Fall Detection for Principal Care Management — Inside Epic
Fall Detection data flows directly into Epic through CCN Health — powering your PCM program with real-time insights and automated billing.
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CCN Health provides a certified Principal Care Management (PCM) integration with Epic, featuring fall detection technology. The platform automates clinical documentation, enables real-time monitoring, and generates Medicare billing records for compliant reimbursement.
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Fall Detection for Principal Care Management with Epic
CCN Health's integration brings fall detection capabilities directly into your Epic PCM workflow. Sensor-based fall detection uses radar presence sensing combined with movement pattern analysis to identify falls in real-time, even when no one witnesses the event.
How Fall Detection Works
Sensor-based fall detection uses radar presence sensing combined with movement pattern analysis to identify falls in real-time, even when no one witnesses the event.
Events and Data Captured
- Fall events
- Room presence/absence
- Activity patterns
- Bed exit timing
- Nighttime movement frequency
Key Advantages
- Detects falls even when no staff member is present
- Reduces time-to-response for fall events
- No wearable pendant that residents forget or refuse to use
- Combines with vital sign data for comprehensive assessment
- Nighttime monitoring without disturbing sleep
Fall Detection vs. Traditional Monitoring
| Factor | Sensor-Based Detection | Traditional Methods |
|---|---|---|
| Detection Method | Automatic via radar/presence | Pendant press or staff witness |
| False Alarms | Low — pattern-based detection | High with motion sensors |
| Resident Compliance | No action needed | Must wear and press pendant |
| Night Coverage | Continuous without disruption | Rounds-based, gaps between checks |
| Data Integration | Linked to vital signs and EHR | Standalone incident report |
Ideal Use Cases
- Memory care residents at high fall risk
- Post-hospital residents during recovery
- Nighttime monitoring in any care setting
- Independent living for early intervention
Fall Detection in PCM Programs
When integrated with CCN Health's PCM program through Epic, fall detection data flows automatically into the EHR and contributes to:
- Clinical Documentation — Fall events and response records documented for care plan updates
- Billing Compliance — Data transmissions count toward 99425 requirements
- Alert Workflows — Abnormal readings trigger notifications to care staff via Epic
- Cross-Program Use — Fall Detection data available across RPM, CCM, and other programs
Billing & Reimbursement
Fall Detection data contributes to PCM billing requirements:
| CPT Code | Reimbursement | Requirements |
|---|---|---|
| 99424 | ~$70/mo | 30+ minutes of clinical staff time per month |
| 99425 | ~$56/mo | Each additional 30 minutes of clinical time |
| 99426 | ~$80/mo | 30+ minutes of physician/QHP time |
| 99427 | ~$64/mo | Each additional 30 minutes of physician time |
Monthly potential per patient: $70+
Frequently Asked Questions
How does fall detection data flow to Epic?
Fall Detection data is captured automatically and transmitted to the CCN Health platform, which syncs bi-directionally with Epic. No manual entry is required.
Does fall detection qualify for PCM billing?
Yes. Fall Detection data transmissions count toward the monitoring requirements for PCM CPT codes when combined with CCN Health's clinical oversight services.
What is the implementation process?
CCN Health handles the full setup: sensor deployment, alert configuration, Epic integration, staff training, and ongoing support.
Technology that stays in the background — so care stays in the foreground.
Why CCN Health
Why Healthcare Facilities Choose CCN Health
Purpose-built technology that fits your clinical workflows and drives measurable outcomes.
Seamless EHR Integration
How CCN Health Works Inside Epic
Your program data flows directly into Epic — no exports, no manual entry, no disruption to your clinical workflow.
Specialist Data
Condition Monitoring, Referrals
AI-Powered Platform
Intelligent data routing, anomaly detection, and automated documentation — working behind the scenes.
Epic
Charts & Care Plans
What Flows Between Systems
Patient Demographics
High-risk conditions, specialist data, and medications
Condition Tracking
Disease-specific metrics monitored and trended
Specialist Coordination
Referral data and specialist notes synchronized
Care Plans
Condition-specific treatment plans inform monitoring
Time Tracking
Care management minutes tracked for billing compliance
Billing Documentation
CPT 99424/99425/99426/99427 records generated automatically
“Every reading, every alert, every care plan update — available across all your programs. One integration, unlimited use cases.”
Infrastructure
Medicare Billing
Automated Medicare billing documentation — every qualifying encounter captured and coded.
Medicare Billing
Principal Care Management (PCM)
2 billing codes
First 30 minutes of clinical staff time for PCM
~$70
Monthly
Each additional 30 minutes of PCM clinical staff time
~$54
Monthly (additional)
Common Questions
Frequently Asked Questions
Everything you need to know about implementation, billing, and clinical workflows.
Yes. CCN Health integrates with Epic via secure APIs, enabling RPM device data and care management documentation to flow into Epic's enterprise health records.
CCN Health transmits device readings and care coordination notes to Epic through FHIR-based APIs, ensuring clinical data is available within existing Epic workflows.
PCM focuses on patients with a single high-risk chronic condition, billed under CPT codes 99424 and 99425, covering care coordination and management services.
Fall detection devices use accelerometers and AI algorithms to identify fall events and immediately alert clinical staff, reducing response times and improving outcomes.
Most facilities are fully operational within 2–4 weeks. CCN Health handles device provisioning, EHR integration setup, staff training, and ongoing clinical support.
Still have questions? We're happy to walk you through anything.
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