HTTP API and External Systems for Hematology-Oncology

Data Characteristics

Hematology-oncology clinical trial pre-screening data originates from hospital Electronic Medical Record (EMR) systems, Laboratory Information Management Systems (LIMS), and gene sequencing reports. This data updates frequently. During patient treatment, complete blood count, biochemical indicators, and imaging reports may update daily or weekly. Data document structures vary. EMR data is typically semi-structured text, including diagnostic records, treatment plans, and medication history. LIMS data and gene sequencing reports are usually structured tables or JSON. Fields include general demographic information and extensive hematological indicators like white blood cell count (WBC), hemoglobin (Hb), and platelet count (PLT). They also include immunohistochemistry results, cytogenetic analysis, and gene mutation information such as FLT3-ITD and NPM1 mutation status. Units strictly follow medical standards; for example, blood cell counts use x10^9/L, and gene mutation frequencies use percentages.

Constraints from "HTTP API and External Systems"

Semi-structured data and high update frequency in hematology-oncology data impose specific HTTP API design requirements. For unstructured text in EMRs, the API must support large text field transmission and allow complex Natural Language Processing (NLP) in external systems to extract key information. High update frequency requires the API to support incremental data synchronization mechanisms, such as comparing data using timestamps or versionId to avoid full synchronization performance overhead. Due to data sensitivity, HTTPS is mandatory for transmission. Implement strict authentication and authorization, such as OAuth2 token (access_token) validation. The complex structure of gene sequencing reports requires the API to handle nested JSON objects. Standardized units for hematological indicators require explicit field types and units during data transmission to prevent parsing errors. Additionally, to handle potential network fluctuations or external system processing delays, the API design should include idempotency mechanisms to ensure repeated requests do not cause data anomalies.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`HTTP_REQUEST_TIMEOUT`	`60000 milliseconds`	Allows sufficient time for external systems to process complex gene sequencing reports or large EMR texts.
`MAX_PAYLOAD_SIZE`	`10 MB`	Accommodates large file transfers like gene sequencing and imaging reports, preventing rejection due to excessive payload size.
`AUTHENTICATION_METHOD`	`OAuth2`	Ensures security and compliance for clinical data transmission, enabling fine-grained permission control.
`RETRY_COUNT`	`3`	Handles transient network fluctuations or temporary external system unavailability, improving data transmission resilience.
`DATA_ENCODING`	`UTF-8`	Prevents garbled characters for Chinese text (e.g., patient names, diagnosis descriptions) during transmission.
`CONCURRENT_REQUESTS_LIMIT`	`20`	Balances system resource usage with data synchronization efficiency, preventing system overload from excessive concurrency.

Common Pitfalls

Symptom: HTTP request returns 403 Forbidden error code, or access_token is invalid. Cause: OAuth2 token generation or refresh mechanism is incorrectly configured during external system integration, leading to expired or rejected access permissions.
Symptom: External system receives incomplete or malformed patient EMR text content. Cause: Content-Type is not correctly set to application/json; charset=UTF-8 or text/plain; charset=UTF-8, causing character encoding parsing errors or text truncation.
Symptom: Data synchronization task times out, logs show HTTP_REQUEST_TIMEOUT error. Cause: External system processing time exceeds the default API waiting time when handling high-concurrency data or performing complex gene mutation analysis.

Verification Steps

Make actual calls to the external HTTP API. Check if the returned HTTP status code is 200 OK. Verify that the responseBody contains the expected data structure and content.
Query the synchronized data in the external system. Cross-check the field values, units, and completeness of key hematological indicators (e.g., WBC, PLT) and gene mutation information (e.g., FLT3-ITD status) against the source system.
Simulate high-concurrency scenarios. Use multiple concurrent requests to call the API. Observe system resource usage and check for requests failing due to CONCURRENT_REQUESTS_LIMIT or HTTP_REQUEST_TIMEOUT.

Note: The values provided are common starting points. Measure them against specific 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.