Workflow Orchestration for Telemedicine Pharmacovigilance

Data Characteristics in This Category

Telemedicine pharmacovigilance data primarily originates from adverse event (AE) reports and medication records submitted by patients. These submissions come via online consultations, remote monitoring devices, and mobile applications. Data updates frequently, potentially in real-time or multiple times daily. Document structures typically include both structured fields and unstructured text. Structured fields cover patient demographics, medication history, adverse event types, occurrence times, and severity. These often use enumerated values or datetime formats. Unstructured text includes patient symptom descriptions and preliminary physician diagnoses. This text uses natural language, potentially containing medical abbreviations and colloquialisms. Data commonly transmits in JSON, XML, or CSV formats. Field names and units must adhere to medical industry standards, such as ICD-10 codes and SNOMED CT terminology.

Constraints Imposed by These Characteristics on "Workflow Orchestration"

High-frequency, real-time data streams in telemedicine demand low-latency processing capabilities from workflows. This ensures timely detection and response to adverse events. The presence of unstructured text necessitates integrating robust Natural Language Processing (NLP) capabilities into the workflow. This is crucial for entity recognition, sentiment analysis, and adverse event classification, directly impacting the selection and configuration of TextSplitter and Embedding nodes. Data heterogeneity (a mix of structured and unstructured data) requires workflows to flexibly perform data preprocessing and schema transformation. An example is field mapping or data cleaning within a DataTransform node. Furthermore, medical data's sensitivity and compliance requirements dictate that data transmission and storage must comply with regulations like HIPAA and GDPR. DataSecurity becomes a critical workflow component for data anonymization and access control, influencing parameter settings for HTTP requests or database operations.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale for This Value
`maxContext`	`8000`	Telemedicine adverse event descriptions are often long. Sufficient context is needed to understand semantics and avoid truncating critical information.
`Chunk size`	`500–750 characters`	Balances semantic integrity and embedding efficiency. Avoids overly long segments diluting key information and overly short segments losing context.
`Recall count`	`Top 10 entries`	Ensures enough relevant adverse event cases or drug instructions are retrieved from the knowledge base to improve accuracy.
`Similarity threshold`	`0.75`	The medical domain requires high relevance. This threshold filters out irrelevant recall results, reducing false positives.
`Rerank result count`	`5 entries`	Refines results further after initial recall through re-ranking, focusing on the most relevant items.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Provides ample file parsing time when processing large PDF drug instructions or case reports, preventing timeouts.

Three Common Mistakes

A workflow node executes but outputs nothing. This often indicates an incorrect regular expression in the DataTransform node, failing to extract the target field.
When calling an external charting tool to generate data visualizations, the chart appears empty. Typically, the JSON structure of the body parameter in the HTTP request node does not match the tool's API requirements, leading to data transfer failure.
After local deployment, the CHAT_API_KEY environment variable is not active, causing model calls to fail. This usually means the environment variable configuration in the docker-compose.yml file is not correctly mapped to the container, or the container was not rebuilt.

How to Verify Correct Configuration

Use the FastGPT debugging interface to step through the workflow. Check if each node's input and output meet expectations, especially the transformation results from the DataTransform node.
Simulate submitting a telemedicine adverse event report containing both structured and unstructured data. Observe if the workflow correctly classifies, extracts key information, and triggers subsequent notification or processing flows.
Verify whether external systems (e.g., electronic medical record systems, notification platforms) receive correct data and instructions from the workflow. Cross-reference the consistency of event_id and patient_id data fields.
Review FastGPT backend logs. Confirm no 400 Bad Request or 500 Internal Server Error messages appear, especially those related to external API calls.

The values provided are common starting points. Measure them 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.