DH
DigitalHealthIQ
Connected clinical, engineering & quality evidence · Requirements · Risk · Verification
AI learning from 1,624 quality outcomes
ENV: DEMO · ROLE: QUALITY REVIEWER
Dashboard
TrendIQ 10
DecisionIQ 8
ActionIQ 8
EffectivenessIQ 24
OntologyIQ 12.4k
KnowledgeIQ 6
Connected Product-Quality Intelligence
Clinical intent, engineering, and quality evidence connected across the product lifecycle · Fictional example, values are illustrative
◊ Knowledge graph: 12,480 linked records
Evidence Linkage Coverage
91%
↑ 4pts this quarter
Requirements With Verification
94%
↑ 2pts this quarter
Change Impacts Mapped
87%
↑ 9pts this quarter
Assessment Prep Time
-38%
vs. manual baseline
Open Evidence Gaps
9
3 on the active change
Continuous Intelligence Loop
Signal → Decision → Execution → Outcome → Organizational Learning
● Active across all modules
TrendIQ
10 active signals
→
DecisionIQ
8 initiatives
→
ActionIQ
7 in execution
→
EffectivenessIQ
24 outcomes learned
Change-Impact Assessment Time — 12-week trend
Hours per assessment by change type
Reviewer Time Recaptured
Hours per reviewer per week, before vs. after connected knowledge
Evidence Linkage Health
Requirement-to-evidence links
Signal Mix — Last 30 Days
By owning domain
AI Confidence Distribution
Across active signals
Top Evidence & Change Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
TrendIQ — Evidence & Change Signal Detection
Signals found by following relationships across clinical, requirement, software, risk, test, and quality records
● Connected to 9 product-quality systems
Active Signals
10
↑ 3 this week
Pattern Clusters
5
2 emerging
Cross-Domain Correlations
6
3 high-strength
RCAs in Progress
3
Avg conf 86%
AI Recommendations
5
Pending review
Signals 12
Patterns 5
Correlations 7
Root Cause Analysis 4
Recommendations 9
DecisionIQ — Initiative Management
Active change-assessment and evidence initiatives created from accepted signals, with AI-suggested follow-on tasks
8 active initiatives 7 AI suggestions pending
AI Continuously Recommending
The system is learning from 1,624 historical change assessments to improve every recommendation below
Initiative owners
94% acceptance
Task owners
89% acceptance
Closure paths
82% accepted
Follow-on tasks
71% accepted
ActionIQ — Execution Coordination
Two-tier Kanban: initiatives at the top, task drill-down on click
Initiatives Kanban
AI Operational Copilot
Live workflow optimizations across the board
Automation Opportunities
Workflows AI can orchestrate end-to-end (with reviewer signoff)
EffectivenessIQ — Organizational Learning
What worked, what failed, and how the system is getting better at change-impact and evidence recommendations
● Continuous learning active
Outcomes Captured
1,624
↑ 87 this month
SME Corrections
142
→ model refinement
Initiative Success Rate
76%
↑ from 64% (Q1)
AI Recommendation Accuracy
89%
↑ 12pts vs baseline
Failed Closures Studied
11
Root-caused
Recent Learning Events
Outcomes from closed initiatives feeding back into the recommendation model
Improvement Over Time
Recommendation acceptance rate by quarter
What's Working (Reinforced)
Patterns the AI is doubling down on
What's Not Working (Down-weighted)
Patterns the AI is moving away from
OntologyIQ — Digital Health Product Knowledge Graph
A living semantic model connecting every entity and risk relationship across active Digital Health Product work
◊ 12,480 linked records · 31,260 relationships
Entity Types Modeled
9
Rationale, Requirement, Component, Version, Test…
Relationship Types
12
supported by · implemented by · verifies · specifies
Graph Freshness
94%
Entities updated within the last cycle
Query Accuracy (validated)
92%
Against SME-reviewed answer set
Ask the Knowledge Graph
Not sure where to start? Try a sample question below, or type your own — answers are traced back to the underlying graph nodes and edges.
◊ Grounded answers only
Knowledge Graph Schema
Core entity and relationship types behind every answer
Highest-Connectivity Entities
Nodes with the most cross-domain relationships — usually the highest-leverage risk points
KnowledgeIQ — Institutional & Tacit Knowledge Capture
Tracks where clinical and engineering know-how is captured, at risk, or missing entirely, and visualizes it as a knowledge graph
◊ Linked to the OntologyIQ knowledge graph
Knowledge Assets Captured
31
↑ 6 this month
Experts at Retirement/Rotation Risk
6
2 not started
Capture Coverage
68%
Of active work with a linked knowledge asset
Avg. Time-to-Capture
11d
↓ from 19d last quarter
Knowledge Graph — Experts, Assets, Records & Releases
Instance-level view of the graph: who holds the knowledge, what's been captured, and which product records and releases it explains. Click any node.
At-Risk Expertise
Specialists nearing retirement or rotation, whose knowledge of how records relate is not written down, ranked by capture status
Knowledge Asset Library
Captured interviews, process sheets, and annotated records, linked to the product record and initiative they explain
Add Custom Task
AI has pre-filled this based on the initiative's closure path
AI Pre-fill — Suggestions below are editable. Owner defaults to AI's recommendation.