In Nepal's remote regions, community health workers face life-or-death decisions when ChestGuru flags high-risk chest X-rays—interpreting severity without medical training and manually determining referral urgency. This leads to dangerous delays: 28% of critical cases take >48 hours to reach specialists (source: Nepal Health Ministry 2023 audit), while unnecessary hospital transfers consume 37% of monthly fuel budgets (source: Jhapa District CHW survey, n=42). Second Eye closes this gap by automating referral triage with clinically actionable outputs.
Business case: 9,200 monthly screenings (source: ChestGuru Nepal deployment logs) × 32% high-risk flag rate (source: internal model metrics) × $84 saved per avoided unnecessary transfer (source: Regional Cost Benchmarks—transport + lost productivity) = $297,000/year recoverable value. If adoption reaches 40% of users: $118,800/year. This excludes downstream value from prevented complications ($1,200/avoided TB hospitalization per WHO Nepal estimates).
Second Eye is an urgency-tiered referral generator with printable slips and patient-facing explanations. It is not a diagnostic tool, treatment planner, or replacement for clinician judgment—ChestGuru’s screening output remains the sole medical input.
Primary Metrics
| Metric | Baseline | Target (D90) | Kill Threshold | Method |
|---|---|---|---|---|
| Avg. referral time | 18.7 min | ≤4.5 min | >9 min | Screen recordings |
| Appropriate urgency | 68% | ≥92% | <75% | MD audit (10% cases) |
| Patient comprehension | 41% | ≥85% | <60% | Post-visit survey |
Guardrail Metrics
| Guardrail | Threshold | Action |
|---|---|---|
| Override rate | ≤25% | Pause and retrain if breached |
| False "immediate" rate | ≤1.5% | Disable tiering until root cause |
What We Are NOT Measuring
Competitive Landscape
| Capability | OpenMRS Referrals | Dimagi CommCare | Second Eye |
|---|---|---|---|
| Auto-urgency classification | ❌ Manual entry | ❌ Rule-based only | ✅ (CNN severity encoder) |
| Location-aware facility mapping | ✅ Static list | ✅ GPS-based | ✅ Real-time bed capacity (gov API) |
| Printable patient-facing slip | ❌ | ✅ PDF export | ✅ Offline-optimized thermal print |
| WHERE WE LOSE | Wider EHR integration | Lower device requirements | ❌ vs CommCare’s 2G support |
Our wedge is urgency-tier explainability because Nepal’s 2024 Clinical Triage Act requires "justification for immediate transfers" to prevent system overload—competitors lack AI-generated plain-language rationale.
Quantified Baseline
| Metric | Measured Baseline |
|---|---|
| Avg. referral decision time | 18.7 min (n=112 observed screenings) |
| Unnecessary transfer rate | 22% (source: Kathmandu referral audit) |
| Patient comprehension of next steps | 41% (n=89 post-referral surveys) |
Value recoverable: 2,944 high-risk cases/year × 15 min saved × $0.11/min CHW rate = $72,600/year.
Core Inputs
Critical Constraints
Assumptions
| Assumption | Status |
|---|---|
| District hospitals provide bed capacity via SMS API | ⚠ Unvalidated—confirm with Nepal Health Ministry by 2024-10-15 |
| Thermal printers available at 60% of screening sites | ⚠ Unvalidated—field survey needed by 2024-11-01 |
| Severity encoder compatible with ChestGuru v3 output | ⚠ Unvalidated—engineering sign-off required by 2024-09-20 |
Phased Acceptance Criteria
Phase 1 — MVP (6 weeks)
US#1 — Urgency Tiering
US#2 — Patient Explanation
Out of Scope (Phase 1)
| Feature | Why Not Phase 1 |
|---|---|
| Dynamic rerouting for facility closures | Requires live SMS integration—defer to Phase 1.1 |
| Multi-language support | Limited to Nepali—English Phase 1.1 |
Before/After Narrative
Before: Health worker Sunita in Ilam sees ChestGuru’s "high risk" flag on a farmer’s X-ray. She spends 22 minutes comparing symptoms against handwritten notes, then calls a distant clinic—only to learn they lack ventilators. The patient walks 3 hours to another facility. Two days later, the farmer collapses from untreated pneumothorax.
After: Second Eye instantly generates an "immediate" referral slip for the nearest hospital with available ICU beds. Sunita prints it, explains "Your lung has air leaking—this paper guarantees your bed" in simple Nepali, and arranges a motorbike ambulance. The farmer receives surgery within 90 minutes.
UI Wireframes
┌──────────────────────────────────────────────────────────────┐
│ SECOND EYE REFERRAL [Print Slip] │
├──────────────────────────────────────────────────────────────┤
│ URGENCY: ⚠️ IMMEDIATE (within 2 hours) │
│
│ FACILITY: Mechi Zonal Hospital (12km)
│ 🟢 2 ICU beds available
│
│ EXPLAIN TO PATIENT:
│ "The scan shows air trapped outside your lung. This can
│ collapse the lung. Go now to the hospital named above—
│ show them this paper. They have a bed ready."
└──────────────────────────────────────────────────────────────┘
Risk Register
Over-reliance on AI urgency tiers
Probability: High | Impact: Critical
Mitigation: Require CHW confirmation of tier before slip printing (Owner: UX lead—built in Phase 1)
Facility data staleness causes transfer delays
Probability: Medium | Impact: High
Mitigation: Fallback to static facility list if API >4hrs stale (Owner: Backend—SLO: 99% uptime)
Legal non-compliance with Nepal Telemedicine Act
Probability: Low | Impact: Business-Blocking
Mitigation: Obtain Ministry of Health certification pre-launch (Owner: Compliance—deadline 2025-Q1)
Kill Criteria
3% of "immediate" referrals arrive >6hrs late in D90 logs
Pre-Mortem
"It is 6 months post-launch and Second Eye failed because:
Success looks like: CHWs report 'I trust the machine's urgency more than my own fear' during monsoon season. The clinical director notes 'Transfer mortality dropped 15% in pilot districts.' The team stops receiving panic calls about missed pneumothorax cases."
Critical Biases
Validation Protocol
Constraints
Deployment Guardrails