How AI Analysis Revealed that 70% of Calls Were Critical Emergencies and Created a Roadmap to Reduce Repeat Contacts by 50% | Real Case Study
In-depth analysis identified that high-risk volume is much higher than average for medical call centers. Invisible bottlenecks: inconsistent triage without automated classification, lack of real-time ambulance tracking, and administrative errors generating repeated calls.
Main Contact Reasons
We analyzed 100 interactions to map exactly where the critical load is
Concentration by Emergency Type
Analysis revealed an exceptionally high load of real emergencies:
| Contact Reason | Percentage | Critical Observations |
|---|---|---|
| Severe clinical emergencies | 30-35% | Dyspnea, cyanosis, chest pain, stroke, hemorrhage - Real life-threatening risk |
| Moderate symptoms requiring visit | 35-40% | Abdominal pain, persistent fever, vomiting - Can escalate |
| Follow-up on previous service / complaints | 10-12% | Increases operational load - Same people 2-4 times |
| Insurance coverage errors | 8-10% | High friction - Disperses call center resources |
| Minor consultations / guidance | 5% | Could be absorbed by automated channels |
Critical Insight
Distribution by Symptom Severity
Classification according to traditional medical criteria reveals critical patterns
High Risk (32-38%)
Dyspnea, low saturation, cyanosis, vomiting blood, chest pain with cardiac history, stroke, confusion, severe hypotension, fever over 48h in children or elderly, hemorrhages.
Real life-threatening - Immediate attentionMedium Risk (45-50%)
Vomiting, persistent diarrhea, acute fever, intense abdominal pain without altered vital signs, falls in elderly, uncontrolled hypertension, incapacitating back pain.
Medium urgency - Can escalateLow Risk (12-15%)
Cough, sore throat, colds, administrative consultations, basic guidance. Cases that could be handled by telemedicine or self-service.
Low urgency - AutomatableWhy This Matters
- High pressure on medical agents
- Increased stress for patients and families
- Frequent call repetition (15% repeat contacts)
- Overload of available ambulances
Where Most Productivity Is Lost
Three critical sources of inefficiency identified by AI
Inconsistent Initial Triage
CriticalAgents ask good questions, but there is no automated classification, risk indicator, homogeneous protocol, or keyword-based prioritization.
Lack of Real-Time Ambulance Feedback
CriticalMost repeated phrases: "How long?", "Has the ambulance left?", "Where is it now?". Patients call multiple times just to know the status.
Insurance Coverage Errors
High FrequencyCauses long calls, repeated calls and high dissatisfaction. Patients in emergency must resolve administrative issues.
Variables That Best Predict Complaints
Top 5 Factors Triggering Dissatisfaction
| # | Factor | Impact |
|---|---|---|
| 1 | Delay over 2 hours | Perception of abandonment - Top predictor of complaint |
| 2 | Lack of visibility on ambulance status | Generates anxiety and repeat contacts |
| 3 | Upfront charge without clear explanation | Friction at critical moment |
| 4 | Administrative errors (insurance) | High operational friction |
| 5 | Perceived lack of physician expertise | Patient receives no diagnosis or guidance |
Top 3 Factors Predicting Repeat Contact
| # | Factor | Probability of Calling Back |
|---|---|---|
| 1 | Intense pain without changes while waiting | Very High |
| 2 | Frail elderly patients | Very High |
| 3 | Delays or lack of insurance response | High |
Operational Impact
- 30-40% average call center handling time
- Up to 25% perception of "poor service" (even when medical criteria was adequate)
- Probability of formal complaint increases significantly
Recommended Actions with Quantified Impact
Based on medical operations benchmarks
Automated AI Triage with Risk Classification
Priority 1
What It Does:
- Identifies critical keywords: cyanosis, hypotension, stroke, blood, "not breathing"
- Automatically prioritizes based on risk level
- Alerts supervisor in critical cases
- Avoids repetitive manual triage
Expected Impact:
- 20-30% reduction in decision time
- 15% fewer repeat contacts
- 10% improvement in NPS for severe emergencies
Real-Time Ambulance Tracking
Priority 1
What It Does:
- Automated notifications: "Your ambulance has been assigned", "On the way"
- Delay alerts: "Delayed by X minutes"
- Complete visibility for patient
- Reduces anxiety and need to call
Expected Impact:
- 35-50% reduction in follow-up calls
- AHT reduction
- Significant drop in delay complaints
Self-Service Channel / WhatsApp for Minor Symptoms
Priority 2
What It Does:
- Diverts low-risk cases: low fever, sore throat
- Simple telemedicine consultations
- Automated basic guidance
- Frees agents for critical cases
Expected Impact:
- Reduce 10-15% of total volume
- Free agents for high-risk cases
- Shorten response times for real emergencies
Specific Flows for Elderly and Chronic Patients
Priority 2
What It Does:
- Differentiated protocol for falls (25-30% of severe emergencies)
- Special attention to consciousness changes
- Automatic prioritization by age + symptoms
- Specific alerts for altered breathing, chest pain
Expected Impact:
- 20% improvement in risk accuracy
- Better prioritization
- Reduction in complications and complaints
Insurance Integration
Priority 3
What It Does:
- Automatic eligibility pre-check
- Unified coverage database
- Avoids unproductive calls
- Instant validation
Expected Impact:
- 8-12% less wasted time
- Less patient friction
- Greater perception of professionalism
Post-Call AI: Structured Summaries
Continuous Improvement
What It Does:
- Automated risk detection
- Structured symptom extraction
- Real-time dashboard generation
- Repeat contact prediction
Expected Impact:
- 30% less manual load on supervisors
- More accurate auditing
- Data-driven improved training
Realistic Expected Improvements
Based on AI-powered medical operations benchmarks
Projected Improvements with Full Implementation
| Metric | Projected Improvement |
|---|---|
| Customer satisfaction (NPS) | +10 to +20% |
| Repeat contact reduction | -15 to -30% |
| Prioritization speed | +20 to +30% |
| Operational efficiency | +10 to +15% |
| Follow-up calls | -35 to -50% |
| Triage decision time | -20 to -30% |
Executive Conclusion
Implementing AI in triage, risk classification, tracking and repeat contact reduction would allow realistic and measurable improvements in all critical metrics, transforming the operation from reactive to proactive and significantly reducing stress for both patients and agents.
Highest Risk Populations
Elderly and Falls (25-30%)
Represent 25-30% of observed severe emergencies. Almost always: fractures or suspicion, disorientation, reduced mobility, complex medication.
Demand specific protocolsRespiratory Emergencies (20-25%)
Between 20% and 25%. Many cases with: low saturation, pulmonary history, COPD, bronchospasms, prolonged fever.
Generate most complaints when delay > 1 hourAcute Gastrointestinal Cases (25%)
About 25% of total. Frequent in young and adult patients, but highly distressing for the patient.
High anxiety - Multiple callsAI-Powered Interaction Analysis by CYF
Transforming medical conversations into critical operational intelligence
Our Solution
We use automatic medical call transcription + specialized language models (LLMs) + hybrid AI pipelines to understand:
- Life-threatening risk keywords (dyspnea, cyanosis, stroke, hemorrhage)
- Symptom patterns and their real severity
- Probability of repeat contact based on pain and clinical history
- Administrative frictions generating dissatisfaction
- At-risk populations (elderly, chronic, respiratory)
This combination of medical analysis + AI reveals insights like "70% require prioritization" and "35-50% reduction in follow-up with tracking" — transforming emergencies into predictable and optimized operations.
