HealthTech and digital health companies operate in a high-stakes environment: patient data must stay secure, regulatory deadlines are non-negotiable, and clinical teams can't afford to wait for IT to build integrations.
This is exactly where n8n shines. Self-hosted, HIPAA-friendly (data never leaves your network), and capable of connecting EHRs, patient apps, lab systems, and cloud APIs — without a line of infrastructure code.
Here are 5 production-ready n8n automations for HealthTech companies, with full workflow JSON you can import today.
Why HealthTech Teams Choose n8n
| n8n (self-hosted) | Zapier | Make.com | |
|---|---|---|---|
| Patient data leaves your network | ❌ Never | ✅ Yes | ✅ Yes |
| HIPAA BAA available | ✅ Yes (your infra) | ✅ Yes (paid) | ✅ Yes (paid) |
| EHR / HL7 / FHIR custom logic | ✅ Full Code node | ❌ Limited | ❌ Limited |
| Cost at scale (1M events/mo) | ~$0 (self-hosted) | ~$1,499/mo | ~$800/mo |
| Audit trail (git-versioned JSON) | ✅ | ❌ | ❌ |
Self-hosted n8n keeps PHI inside your infrastructure. That's the argument that closes the compliance conversation.
Workflow 1: Patient Appointment Reminder & No-Show Prevention
The problem: Patients forget appointments. No-shows cost clinics $150–$300 per slot and disrupt care continuity.
The solution: Multi-touch reminder sequence — email at 48 hours, SMS at 2 hours.
{
"name": "Patient Appointment Reminder",
"nodes": [
{
"name": "Schedule — Every Hour",
"type": "n8n-nodes-base.scheduleTrigger",
"parameters": { "rule": { "interval": [{ "field": "hours", "hoursInterval": 1 }] } }
},
{
"name": "Fetch Upcoming Appointments",
"type": "n8n-nodes-base.googleSheets",
"parameters": { "operation": "getAll", "sheetId": "YOUR_SHEET_ID", "range": "Appointments!A:F" }
},
{
"name": "Filter 48h and 2h Windows",
"type": "n8n-nodes-base.code",
"parameters": {
"jsCode": "const now = Date.now();\nconst h48 = 48*3600*1000;\nconst h2 = 2*3600*1000;\nconst win = 10*60*1000;\nreturn items.filter(i => {\n const appt = new Date(i.json.appointment_time).getTime();\n const diff = appt - now;\n return (diff > h48-win && diff < h48+win) || (diff > h2-win && diff < h2+win);\n}).map(i => ({ json: { ...i.json, reminder_type: Math.abs(new Date(i.json.appointment_time).getTime()-now-h48) < win ? '48h' : '2h' } }));"
}
},
{
"name": "Send Reminder Email",
"type": "n8n-nodes-base.gmail",
"parameters": {
"operation": "send",
"toList": "={{ $json.patient_email }}",
"subject": "Appointment Reminder — {{ $json.reminder_type === '48h' ? 'Tomorrow' : 'Today in 2 Hours' }}",
"message": "Hi {{ $json.patient_name }},\n\nReminder: appointment with {{ $json.provider_name }} on {{ $json.appointment_time }}.\n\nNeed to reschedule? Reply to this email."
}
}
]
}
Impact: 25–40% reduction in no-shows. Each prevented no-show = $150–$300 recovered.
Workflow 2: Remote Patient Monitoring Alert Pipeline
The problem: RPM data (BP, glucose, heart rate) comes in continuously. Manual dashboard review doesn't scale.
The solution: Automated alert routing with severity classification and escalation.
{
"name": "RPM Alert Pipeline",
"nodes": [
{
"name": "Webhook — Receive RPM Data",
"type": "n8n-nodes-base.webhook",
"parameters": { "path": "rpm-readings", "responseMode": "onReceived" }
},
{
"name": "Classify Alert Severity",
"type": "n8n-nodes-base.code",
"parameters": {
"jsCode": "const r = $input.first().json;\nconst alerts = [];\nif (r.systolic > 180 || r.diastolic > 110) alerts.push({ metric: 'BP', value: r.systolic+'/'+r.diastolic, level: 'CRITICAL' });\nelse if (r.systolic > 160 || r.diastolic > 100) alerts.push({ metric: 'BP', value: r.systolic+'/'+r.diastolic, level: 'HIGH' });\nif (r.glucose) {\n if (r.glucose < 54 || r.glucose > 400) alerts.push({ metric: 'Glucose', value: r.glucose, level: 'CRITICAL' });\n else if (r.glucose < 70 || r.glucose > 250) alerts.push({ metric: 'Glucose', value: r.glucose, level: 'HIGH' });\n}\nif (r.heart_rate && (r.heart_rate < 40 || r.heart_rate > 130)) alerts.push({ metric: 'HR', value: r.heart_rate, level: 'CRITICAL' });\nreturn alerts.map(a => ({ json: { ...r, ...a } }));"
}
},
{
"name": "Route by Severity",
"type": "n8n-nodes-base.switch",
"parameters": { "rules": { "rules": [
{ "conditions": { "string": [{ "value1": "={{ $json.level }}", "value2": "CRITICAL", "operation": "equal" }] } },
{ "conditions": { "string": [{ "value1": "={{ $json.level }}", "value2": "HIGH", "operation": "equal" }] } }
] } }
},
{
"name": "Slack — CRITICAL (On-Call MD)",
"type": "n8n-nodes-base.slack",
"parameters": { "channel": "#rpm-critical", "text": "🚨 CRITICAL: {{ $json.patient_name }} — {{ $json.metric }} = {{ $json.value }}. CALL IMMEDIATELY." }
},
{
"name": "Slack — HIGH (Care Team)",
"type": "n8n-nodes-base.slack",
"parameters": { "channel": "#rpm-monitoring", "text": "⚠️ HIGH: {{ $json.patient_name }} — {{ $json.metric }} = {{ $json.value }}. Review required." }
},
{
"name": "Log to Sheets",
"type": "n8n-nodes-base.googleSheets",
"parameters": { "operation": "append", "sheetId": "YOUR_SHEET_ID", "range": "RPM_Alerts!A:F" }
}
]
}
Impact: Sub-30-second alert delivery vs 2–4 hour manual dashboard review cycles.
Workflow 3: Clinical Trial Milestone & Compliance Tracker
The problem: Missed IRB renewals or regulatory deadlines = FDA findings, site closures, trial delays.
The solution: Daily compliance check with tiered urgency alerts to PIs and clinical ops.
{
"name": "Clinical Trial Milestone Tracker",
"nodes": [
{
"name": "Schedule — Daily 7 AM",
"type": "n8n-nodes-base.scheduleTrigger",
"parameters": { "rule": { "interval": [{ "field": "cronExpression", "expression": "0 7 * * *" }] } }
},
{
"name": "Fetch Milestones",
"type": "n8n-nodes-base.googleSheets",
"parameters": { "operation": "getAll", "sheetId": "YOUR_SHEET_ID", "range": "Milestones!A:G" }
},
{
"name": "Classify Urgency",
"type": "n8n-nodes-base.code",
"parameters": {
"jsCode": "const now = Date.now();\nreturn items.filter(i => i.json.status !== 'Complete').map(i => {\n const days = Math.ceil((new Date(i.json.due_date).getTime() - now) / 86400000);\n const urgency = days < 0 ? 'OVERDUE' : days <= 3 ? 'CRITICAL' : days <= 7 ? 'URGENT' : days <= 14 ? 'WARNING' : days <= 30 ? 'NOTICE' : null;\n return urgency ? { json: { ...i.json, days_until_due: days, urgency } } : null;\n}).filter(Boolean);"
}
},
{
"name": "Slack — Clinical Ops",
"type": "n8n-nodes-base.slack",
"parameters": { "channel": "#clinical-ops", "text": "{{ $json.urgency }}: {{ $json.milestone_name }} — {{ $json.trial_id }} — Due {{ $json.due_date }} ({{ $json.days_until_due }} days)" }
},
{
"name": "Email PI/Regulatory Lead",
"type": "n8n-nodes-base.gmail",
"parameters": {
"operation": "send",
"toList": "={{ $json.owner_email }}",
"subject": "[{{ $json.urgency }}] Trial Milestone Due: {{ $json.milestone_name }}",
"message": "Milestone: {{ $json.milestone_name }}\nTrial: {{ $json.trial_id }}\nDue: {{ $json.due_date }}\nStatus: {{ $json.urgency }}\n\nPlease update the milestone tracker."
}
}
]
}
Impact: Zero missed IRB renewals or FDA regulatory deadlines. Eliminates $50k+ audit risk per missed submission.
Workflow 4: EHR to Analytics Warehouse (FHIR API Pipeline)
The problem: Getting data from EHRs (Epic, Athenahealth, Cerner) into analytics tools requires expensive integrations or fragile cron scripts.
The solution: n8n FHIR API pipeline — pulls updated patient records daily, transforms to warehouse schema, upserts to Postgres.
{
"name": "EHR FHIR to Analytics Pipeline",
"nodes": [
{
"name": "Schedule — Daily 5 AM",
"type": "n8n-nodes-base.scheduleTrigger",
"parameters": { "rule": { "interval": [{ "field": "cronExpression", "expression": "0 5 * * *" }] } }
},
{
"name": "Fetch FHIR Patient Data",
"type": "n8n-nodes-base.httpRequest",
"parameters": {
"method": "GET",
"url": "={{ $env.FHIR_BASE_URL }}/Patient?_count=100&_lastUpdated=gt{{ $today.minus({days:1}).toISO().slice(0,10) }}",
"authentication": "genericCredentialType",
"genericAuthType": "httpHeaderAuth"
}
},
{
"name": "Transform FHIR to Warehouse Schema",
"type": "n8n-nodes-base.code",
"parameters": {
"jsCode": "const bundle = $input.first().json;\nreturn (bundle.entry || []).map(e => {\n const p = e.resource; const n = p.name?.[0]; const a = p.address?.[0];\n return { json: { patient_id: p.id, given_name: n?.given?.join(' ') || '', family_name: n?.family || '', birth_date: p.birthDate, gender: p.gender, city: a?.city, state: a?.state, updated_at: p.meta?.lastUpdated } };\n});"
}
},
{
"name": "Upsert to Postgres",
"type": "n8n-nodes-base.postgres",
"parameters": {
"operation": "executeQuery",
"query": "INSERT INTO patients (patient_id, given_name, family_name, birth_date, gender, city, state, updated_at) VALUES ($1,$2,$3,$4,$5,$6,$7,$8) ON CONFLICT (patient_id) DO UPDATE SET given_name=EXCLUDED.given_name, updated_at=EXCLUDED.updated_at"
}
},
{
"name": "Slack Pipeline Status",
"type": "n8n-nodes-base.slack",
"parameters": { "channel": "#data-pipelines", "text": "✅ FHIR Pipeline complete — {{ $items().length }} records processed" }
}
]
}
Impact: Replaces $15k–$50k/year HL7 integration middleware. PHI stays inside your network — no Zapier/Make cloud egress.
Workflow 5: Patient Satisfaction Survey & NPS Tracker
The problem: Manual outreach for post-visit feedback is inconsistent. Survey response rates are low. NPS data doesn't reach leadership.
The solution: Automated post-visit survey dispatch, 24h delay, follow-up for non-responders, weekly NPS digest to leadership.
{
"name": "Patient Satisfaction Survey & NPS Tracker",
"nodes": [
{
"name": "Trigger — Visit Completed",
"type": "n8n-nodes-base.googleSheetsTrigger",
"parameters": { "sheetId": "YOUR_SHEET_ID", "range": "Visits!A:E", "event": "rowAdded" }
},
{
"name": "Wait 24 Hours",
"type": "n8n-nodes-base.wait",
"parameters": { "amount": 24, "unit": "hours" }
},
{
"name": "Send Survey Email",
"type": "n8n-nodes-base.gmail",
"parameters": {
"operation": "send",
"toList": "={{ $json.patient_email }}",
"subject": "How was your visit with {{ $json.provider_name }}?",
"message": "Hi {{ $json.patient_name }},\n\nWe'd love your feedback on yesterday's visit. 2 minutes:\n{{ $env.SURVEY_LINK }}?visit={{ $json.visit_id }}\n\nThank you!"
}
},
{
"name": "Wait 5 Days",
"type": "n8n-nodes-base.wait",
"parameters": { "amount": 5, "unit": "days" }
},
{
"name": "Check Survey Completed",
"type": "n8n-nodes-base.googleSheets",
"parameters": { "operation": "getAll", "sheetId": "YOUR_SHEET_ID", "range": "Survey_Responses!A:C" }
},
{
"name": "IF Not Responded",
"type": "n8n-nodes-base.if",
"parameters": { "conditions": { "number": [{ "value1": "={{ $items().length }}", "value2": 0, "operation": "equal" }] } }
},
{
"name": "Follow-Up Reminder",
"type": "n8n-nodes-base.gmail",
"parameters": {
"operation": "send",
"toList": "={{ $json.patient_email }}",
"subject": "Quick reminder — your feedback matters",
"message": "Hi {{ $json.patient_name }},\n\nOne more chance — it's 2 minutes and shapes how we improve care:\n{{ $env.SURVEY_LINK }}?visit={{ $json.visit_id }}\n\nThank you!"
}
}
]
}
Impact: Survey response rates increase 30–60% vs single email. NPS data feeds quality programs that affect accreditation and payer contract terms.
ROI at a Glance
| Automation | Time Saved | Revenue/Cost Impact |
|---|---|---|
| Appointment Reminders | 2h/day admin calls | $200–$500/day recovered no-show slots |
| RPM Alert Pipeline | 4h/day dashboard monitoring | Earlier intervention = fewer hospitalizations |
| Trial Milestone Tracker | 5h/week coordinator time | Eliminates $50k+ regulatory finding risk |
| EHR Data Pipeline | 10h/week data engineering | Replaces $15k–$50k/year integration middleware |
| Patient Satisfaction | 3h/week outreach | Better NPS → better payer contract terms |
Total: 15–25 hours/week saved per clinical operations team.
Ready-to-Use Templates
We've packaged the most commonly needed n8n automations into ready-to-import templates at stripeai.gumroad.com:
- AI Customer Support Bot ($29) — route patient inquiries with AI triage
- Daily Report Generator ($19) — morning briefing for clinical leadership
- Appointment Reminder ($15) — the exact workflow above, fully configured
- Lead Capture to CRM ($19) — intake form → care coordination pipeline
- Complete Template Bundle ($97) — all 15 templates
Each template is a ready-to-import .json file with a setup guide. Import in 2 clicks, configure credentials, done.
Built with n8n + the FlowKit template library. Questions? Drop them in the comments.
Top comments (0)