Cardiovascular Quality Documentation: HTTP Interface and

Data Characteristics

Cardiovascular disease quality documentation draws from diverse sources. Common document types include clinical trial protocols, research reports, case report forms (CRFs), drug inserts, and consensus guidelines. Data update frequency is relatively stable; new drug development cycles are long, and guidelines update every few months to several years. Document structures typically contain precise medical terminology, dosage units, diagnostic criteria, and treatment procedures. Core fields include patient ID, diagnosis codes (e.g., ICD-10), drug names, dosages (mg, µg), administration routes, frequencies, and laboratory results (mmol/L, mmHg, bpm). Documents often feature complex tables, charts, and nested structures, posing challenges for text extraction and structured processing.

Constraints from HTTP Interface and External Systems

The specialized nature and structural complexity of cardiovascular quality documentation impose specific requirements on HTTP data transmission and external system integration. Frequent medical terms and units necessitate UTF-8 encoding support in interfaces to prevent garbled text, ensuring correct parsing by external systems. The abundance of tables and charts in documents requires interfaces to handle non-plain text data, potentially needing prior OCR or PDF parsing. Given the relatively stable data update frequency, interface design can consider periodic full or incremental synchronization mechanisms to avoid resource waste. Strict field validation and unit conversion are critical for successful integration. For example, blood pressure values in mmHg and heart rates in bpm require external systems to accurately identify and validate numerical ranges.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	Cardiovascular documents (e.g., clinical trial reports) can contain numerous images and charts, leading to larger file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing complex PDF documents and image recognition requires significant time; this prevents parsing failures due to timeouts.
`maxContext`	`8000 tokens`	Cardiovascular guidelines and research reports often contain lengthy professional discussions, requiring a larger context window.
`Chunk size`	`800–1200 characters`	This ensures the completeness of medical concepts, preventing semantic loss from overly fine-grained segmentation.
`Similarity threshold`	`0.75`	Precise matching of medical terminology is crucial; a higher threshold helps retrieve more relevant specialized content.
`HTTP_REQUEST_RETRY_COUNT`	`3 times`	Network fluctuations are common during external system integration; an appropriate number of retries improves stability.

Common Pitfalls

HTTP requests return 4xx or 5xx error codes. This may be due to external system interface authentication failures or request parameters not conforming to expected formats.
Key fields (e.g., drug dosage, examination results) are empty or incorrectly formatted after document parsing. This occurs because documents contain various expressions or units that the parser does not fully cover.
Global variables are not correctly identified or applied during API access. This happens when the interface parameter passing method does not match the variable type configured in the application.

Verification Steps

Upload a cardiovascular guideline PDF containing complex tables and charts. Verify that the parsed document content is complete and key fields are correctly extracted.
Use the HTTP interface to send test data with medical terminology and special units. Verify that the external system correctly receives and processes the data.
Perform different types of queries, such as those containing ICD codes or drug names. Observe the relevance and accuracy of the retrieved results.
Check system logs to confirm that file upload, parsing, and external system call response times are within expected ranges, with no timeouts or errors.

The values provided are common starting points. Measure 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.