expected results, and adjust the Similarity Threshold accordingly.

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.