MODULE ID: SVC-05

Sleep & Fatigue Screening Wing

STATUS: ACTIVE
CIRCADIAN MAPPING

Not a diagnosis. Does not replace clinicians. Escalates on red flags.

0h
3h
6h
9h
12h
15h
18h
21h
00:0012:0024:00
DEEP
REM
LIGHT
AWAKE
01/14MODULE: PROBLEM SPACE

The Problem

Sleep studies ordered late or unnecessarily.

→Polysomnography ordered without screening
→Delayed diagnosis due to wait times
→Low-yield sleep lab tests
IMPACT METRICS
30-40%
Reduction in sleep lab tests
$1.5M
Savings per 10K patients
02/14MODULE: CLINICAL SCENARIOS

Sleep Presentations

Chronic Fatigue
CASE 1
Chronic Fatigue
Duration
>3 months
Symptoms
Daytime sleepiness, poor concentration
Typical
Immediate sleep study referral
Home monitoring first, defer sleep lab
Low-Moderate
Snoring Concerns
CASE 2
Snoring Concerns
Duration
Long-term
Symptoms
Loud snoring, partner complaints
Typical
Sleep study ordered
Audio analysis, risk stratification
Low
Insomnia
CASE 3
Insomnia
Duration
Variable
Symptoms
Difficulty falling/staying asleep
Typical
Sleep study considered
Questionnaire + monitoring, defer study
Low
03/14MODULE: INPUTS

Input Sources

Overnight Audio
01

Overnight Audio

Snoring patterns, apnea events

Motion Proxies
02

Motion Proxies

Sleep-wake cycles, restlessness

Questionnaires
03

Questionnaires

Epworth scale, sleep quality

04/14MODULE: PROCESSING

Circadian Signal Processing

1
Capture
2
Filter
3
Analyze
4
Classify
5
Output
AUDIO ANALYSIS
  • • Snoring intensity measurement
  • • Apnea event detection
  • • Frequency domain analysis
  • • Pattern recognition
MOTION ANALYSIS
  • • Sleep-wake cycle detection
  • • Restlessness quantification
  • • Circadian rhythm analysis
  • • Sleep efficiency calculation
05/14MODULE: OUTPUT

Engine Output

APNOEA RISK BAND
Risk LevelModerate
AHI Estimate
8-12
Events per hour
Confidence
72%
RECOMMENDATION
Home Monitoring vs Sleep Lab

Based on risk assessment, home monitoring recommended first. Sleep lab if symptoms worsen.

Next Steps
  • • Continue home monitoring
  • • Reassess in 4-6 weeks
  • • Escalate if AHI increases
06/14MODULE: IMPACT

Test Minimization Impact

Reduction Metrics

Sleep Lab Tests-35%
Polysomnography-42%
Unnecessary Referrals-38%

Cost Savings

Per Patient
$150
Average reduction
Per 10K Patients
$1.5M
Annual savings
Wait Time Reduction
-45%
Faster access for high-risk
07/14MODULE: WORKFLOW

Clinical Workflow Integration

T+0

Patient Presents

Sleep concerns reported

T+1day

Overnight Monitoring

Audio + motion data collected

T+2days

Signal Processing

Analysis, pattern recognition

T+3days

Risk Assessment

Engine generates recommendation

T+4days

Clinician Review

Decision: home monitoring or sleep lab

T+6weeks

Follow-up

Reassess, escalate if needed

08/14MODULE: SAFETY

Safety Triggers & Escalation

IMMEDIATE ESCALATION
  • ⚠Daytime somnolence + CV risk
  • ⚠Severe AHI (>30 events/hour)
  • ⚠Oxygen desaturation <85%
  • ⚠Cardiac arrhythmias during sleep
  • ⚠High-risk comorbidities
CAUTION FLAGS
  • →Moderate AHI (15-30)
  • →Mild desaturation
  • →Multiple risk factors
  • →Occupational concerns
09/14MODULE: USE CASES

Real-World Applications

Primary Care
USE CASE 1

Primary Care

Scenario
Chronic fatigue evaluation
Traditional
Immediate sleep study referral
Live Corp
Home monitoring first, defer sleep lab
$1,200 saved, faster access
Occupational Health
USE CASE 2

Occupational Health

Scenario
Commercial driver screening
Traditional
Routine polysomnography
Live Corp
Risk stratification, selective testing
35% reduction in tests
Pediatric Care
USE CASE 3

Pediatric Care

Scenario
Child sleep concerns
Traditional
Sleep lab study
Live Corp
Home monitoring, defer if low risk
Reduce invasive testing
Telemedicine
USE CASE 4

Telemedicine

Scenario
Remote sleep assessment
Traditional
In-person sleep lab required
Live Corp
Audio + motion, home monitoring
50% avoid sleep lab visits
10/14MODULE: TECHNICAL

Technical Specifications

AUDIO PROCESSING
  • • Sample rate: 44.1kHz
  • • Frequency range: 20Hz-8kHz
  • • Apnea detection: Pattern matching
  • • Noise reduction: Auto
MOTION ANALYSIS
  • • Accelerometer data: 3-axis
  • • Sampling: 1Hz continuous
  • • Sleep-wake detection: ML-based
  • • Efficiency calculation: Auto
ML MODELS
  • • Apnea detector: v2.4.1
  • • Sleep classifier: v1.9.3
  • • Risk calculator: v3.2.1
  • • Confidence: 75% threshold
11/14MODULE: PERFORMANCE

Performance Metrics

85%
Accuracy
Validated on 10K cases
90%
Sensitivity
True positive rate
82%
Specificity
True negative rate
<3s
Processing
Average latency
12/14MODULE: INTEGRATION

Integration & Deployment

API Endpoints

POST /api/sleep/assess
Submit audio, motion, questionnaire
Returns: Risk band + recommendation
GET /api/sleep/status
Check processing status

Deployment

CLOUD
SaaS, HIPAA-compliant, auto-scaling
ON-PREMISE
Self-hosted, full data control
13/14MODULE: CASE STUDIES

Clinical Case Studies

Case 1: Chronic Fatigue
PATIENT
48-year-old, office worker
PRESENTATION
6-month fatigue, mild snoring
TRADITIONAL
Immediate sleep study referral
LIVE CORP
Home monitoring: low risk, defer sleep lab
Resolved with lifestyle changes, $1,200 saved
Case 2: Snoring Concerns
PATIENT
55-year-old, partner complaints
PRESENTATION
Loud snoring, no daytime symptoms
TRADITIONAL
Polysomnography ordered
LIVE CORP
Audio analysis: moderate risk, home monitoring
Sleep lab deferred, $800 saved
14/14MODULE: INTEGRATION

How This Service Fits the Engine

1
Inputs
2
Validation
3
Fusion
4
Output
5
Safety

This service operates within Live Corp's Test-Minimization Engine and follows the same abstain-when-uncertain and escalation rules.

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