Workflow Orchestration for Rehabilitation Device Clinical

Data Characteristics

Rehabilitation device clinical trial pre-screening data primarily originates from Electronic Health Record (EHR) systems, rehabilitation training records, device usage logs, and patient-completed questionnaires. EHRs contain structured data like diagnoses, medical history, and medication, alongside unstructured text such as physician assessments and rehabilitation plans. Device usage logs are typically in CSV or JSON format, recording device status, parameter settings, and patient usage duration or intensity. Patient questionnaires are often in PDF or image formats, requiring OCR for conversion into processable text. Data update frequency varies by source: EHR data is often real-time, device logs synchronize daily or hourly, and questionnaire data updates as patients complete them. Common fields include patient_id, device_model, therapy_duration_minutes, motor_score_baseline, and adverse_event_type. Units involved include minutes, times/minute, Newtons, and Pascals.

Constraints Imposed by Data Characteristics on Workflow Orchestration

The diverse and heterogeneous nature of rehabilitation device data requires robust multi-format parsing capabilities during the data ingestion phase. Specifically, the semi-structured nature of device logs necessitates specialized data preprocessing to extract key fields like device_id and timestamp. The large volume of unstructured text in EHRs, such as physician diagnostic opinions or therapist observation notes, demands Natural Language Processing (NLP) capabilities within the workflow to accurately identify core information like rehabilitation_goal and contraindications. Image or PDF formats for questionnaire data require workflow integration with OCR modules to ensure accurate text conversion. Additionally, the asynchronous nature of data updates dictates that workflow designs must include incremental synchronization mechanisms to avoid reprocessing historical data and ensure pre-screening results are based on the latest information. Maintaining global variables across sessions is crucial for aggregating and analyzing patient data across multiple devices or time points.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Accounts for the combined size of images, PDF questionnaires, and device log files for a single patient, preventing upload failures.
`Chunk size` (Chunk Length)	`800 characters` (characters)	Balances semantic completeness of text with retrieval efficiency, suitable for medical record descriptions and rehabilitation plan texts.
`Recall count` (Recall Count)	`10 entries` (items)	Increases knowledge base coverage during the initial screening phase, capturing more potentially relevant information and reducing false negatives.
`Similarity threshold` (Similarity Threshold)	`0.75`	Balances recall and precision, ensuring retrieved knowledge is highly relevant to patient data and avoiding irrelevant information interference.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Accommodates the parsing time for large PDF questionnaires or complex device log files, preventing timeout failures.
`maxContext`	`16384 token`	Ensures the model can process context containing complete medical records, device data, and questionnaire content for comprehensive judgment.

Common Pitfalls

Frontend test results do not match workflow debugging results: This typically occurs when frontend request parameter formats do not align with workflow expectations, or when the frontend fails to correctly pass the session ID, leading to global variables not being applied.
Knowledge base retrieval results are missing or inaccurate: This happens due to an unreasonable file chunking strategy, causing critical information to be truncated; or because the knowledge base index has not been updated in time to include the latest uploaded rehabilitation device manuals.
Workflow is unresponsive or fails after a long time: This is often caused by the OCR module taking too long to process large image files, or external API calls (e.g., drug interaction queries) returning timeout errors.

Verification Steps

Upload simulated patient data in various formats (including PDF questionnaires, CSV device logs, EHR text). Check if all files are successfully parsed and vectorized into the knowledge base, and verify the parse_status field.
For typical cases, input basic patient information via the frontend interface. Observe if the workflow correctly retrieves relevant rehabilitation device contraindications and indications from the knowledge base, and evaluate the score field of the retrieved documents.
Set multiple Global Variable (global variables) in the workflow to simulate data flow under different session states. Verify that the value of these variables updates and persists as expected throughout the session lifecycle.

Note: The values provided are common starting points and should be measured 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.