Where is your device right now?
Whether you're navigating a regulatory submission, monitoring post-market signals, or managing a portfolio of 100K+ SKUs — start with Ground Truth for your current stage.
Device Lifecycle
Fourteen modules. Twelve stages. From concept through end of service.
Medical devices follow their own lifecycle — regulatory pathways, manufacturing validation, post-market surveillance, portfolio refresh. Different decisions. Different intelligence. Same platform.
Development
Regulatory & Manufacturing
Commercial
Late Lifecycle
Knowledge Graph
FoundationStages 0–11
The persistent, compounding intelligence layer beneath every module. Device program knowledge graph captures regulatory decision history, predicate analysis iterations, pre-submission outcomes, FDA feedback, and submission versions. For companies managing 100K–350K+ SKUs, MKG is the only system that maintains portfolio-level institutional memory.
Key Capabilities
Regulatory decision history — predicate analysis, pre-submission meeting outcomes, FDA feedback captured
Cross-program regulatory patterns — predicate relationships and precedent shared across device programs
Post-M&A portfolio mapping — hidden dependencies and synergies across acquired product lines
Manufacturing and supplier intelligence — lot-level traceability, supplier change history, quality events
Installed base intelligence — field performance, complaint trends, surgeon preference patterns
Cross-device intelligence — signals from one program informing strategy for another
Decision Artifacts
▸Institutional memory across all device lifecycle stages
▸Cross-program signal routing and regulatory pattern detection
▸Portfolio-level intelligence for M&A integration
▸Knowledge transfer packages for next-gen programs
60%
Setup reduction by Device 4
350K+
SKUs managed at portfolio scale
0
Institutional memory loss through turnovers
Proven Impact
CS-05 — Knowledge Graph captured predicate analysis history, enabling rapid regulatory pathway refinement as FDA feedback was incorporated. Cross-program intelligence informed submission strategy across device portfolio.
Synthetics & Phenotype Intelligence
DevelopmentStages 0–2
SPI models the patient populations and clinical scenarios that a device will serve — predicting failure modes, modeling biocompatibility profiles, and identifying patient subgroups where device performance varies. Failure mode prediction from MAUDE data identifies patterns before bench testing reveals them.
Key Capabilities
Patient population modeling for device clinical studies and enrollment strategy
Biocompatibility and safety profile simulation based on material characteristics and MAUDE precedent
Failure mode prediction — mining MAUDE data to identify failure patterns before bench testing
Patient phenotype stratification for clinical evidence generation
Use environment modeling — device performance across anatomy variations, activity levels, comorbidity profiles
Decision Artifacts
▸Design input validation — user needs confirmed against synthetic patient populations (Stage 1)
▸Clinical study design — population, endpoints, comparators (Stage 2)
▸Risk management — hazard identification grounded in population-level failure mode analysis (Stages 1–2)
MAUDE
Predictive failure mode identification
5,000+
Synthetic patient profiles generated
Pre-bench
Failure patterns identified early
Proven Impact
Failure mode prediction from MAUDE data identified loosening patterns in similar device categories before bench testing — the same class of signal that CS-06 detected post-market, but caught in the design phase.
Competitive & Regulatory Intelligence
DevelopmentStages 0–4
The single most critical device intelligence module. Component-level predicate mapping across 190,000+ FDA clearances — finding pathways buried in databases that manual search misses. The difference between a 7-month 510(k) and an 18-month De Novo.
Key Capabilities
Predicate strategy optimization — component-level mapping across 190,000+ FDA clearances, including split-predicate identification
510(k) vs. De Novo vs. PMA decision support — risk-adjusted pathway recommendation with cost-timeline tradeoffs
FDA reviewer pattern analysis — historical review patterns by product code and division for pathway confidence
EU MDR compliance intelligence — re-classification risk, Notified Body capacity, CER requirements, EUDAMED monitoring
SaMD classification guidance — decision support under FDA's SaMD framework analyzed against 500+ AI/ML clearances
Pre-submission strategy support — briefing documents informed by predicate analysis and reviewer patterns
Competitor clearance tracking — real-time monitoring across all relevant product codes
Decision Artifacts
▸Predicate landscape and pathway selection — the highest-impact regulatory decision (Stages 0–1)
▸Split-predicate strategy identification (Stages 1–3)
▸Pre-submission preparation and FDA alignment (Stage 3)
▸EU MDR re-classification strategy (continuous for legacy devices)
▸Competitor regulatory timeline reconstruction (Stages 0–4)
14mo
Timeline saved (CS-05: 510(k) vs. De Novo)
2wk
Split-predicate found vs. 4 months manual
190K+
FDA clearances analyzed
Proven Impact
CS-05 — Split-predicate strategy found in 2 weeks vs. 4 months manual search. Three cleared devices combined (surgical navigation + intraoperative imaging + AI-assisted guidance) for 510(k) eligibility. Cleared in 7 months vs. 18–24 month De Novo. 14 months saved. Competitor cleared 4 months later — two quarters of sole-source positioning.
Trial Design & Evidence Strategy
DevelopmentStages 2–3
Device clinical evidence strategy is fundamentally different from pharma — shorter timelines, different regulatory endpoints, bench-to-clinical correlation, and evidence requirements that vary dramatically by pathway. TDO bridges the gap between what you've tested and what the regulator needs to see.
Key Capabilities
Clinical study design aligned to regulatory pathway — endpoints, population, duration, comparators
Bench-to-clinical correlation modeling — where clinical evidence is essential vs. where bench data suffices
Performance testing strategy — IEC, ISO, ASTM standards with gap analysis relative to predicate comparison
Evidence sufficiency modeling — what's required, what exists, what gaps remain per pathway
Post-market clinical follow-up (PMCF) design — evidence-generating strategies under EU MDR, not just compliance
Decision Artifacts
▸Clinical evidence strategy for regulatory submission (Stages 2–3)
▸Performance testing plan relative to predicate devices (Stage 2)
▸PMCF plan design for EU MDR compliance (Stage 3+)
▸Evidence gap cost and timeline analysis per pathway (Stages 2–3)
8wk
Pre-submission confidence vs. 12mo commitment
510(k)
Evidence sufficiency confirmed pre-meeting
EU MDR
PMCF strategies designed, not just compliant
Proven Impact
CS-05 — Pre-submission meeting confirmed split-predicate approach, de-risking the entire strategy with 8 weeks of effort rather than 12 months of commitment to the wrong pathway.
Requirements & Evidence Traceability
Regulatory & TraceabilityStages 1–11
Device evidence traceability governed by ISO 13485, 21 CFR Part 820, ISO 14971, and IEC 62304 — fundamentally different requirements than pharma. Every design input maps to a design output, every output to a V&V test, every test to a result. Full design history file audit readiness at any point.
Key Capabilities
Design controls traceability (ISO 13485, 21 CFR Part 820) — full DHF audit readiness at any point
Risk management traceability (ISO 14971) — hazard through control to verification evidence
Software safety classification (IEC 62304) — requirements through testing for SaMD and embedded software
Substantial equivalence evidence mapping — every characteristic comparison traceable to bench data
V&V traceability — requirement through test method to test result with gap identification
PMCF evidence management — continuous evidence generation under EU MDR
Health economics evidence for VAC submissions — clinical, economic, outcomes evidence packages
Decision Artifacts
▸Design control completeness and audit readiness (Stages 1–3)
▸Submission readiness assessment (Stage 3)
▸V&V gap identification before regulatory submission (Stages 2–3)
▸Post-market evidence management and PMCF compliance (Stages 6–11)
▸Health economics evidence for market access (Stages 5–8)
100%
Design control traceability
ISO 13485
Continuous audit readiness
EU MDR
PMCF evidence management
Proven Impact
Full design controls traceability enabled submission readiness within weeks of design freeze. Zero evidence gaps identified during FDA review — every technological characteristic comparison documented and traceable.
Supply Chain Risk Intelligence
Cross-CuttingStages 2–11
Device supply chains have unique vulnerability profiles — single-source components, rare material dependencies, sterilization bottlenecks, and the direct connection between supplier changes and field performance. The capability that would have prevented the CS-06 situation proactively.
Key Capabilities
Component supplier financial health monitoring — critical for single-source components where distress creates regulatory risk
Rare material sourcing risk — titanium, cobalt-chrome, PEEK, ePTFE with geopolitical risk tracking
Sterilization capacity tracking — EtO capacity constraints and alternative sterilization qualification
Supplier change impact prediction — connecting process changes to field performance impacts
Logistics disruption modeling — shipping routes, customs patterns, inventory positioning
Second-source qualification planning — cost and timeline to qualify alternatives vs. single-source risk
Decision Artifacts
▸Supplier qualification and ongoing risk assessment (Stages 2–5)
▸Manufacturing readiness and scale-up (Stages 4–5)
▸Ongoing supply continuity and disruption response (Stages 6–11)
▸Component change impact assessment (Stages 6–11)
EtO
Sterilization capacity monitored
Single-src
Component risk mapped
Proactive
Supplier change detection
Proven Impact
Supplier change impact prediction connects upstream process changes to field performance — the capability that would have detected the CS-06 supplier coating change before it reached patients, instead of after 8 complaints.
Synthetic Audience Modeling
Commercial IntelligenceStages 4–8
The most transformative capability for device market access. Device adoption decisions involve fundamentally different stakeholders with conflicting decision criteria — surgeons, hospital administrators, VAC committees, C-suite — and SAM can model all of them. A single synthetic VAC study can prevent a $5–50M revenue delay.
Key Capabilities
Synthetic surgeon panels — 500–5,000 profiles by specialty, procedure volume, adoption propensity, platform experience
Synthetic hospital administrator panels — CFOs, VP Supply Chain, CMOs profiled by system size and GPO affiliation
Synthetic VAC committee panels — realistic role distributions simulating multi-stakeholder evaluation
Synthetic surgeon champion modeling — identify characteristics of surgeons most likely to champion adoption
Capital equipment decision modeling — C-suite justification for $1.5M–$3.5M systems including ROI and service contracts
Decision Artifacts
▸VAC submission strategy and messaging (Stage 5)
▸Surgeon adoption and champion identification (Stages 5–7)
▸Hospital-by-hospital market entry prioritization (Stage 6)
▸Capital equipment value justification (Stages 5–8)
▸Competitive positioning against installed base incumbents (Stages 6–8)
6–12mo
Surgeon research replaced in weeks
$5–50M
Revenue delay prevented per VAC study
5,000
Synthetic surgeon profiles generated
Proven Impact
Synthetic VAC committee simulation identified messaging gaps that would have caused submission failure at 3 target hospitals. Corrected positioning secured adoption in all 3 — preventing estimated $12M revenue delay.
Messaging, Positioning & Engagement
Commercial IntelligenceStages 4–8
Device messaging must address fundamentally different stakeholder audiences — each evaluating the device through a different lens. MPE develops differentiated value propositions for surgeons (clinical outcomes), administrators (total cost of ownership), and C-suite (competitive positioning).
Key Capabilities
Clinical messaging for surgeons — outcomes, efficiency, complication reduction, learning curve, procedure time
Economic messaging for administrators — total cost of ownership, DRG margin, OR throughput, service contracts
Strategic messaging for C-suite — competitive positioning, strategic alignment, patient volume impact
VAC presentation optimization — clinical, financial, and administrative arguments unified
Competitive differentiation — evidence-grounded claims against installed base incumbents
Decision Artifacts
▸Launch value proposition by stakeholder audience (Stages 4–5)
▸VAC submission messaging packages (Stages 5–6)
▸In-market competitive messaging refinement (Stages 6–8)
▸Surgeon engagement and adoption messaging (Stages 5–8)
3x
Stakeholder audiences addressed simultaneously
VAC
Multi-stakeholder submission packages
DRG
Margin analysis grounding economic claims
Proven Impact
Differentiated messaging architecture addressed surgeon, administrator, and C-suite evaluation criteria in unified VAC submissions — reducing hospital evaluation cycles and accelerating adoption decisions.
Market Access & Reimbursement
Market AccessStages 4–10
Device market access operates through hospital-by-hospital procurement across ~6,200 US hospitals, each with its own purchasing system, VAC, and budget constraints. DRG margin analysis is the single most powerful economic argument for device adoption.
Key Capabilities
VAC committee intelligence — composition, decision criteria, evaluation timeline, prior decisions per hospital
GPO contract monitoring — tier structures, compliance pricing, preferred vendor lists across Vizient, Premier, HealthTrust
DRG/APC reimbursement alignment — identifying where hospitals make or lose money with your device
Hospital budget cycle timing — capital equipment budgets, allocation decisions, replacement schedules
IDN purchasing policy tracking — standardization decisions affecting 10–100 hospitals simultaneously
Evidence-based procurement support — budget impact models and cost-effectiveness analyses for VAC
Decision Artifacts
▸Hospital targeting and prioritization (Stages 5–6)
▸VAC submission evidence packages (Stages 5–6)
▸GPO contract strategy and negotiation (Stages 5–8)
▸Reimbursement alignment and economic justification (Stages 5–8)
▸Capital equipment budget cycle timing (Stages 5–8)
6,200
US hospitals with unique procurement
70%
Purchase decisions influenced by VAC
DRG
Margin analysis per procedure
Proven Impact
Hospital-level procurement intelligence identified optimal VAC submission timing aligned to budget cycles and contract renewal windows. DRG margin analysis demonstrated positive hospital economics, accelerating adoption across target systems.
Competitive Intelligence (Commercial)
Commercial IntelligenceStages 5–10
Hospital-level competitive intelligence across the full device commercial landscape. Real-time clearance monitoring, hospital-level tracking, GPO contract intelligence, surgeon adoption tracking, and war gaming for multi-company competitive scenarios.
Key Capabilities
Clearance and launch monitoring — real-time tracking of competitor 510(k), De Novo, PMA, CE markings
Hospital-level competitive tracking — which hospitals have approved competitors, which are in active evaluation
GPO contract intelligence — tier structures, switching economics, network coverage dynamics
Surgeon adoption tracking — competitor platform adoption, procedure volumes, institutional influence
Conference monitoring — AAOS, SRS, AUA, AORN, AAGL for competitive readouts and KOL shifts
Surgical robotics CI — platform expansion tracking, clinical evidence milestones, installed base growth
War gaming for device markets — multi-company scenarios for specialty expansion and displacement
Decision Artifacts
▸Hospital targeting and prioritization (Stages 5–6)
▸Competitive response to new clearances and market entries (Stages 6–8)
▸GPO contract strategy and renewal timing (Stages 6–10)
▸Platform defense against competitive displacement (Stages 8–10)
4mo
Sole-source positioning (CS-05)
Real-time
Competitor clearance monitoring
GPO
Contract intelligence across major networks
Proven Impact
CS-05 — Competitor cleared 4 months later. CIC intelligence enabled sole-source positioning during the gap. The CEO: "Two quarters of market lead was worth more than the entire device development cost."
Post-Market Signal Detection
Post-MarketStages 6–11
The most differentiated device capability. Continuous surveillance with trajectory analysis, manufacturing lot correlation, and supplier change detection — catching signals that quarterly review structurally misses. The difference between a $2.8M proactive corrective action and a $47M Class I recall.
Key Capabilities
Continuous MAUDE monitoring — comparative baseline across your devices and all competitor devices in same categories
MDR trend analysis with narrative NLP — detecting failure mechanisms that complaint code trending misses entirely
Complaint pattern detection with manufacturing lot correlation — mapping patterns to variants, lots, sites, surgeons
Supplier change correlation — detecting quality shifts from upstream process changes invisible to your quality system
Proactive field corrective action — every affected patient, surgeon, lot number identified within days
Signal classification with quantified benefit-risk — monitor, investigate, escalate, act with projected trajectories
PMCF intelligence (EU MDR) — continuous evidence stream, not just compliance exercise
Competitor recall monitoring — tracking signals that create competitive opportunities
Decision Artifacts
▸Signal investigation and triage (Stages 6–11) — continuous
▸Field safety corrective action decision (Stages 6–11) — when signals escalate
▸Supplier qualification and ongoing monitoring (Stages 6–11)
▸Design refresh informed by field performance data (Stages 8–9)
▸PMCF compliance and evidence generation (Stages 6–11)
4mo
Earlier signal detection vs. quarterly review
$2.8M
Proactive action vs. $47M recall
800
Additional implantations prevented (CS-06)
Proven Impact
CS-06 — 140,000 orthopedic implants. Eight complaints with accelerating trajectory. Narrative NLP detected shared loosening mechanism invisible to complaint code trending. Traced to supplier coating change. Detected at 8 complaints, 4 months before quarterly review. 800 implantations prevented. Regulatory response: "exemplary." Competitor's comparable recall — detected 18 months late — cost $47M.
Lifecycle Management & Optimization
Late LifecycleStages 7–10
Device lifecycle extension is fundamentally different from pharma — it's about technology evolution, product refresh timing, and competitive displacement defense. When to invest in next-gen vs. extending current platform. Too early wastes R&D. Too late loses market position.
Key Capabilities
Design refresh timing optimization — next-gen investment vs. current platform extension
Technology upgrade evaluation — new materials, AI/ML, connectivity, miniaturization for existing platforms
Product line extension strategy — expanding into adjacent specialties with competitive intelligence
Competitive obsolescence defense — monitoring competitor next-gen products threatening installed base
Platform vs. point solution strategy — platform breadth vs. specialty depth for robotics and imaging
Decision Artifacts
▸Product refresh go/no-go and timing (Stages 7–9)
▸Technology upgrade investment prioritization (Stages 7–9)
▸Competitive displacement risk assessment (Stages 8–10)
▸Product line extension evaluation (Stages 7–9)
Next-gen
Refresh timing optimized
Platform
Breadth vs. depth strategy
AI/ML
Technology integration assessed
Proven Impact
Design refresh timing analysis prevented premature next-gen investment while competitor signals indicated 18-month window before displacement risk materialized — preserving R&D capital for optimally timed platform refresh.
Obsolescence Defense
Late LifecycleStages 8–11
The device equivalent of loss of exclusivity is technology obsolescence — less predictable than patent expiry, but potentially more devastating because it erodes the installed base that generates recurring revenue. Leadless displacing transvenous, robotic displacing manual, AI-guided displacing conventional.
Key Capabilities
Competitor next-gen development tracking — patents, trials, congress presentations, hiring patterns
Technology disruption risk assessment — fundamentally new approaches threatening entire product categories
Installed base migration planning — which hospitals upgrade first, conversion timeline, service contract economics
Successor product transition strategy — next-gen launch while managing current-gen revenue and surgeon loyalty
Legacy regulatory burden management — ISO 13485/FDA QSR costs for legacy products, optimal end-of-service timing
Post-M&A technology rationalization — overlapping platforms to consolidate, maintain, or retire with revenue modeling
Decision Artifacts
▸Competitive obsolescence response timing (Stages 8–10)
▸Installed base migration planning (Stages 9–11)
▸Legacy product end-of-service decision (Stages 10–11)
▸Post-M&A technology platform rationalization (event-driven)
M&A
Platform rationalization modeled
Installed
Base migration planned
Legacy
Regulatory burden quantified
Proven Impact
Post-M&A technology rationalization identified 3 overlapping surgical platforms. Revenue impact modeling quantified the cost of each consolidation scenario, enabling evidence-based portfolio decisions that preserved surgeon loyalty during transition.
Portfolio Lifecycle Analytics
Cross-CuttingStages 0–11
PLA at device scale is a fundamentally different problem — managing hundreds of thousands of SKUs with accumulated decision debt from decades of deferred portfolio decisions. A single deferred refresh decision on a $200M product line can cost $50–100M in lost market share over 3–5 years. Aggregate decision debt across 500+ active products can exceed $1B annually.
Key Capabilities
Portfolio age distribution analysis — revenue concentration by lifecycle position
Refresh wave timing — sequencing capital allocation when multiple lines approach refresh simultaneously
Technology platform dependency mapping — revenue at risk from approaching obsolescence
Competitive vulnerability mapping — where across the portfolio competitors are introducing next-gen products
Decision debt quantification — compounding cost of deferred refresh/extend/divest decisions
Post-M&A portfolio rationalization — overlap mapping and revenue impact modeling for acquired portfolios
SKU-level lifecycle intelligence — comprehensive decision layer across 100K–350K+ SKUs
Decision Artifacts
▸Portfolio resource allocation and prioritization (continuous)
▸Refresh wave sequencing and capital planning (continuous)
▸M&A evaluation and post-acquisition rationalization (event-driven)
▸Decision debt identification and remediation prioritization (continuous)
350K+
SKUs managed simultaneously
$1B+
Annual decision debt quantified
Post-M&A
Portfolio rationalization
Proven Impact
Decision debt quantification across enterprise device portfolios revealed $1B+ in annual compounding risk from deferred decisions. SKU-level lifecycle intelligence provided the first comprehensive view for companies managing 100K+ active products.
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Illustrative Example — Representative device scenarios shown to demonstrate Behavior Labs platform capabilities.
Reference
Module Activation Across the Device Lifecycle
Which intelligence modules activate at each stage — and at what intensity.
InactiveLowMediumHighPeak