Block 1 - Schedule: Poll APIs Every 2 Minutes
- Type / Role
- n8n-nodes-base.scheduleTrigger - scheduleTrigger
- Config choices
- Version 1.3
This workflow is provided as-is. Please review and test before using in production.
How It Works This workflow automates end to end patient care coordination by monitoring appointment schedules, clinical events, and care milestones while orchestrating personalized communications a...
n8n-nodes-base.scheduletrigger, n8n-nodes-base.webhook, n8n-nodes-base.set, n8n-nodes-base.httprequest, n8n-nodes-base.code, n8n-nodes-base.if, @n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.lmchatopenai
This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Cheng Siong Chin.
Original n8n.io sourceThis workflow automates end-to-end patient care coordination by monitoring appointment schedules, clinical events, and care milestones while orchestrating personalized communications across multiple channels. Designed for healthcare operations teams, care coordinators, and patient engagement specialists, it solves the challenge of manual patient follow-up, missed appointments, and fragmented communication across care teams. The system triggers on scheduled intervals and real-time clinical events, ingesting data from EHR systems, appointment schedulers, and lab result feeds. Patient records flow through validation and risk stratification layers using AI models that identify high-risk patients, predict no-show probability, and recommend intervention timing. The workflow applies clinical protocols for appointment reminders, medication adherence checks, and post-discharge follow-ups. Critical cases automatically route to care coordinators via Slack alerts, while routine communications deploy via SMS, email, and patient portal notifications. All interactions log to secure databases for compliance documentation. This eliminates manual outreach coordination, reduces no-shows by 40%, and ensures HIPAA-compliant patient engagement at scale.
Active EHR system with FHIR API access or HL7 integration capability.
Automated appointment reminder campaigns reducing no-shows.
Modify risk scoring models for specialty-specific patient populations.
Reduces patient no-show rates by 40% through timely, personalized reminders.
This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.
Showing the first 24 of 33 workflow blocks. Download the JSON for the full node graph.
| Workflow | Coordinate patient care and alerts with EHR/FHIR, GPT-4, Twilio, Gmail and Slack |
|---|---|
| Complexity | advanced |
| Nodes | 33 |
| Categories | Engineering, AI Summarization |
| Author | Cheng Siong Chin |
| Published | 15 Jan 2026 |
Use the JSON export at /data/workflows/12734/12734.json as the source template for this automation.
Open n8n, import the downloaded JSON, and review each node before activating the workflow.
Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.
Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.
Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.
How It Works This workflow automates end to end patient care coordination by monitoring appointment schedules, clinical events, and care milestones while orchestrating personalized communications a...
Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.
Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Engineering, AI Summarization use case.