Data Characteristics
Medical insurance settlement drug vigilance data primarily originates from Hospital Information Systems (HIS), medical insurance payment systems, pharmacy management systems, and national drug adverse reaction monitoring centers. Data updates frequently. Medical insurance settlement data typically generates daily or weekly. Adverse drug reaction reports may be real-time or summarized monthly. Document structures are mainly structured and semi-structured data. This includes basic patient information, diagnoses, medication records (drug name, dosage, frequency, administration route), medical insurance payment details (cost codes, reimbursement ratios, out-of-pocket amounts), and adverse event descriptions. Key fields include generic drug name, drug batch number, occurrence time, medical insurance catalog code, reimbursement type, and patient demographics like age and gender.
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
The update frequency of medical insurance settlement data requires the knowledge base to support rapid synchronization and incremental updates. This ensures the timeliness of retrieval results. Highly structured data necessitates the knowledge base to support precise field matching and range queries, such as filtering by specific medical insurance catalog codes or reimbursement types. Semi-structured adverse event descriptions challenge the knowledge base's ability to understand unstructured text, especially in identifying drug-symptom associations. Inconsistent standardization of field names and units may require polysemy or synonym mapping during retrieval. Additionally, sensitive information involving patient privacy demands strict permission control and de-identification capabilities during retrieval and display. This prevents sensitive data leakage.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Medical insurance settlement records often contain multiple related fields. Shorter segments may break semantic continuity; longer segments introduce noise. |
Recall count | Top 5–8 entries | Ensures coverage of potentially relevant medical insurance policies, drug instructions, or adverse reaction cases. |
Similarity threshold | Calibrate by actual measurement | Requires adjustment based on specific datasets and business needs using test sets to balance recall and precision. |
Rerank result count | 3 entries | Further refines initial recall results, improving the relevance and readability of the final output to the user. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Medical insurance settlement documents can be large, requiring sufficient parsing time to avoid timeouts. |
maxContext | 4000 tokens | Ensures the capacity to hold long text content such as recalled medical insurance policy terms and drug instructions. |
Common Pitfalls
- Knowledge base retrieval results show numerous irrelevant medical insurance policy clauses. This occurs because the
Similarity thresholdis set too low, recalling documents that do not match the query intent. - When users query specific drug reimbursement details, the system returns adverse reaction information for that drug. This happens because the knowledge base does not correctly distinguish between "medical insurance policy" and "drug vigilance" query intents, leading to an unrefined retrieval strategy.
- When retrieving adverse reaction cases, results lack critical drug batch numbers or dosage information. This is due to the knowledge base not effectively extracting and indexing key fields during construction, preventing precise matching during retrieval.
Validation Steps
- Select a set of typical medical insurance settlement and drug vigilance queries. Test the relevance of knowledge base retrieval results. Ensure retrieved document content highly matches the query intent.
- Verify the timeliness of knowledge base retrieval for newly entered medical insurance policies or adverse drug reaction reports. Check if new data is indexed and recalled within the expected timeframe.
- For queries containing specific medical insurance codes, generic drug names, or adverse event keywords, check if retrieval results precisely hit relevant documents. Verify the accurate presentation of key fields (e.g.,
reimbursement ratio,drug batch number). - Simulate high-concurrency query scenarios. Evaluate the knowledge base's retrieval response time and stability. Ensure it meets actual business requirements.
The values given 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.