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Intelligent Device Reliability for Connected Medical Systems

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🧭 Root-Cause Traceability

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Discovery of: Sortest path from a recent event to its Supplier Lot (auto-picks a recent event)

❌ Real-World Problem

When failures occur, engineering teams spend weeks:
  • hunting logs
  • isolating possible faulty parts
  • tracing supplier records
  • testing firmware rollbacks
Root cause remains fuzzy → broad & expensive recalls

✅ Solution with Graph

Automatically trace the exact shortest path from any event to:
  • The component involved
  • The supplier lot that part came from
  • The firmware that drives that behavior
  • The clinic where it failed
“14 failures this week all point to Supplier's' Lot 1 and Lot 3
used only in V-Beta devices running Firmware 1.3.”

Immediate business value:

  • ✅ Micro-recalls instead of fleet-wide recalls
  • ✅ Supplier accountability & charge-backs
  • ✅ Engineering time cut by 70–90%

🌟 Graph advantage:

  • ✔ Ad-hoc pathfinding across unknown depth
  • ✔ Root-cause analytics on irregular manufacturing relationships
  • ✔ Inspection “follows the data”

👁️ Device Context Visualization

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Detection of: Community detection over Patints-Devices-Sites, summarization

❌ Real-World Problem

Troubleshooting happens in silos:
  • Engineering looks at firmware
  • Ops blames the environment
  • Support blames patient misuse
  • No unified picture = slow response + finger-pointing

✅ Solution with Graph

One click surfaces all contributing context:
  • Who is wearing the device
  • What firmware is installed
  • Which components and supplier batches are in play
  • Where it is deployed
  • What environmental conditions exist
  • What failures are clustered around it
“Device Dev-030 fails only at clinics with humidity > 75%
and using BLEChip from Lot-2 or Strap from Lot-3”

Immediate business value:

  • 🔎 Universal observability = faster RCA
  • 🤝 Aligns Engineering + Ops + Product
  • 💡 Drives targeted product improvements

🌟 Graph advantage:

  • ✔ Edge-first visual exploration
  • ✔ Root cause emerges naturally
  • ✔ Debugging becomes 2 clicks, not 20 queries

⚠️ Failure Cluster Detection

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Detection of: BLE dropout clusters by site for Firmware 1.3 (last 14 days)

❌ Real-World Problem

Devices in the field are black boxes. When a spike in failures begins:
  • No one knows where it’s centered
  • No one knows what change triggered it (firmware? network? environment?)
  • Alerts come only after enough damage occurs
  • Failures seem random until they become catastrophic

✅ Solution with Graph

A continuous graph query identifies clusters of failure events (e.g., BLE_DROPOUT) tied to specific:
  • Firmware versions (v1.3 regression)
  • Sites (Clinic 4 humidity interference)
  • Device models (V-Beta power draw issues)
Instead of “we have a problem,” we get
“Firmware 1.3 is failing in Clinic 4 — fix that area first.”

Immediate business value:

  • 🛑 Reduce patient safety exposure
  • ⚡ Faster detection → less downtime
  • 💸 Avoid big recalls — fix where the failures are

🌟 Graph advantage:

  • ✔ Natural traversal across any added dimension
  • ✔ Query stays simple even as the model evolves

👥 Adherence-Risk Communities

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Cluster of: Adherence-risk cohort: low average session time+recent Sensor fails.

❌ Real-World Problem

Wearables only help if people wear them. But adherence drops when devices:
  • ⚠ hurt / are uncomfortable
  • ⚠ disconnect intermittently
  • ⚠ show battery/wear issues
Today, patient usage issues have no visible trigger

✅ Solution with Graph

By correlating:
  • Patient usage (Session.durationMin)
  • Device failures (Event.type = SENSOR_FAIL)
  • Their connections to shared devices, firmware, components
We uncover behaviorally-linked cohorts:
“This group’s adherence collapsed after the strap revision
installed on devices from Supplier Lot SL-103.”

Immediate business value:

  • 🎯 Targeted interventions (not mass emails)
  • 📈 Improved clinical success & customer satisfaction
  • 🧩 Evidence for product design improvements (comfort, battery, fit)

🌟 Graph advantage:

  • ✔ Community detection (even simplified cohorts) is relationship-first analysis
  • ✔ Easy to pivot to any factor — environment, firmware, component lots
  • ✔ Real root causes emerge from connectivity, not aggregates