Almost lost my first healthcare client because my automation delayed emergency procedures ๐ฅ
Friend referred me to medical practice needing prior authorization automation. 150 insurance authorization requests monthly. 2-3 hours manual processing each. Perfect first client - clear problem, measurable ROI, healthcare industry experience. Built n8n workflow. Gmail monitoring authorization request emails. Document extraction pulling patient details, insurance info, procedure codes, diagnosis codes. Coverage validation. Google Sheets tracking. Slack notifications. Clean automation with good metrics. Deployed. Working great for 4 months. Processing time dropped from 2 hours to 15 minutes. Client happy with results. Then patient complaints escalated. THE DISASTER: Three emergency authorization requests delayed 48 hours in standard approval queue. Patients needed urgent procedures (emergency surgery, critical MRI, immediate specialist consultation). Documents clearly marked "EMERGENCY" with urgent procedure dates. My system extracted emergency flags correctly at 94% confidence. Never routed differently. Sent all requests through same 24-48 hour approval path. One patient condition worsened during authorization delay. Required additional intervention. Medical group filed incident report. My automation caused delay. THE FIX: Added emergency triage layer IMMEDIATELY after document extraction. Built IF node checking: Emergency indicators: - Emergency flag = true in extracted data - Urgency level contains "urgent" or "emergency" - Procedure date within 24 hours - Request marked "STAT" or "URGENT" Emergency path (if ANY indicator true): - Auto-approve immediately - Slack alert to medical director (#emergency-auth channel) - Google Sheets row marked "EMERGENCY - APPROVED" - Processing time: Under 10 minutes Standard path (if none true): - Full validation (coverage status, documentation completeness, cost thresholds) - Peer review routing if needed - Standard 24-hour approval cycle RESULT: Tested triage against 600 historical requests. Would have caught 89% of urgent cases. Reprocessed 4 months data. Found 17 additional emergency delays. Created corrective documentation.