Data Characteristics in this Category
Quality documentation in telemedicine primarily originates from patient Electronic Health Records (EHR), remote consultation records, imaging reports, lab results, medication lists, and healthcare professional consultation opinions and care plans. This data typically exists as unstructured documents (e.g., medical record texts, PDF reports) and semi-structured data (e.g., diagnostic codes, medication dosages). Document update frequency is high, especially when a patient's condition changes or treatment plans are adjusted. Document structure standardization varies; some follow medical industry standards (e.g., HL7, DICOM), but a large volume of free-text descriptions also exists. Key fields include patient ID, visit date, diagnosis results, treatment plans, drug names, dosage units (e.g., mg, ml), frequency (e.g., times/day), and medication administration status.
Constraints Imposed by these Characteristics on Workflow Orchestration
The mixed unstructured and semi-structured nature of telemedicine quality documentation requires workflows to have robust text parsing and information extraction capabilities during the data preprocessing stage. High update frequency means workflows need to support real-time or near real-time triggering mechanisms, such as event listeners for EHR updates. Document structural diversity poses challenges for knowledge base construction, requiring flexible segmentation strategies and metadata management to ensure accurate RAG recall. The specificity of fields and units, especially for medication dosages and time frequencies, demands that model dialogue components accurately understand and use these medical professional terms when generating content, avoiding misunderstandings that could lead to risks. Additionally, due to the sensitivity of medical data, identity authentication and data isolation mechanisms within the workflow are critical.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale for this Value |
|---|---|---|
maxContext | 8192 | Telemedicine documents are complex; a larger context window is needed to capture the full patient history. |
Chunk size (Segment Length) | 800–1200 characters (characters) | Balances semantic completeness of medical text with recall efficiency, preventing truncation of critical information. |
Recall count (Number of Recall Items) | Top 5 entries (Top 5) | Ensures RAG covers primary diagnosis, treatment, and medication records, improving relevance. |
Similarity threshold (Similarity Threshold) | 0.75 | Medical terminology is precise; a high threshold reduces interference from irrelevant or ambiguous information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Large imaging reports or complex medical record PDFs require time to parse; sufficient processing time is allocated. |
SYSTEM_TIME_VARIABLE | {{current_date_iso_8601}} | Ensures the timestamp format is uniform and accurately reflects the current system date, complying with medical record standards. |
Three Common Pitfalls
- Symptom: Model dialogue components generate reports with incorrect drug dosage units or frequency descriptions. Reason: The prompt did not explicitly instruct the model to focus on medical professional units, or the model did not fully understand the dosage information in the context.
- Symptom: During the knowledge base RAG retrieval phase, the workflow fails to recall the patient's most recent consultation records, leading to outdated information. Reason: The knowledge base update mechanism is not synchronized in real-time with the EHR system, or the document segmentation strategy does not effectively handle incremental updates.
- Symptom: During workflow execution, shared links display an "access denied" error. Reason: The identity authentication module was not enabled or correctly configured in the workflow sharing settings, preventing anonymous or unauthorized users from accessing it.
How to Verify Correct Configuration
- Select multiple typical telemedicine quality documents, run them through the workflow, and check if the diagnostic suggestions and medication plans output by the model dialogue component are accurate, paying special attention to critical medical information such as dosage, units, and frequency.
- Simulate patient record updates, trigger workflow execution, and verify if the documents recalled by the knowledge base RAG include the latest consultation records. Check if the relevance threshold of the recall results is reasonable.
- Test different permission levels for users accessing workflow shared links. Confirm that the identity authentication mechanism functions correctly, unauthorized users cannot access, and authorized users can use it normally.
Note: The values provided are common starting points. Measure against specific samples to determine optimal values for a particular use case.
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.