CO
CareOpsIQ
Enterprise Reasoning Platform for Ambulatory Health Technology · EHR · RCM · Scheduling · Payer Networks
AI learning from 1,860 enterprise health-technology outcomes
ENV: PROD · ROLE: PRODUCT & RCM LEAD
Dashboard
TrendIQ 10
DecisionIQ 9
ActionIQ 9
EffectivenessIQ 19
OntologyIQ 18,460
KnowledgeIQ 6
ReasoningIQ 4
Ambulatory Health Technology Enterprise Operations
Continuous enterprise reasoning across revenue cycle, clinical workflow, customer success, and compliance signals
◊ Knowledge graph: 18,460 linked entities
Denial Prevention Rate
82%
↑ 9pts this quarter
Prior Auth Turnaround Coverage
71%
↑ from 58% last quarter
Onboarding Cycle Closure
64%
↑ 12pts this quarter
Days-in-AR Cycle Time
-14%
↓ vs last quarter
Open Revenue & Compliance Risks
6
↑ 2 this month
Continuous Intelligence Loop
Signal → Decision → Execution → Outcome → Organizational Learning
● Active across all modules
TrendIQ
10 active signals
→
DecisionIQ
9 initiatives
→
ActionIQ
8 in execution
→
EffectivenessIQ
19 outcomes learned
Days-in-AR — 12-week trend
Mid-size practice segment, weekly average
Support Engineering Hours Recaptured
By automation category, last 6 releases
Integration & Reporting Coverage Health
Coverage across onboarding integrations and regulatory reporting workflows
Signal Mix — Last 30 Days
Distribution of active signals by domain
AI Confidence Distribution
Confidence bands across all active recommendations
Top Enterprise Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
TrendIQ — Enterprise Signal Detection
Signals surfaced continuously across RCM, clinical workflow, customer success, and compliance systems
● Connected to 6 enterprise systems
Active Signals
10
↑ 3 this week
Pattern Clusters
5
2 emerging
Cross-Domain Correlations
6
3 high-strength
RCAs in Progress
4
Avg conf 86%
AI Recommendations
5
Pending review
Signals 12
Patterns 5
Correlations 7
Root Cause Analysis 4
Recommendations 9
DecisionIQ — Initiative Management
Active enterprise initiatives created from accepted signals, with AI-suggested follow-on tasks
9 active initiatives 6 AI suggestions pending
AI Continuously Recommending
The system is learning from 1,860 historical enterprise outcomes 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 RCM/ops lead signoff)
EffectivenessIQ — Organizational Learning
What worked, what failed, and how the system is getting better at enterprise reasoning recommendations
● Continuous learning active
Outcomes Captured
19
↑ 4 this month
SME Corrections
7
→ model refinement
Initiative Success Rate
84%
↑ from 76% (Q1)
AI Recommendation Accuracy
88%
↑ 6pts vs baseline
Failed Closures Studied
5
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 — enterprise health-technology Knowledge Graph
A living semantic model connecting every entity and risk relationship across active enterprise health-technology work
◊ 18,460 entities · 41,200 relationships
Entity Types Modeled
8
Patients, Providers, Encounters, Appointments, Claims, Payers, Diagnoses, Policies
Relationship Types
12
governs · requires · generates · correlates-with · exposes · owned-by…
Graph Freshness
96%
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 RCM and clinical operations 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
↑ 7 this month
Experts at Retirement/Rotation Risk
6
2 uncaptured
Capture Coverage
68%
Of active work with a linked knowledge asset
Avg. Time-to-Capture
11d
↓ from 19d last quarter
Knowledge Graph — Experts, Assets, Programs & Sites
Instance-level view of the graph: who holds the knowledge, what's been captured, and which programs and sites it feeds. Click any node.
At-Risk Expertise
Operators and specialists nearing retirement or rotation, ranked by capture status
Knowledge Asset Library
Captured video, process sheets, and annotated drawings, linked to the program and initiative they came from
ReasoningIQ — Evidence-Backed enterprise health-technology Recommendations
Executives need recommendations, not just answers — the reasoning engine evaluates multiple enterprise health-technology factors together to produce a single evidence-backed recommendation
◊ Reasoning over the OntologyIQ knowledge graph
Reasoning Chains Run
214
↑ this week
Avg. Factors Evaluated
5.0
per recommendation
Avg. Recommendation Confidence
87%
Grounded in graph evidence
Executive Recommendations Delivered
12
This month
From Systems of Record to Systems That Reason
No existing system is replaced — each becomes more valuable feeding the reasoning engine
EHR · RCM Billing · Scheduling · Payer Clearinghouse · Support Ticketing · Data Lake
6 systems connected
→
Enterprise Ontology
8 entity types
→
Knowledge Graph
18,460 entities
→
Reasoning Engine
214 chains run
→
Business Users
Executive decisions
Reasoning Scenarios
Each scenario evaluates multiple factors across the enterprise before producing a recommendation — click a scenario to see the full evidence chain
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.