HTTP Interface and External Systems for Rehabilitation

Data Characteristics in this Category

Data for rehabilitation device clinical trial pre-screening primarily comes from device sensors, Electronic Health Record (EHR) systems, and Clinical Trial Management Systems (CTMS). Sensor data is typically time-series, such as gait parameters (stride length, cadence, stance phase duration) from gait analyzers, or activity levels and heart rates from smart wearables. EHR systems provide structured and unstructured text data, including patient medical history, medication, and diagnostic results. CTMS contains trial protocols, screening criteria, and recruitment progress. Data update frequencies vary: sensor data can update every second, EHR data updates are relatively slower, and CTMS data updates periodically as trials progress. Document structures are diverse, including structured data in JSON, CSV, and XML formats, and unstructured data like PDFs and DICOM images. Fields and units generally follow medical standards; for example, stride length is in centimeters (cm) and heart rate is in beats per minute (bpm).

Constraints Imposed by these Characteristics on "HTTP Interface and External Systems"

The high update frequency and diverse formats of rehabilitation device data demand real-time capabilities and robust data parsing from HTTP interfaces. For instance, gait analysis data may require hundreds of interface calls per second to capture subtle changes in patient movement, necessitating high concurrency handling. Unstructured text data from EHR systems, such as physician diagnostic records, requires Natural Language Processing (NLP) techniques for information extraction and conversion into structured data for pre-screening decisions, increasing interface complexity. Furthermore, differing units and field naming conventions across data sources require standardization mapping at the interface level to ensure data consistency and prevent screening errors due to unit mismatches. Interface failure handling mechanisms must account for unstable data sources or network latency; for example, partial sensor data loss should not interrupt the entire pre-screening process. When calling external systems, data transmission encryption and compliance are essential, especially for sensitive patient privacy information.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`HTTP_TIMEOUT_SECONDS`	`30 seconds`	Balances real-time transmission of high-frequency sensor data with network instability, preventing task accumulation from excessive waiting.
`MAX_RETRIES_ON_FAILURE`	`3 times`	Provides a retry mechanism for occasional external system failures, reducing data loss due to transient network fluctuations.
`DATA_PARSING_SCHEMA`	`JSON Schema Strict Validation`	Ensures received sensor and EHR data formats conform to expectations, reducing pre-screening anomalies caused by data format errors.
`BATCH_SIZE_FOR_SENSOR_DATA`	`500 Records`	Optimizes high-frequency sensor data transmission efficiency, reduces overhead of single HTTP requests, and increases throughput.
`DATA_ENCRYPTION_STANDARD`	`TLS 1.2`	Guarantees the security and compliance requirements for sensitive patient data during transmission.
`ERROR_NOTIFICATION_CHANNEL`	`Slack or Email`	Ensures developers receive timely alerts for interface call failures, enabling quick response and resolution.

Common Pitfalls

Symptom: 401 Unauthorized error code occurs during external system calls. Reason: API Key or authentication token is expired or incorrectly configured, preventing successful security authentication with the external system.
Symptom: Patient data fields obtained from an external system are empty or type-mismatched, preventing correct execution of pre-screening rules. Reason: The data structure returned by the external system interface is inconsistent with the expected DATA_PARSING_SCHEMA, or data mapping configuration is incorrect.
Symptom: Workflow execution times out or data processing is delayed during high-frequency sensor data transmission. Reason: HTTP_TIMEOUT_SECONDS is set too short, or BATCH_SIZE_FOR_SENSOR_DATA is too large, causing the data volume processed in a single request to exceed the interface's capacity.

Verification of Configuration

Perform independent functional tests for each external system interface to verify correct data return.
Simulate high-concurrency scenarios using actual rehabilitation device sensor data. Observe workflow execution time and data processing latency to ensure stable operation under peak load.
Check the logging system. Confirm no HTTP_TIMEOUT_SECONDS timeout errors occur for interface calls, and the retry mechanism configured by MAX_RETRIES_ON_FAILURE functions correctly.
Randomly sample a batch of pre-screened patient data. Manually compare key fields to verify the accuracy of DATA_PARSING_SCHEMA and data unit conversions.

The values provided are common starting points. Measure them against your own samples for optimal results.

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.