Stop Drowning in Administrative Work
Stop Drowning in Administrative Work
Healthcare providers spend two hours on administrative tasks for every hour of patient care. We engineer AI agents that handle routine cases autonomously, escalate complex scenarios with full context, and learn from human decisions to continuously improve.
The 2:1 Problem
The Real Problem with
Healthcare AI
For every hour a physician spends with patients, they spend two hours on administrative work.
Prior authorizations. Referral management. Clinical documentation. Insurance verification. Claims processing. This administrative burden contributes directly to clinician burnout and represents billions in operational waste across healthcare.
Simple automation helps at the margins, but breaks when scenarios deviate from scripts. Healthcare needs cognitive automation that understands context, makes decisions, and handles routine work while recognizing when human judgment is required.
That's what agentic AI delivers.
Not Chatbots. Autonomous Agents.
What AI-Ready Data Actually
Looks Like
What AI-Ready Data
Actually Looks Like
What Makes AI "Agentic"
What Makes AI "Agentic"
Agentic AI refers to autonomous systems that perceive context, make decisions, take actions, and adapt based on outcomes.
Agentic AI refers to autonomous systems that perceive context, make decisions, take actions, and adapt based on outcomes.
In healthcare, this means agents that:
In healthcare, this means agents that:
Read EHR records and extract relevant clinical information
Check insurance eligibility through real-time APIs
Generate documentation meeting coding standards
Send faxes and coordinate across multiple platforms
Route cases based on complexity and confidence levels
Read EHR records and extract relevant clinical information
Check insurance eligibility through real-time APIs
Generate documentation meeting coding standards
Send faxes and coordinate across multiple platforms
Route cases based on complexity and confidence levels
The key difference?
These agents complete entire workflows from start to finish, not just answer questions or provide suggestions.
Four Design Principles
Human-in-the-Loop by Default
80-90% of cases: AI handles autonomously from start to finish
10-20% of cases: AI routes to humans with full context and recommended actions
This isn't about replacing clinical judgment. It's about freeing humans from repetitive work so they can focus on cases requiring expertise, empathy, and nuanced decision-making.
What changes: Clinicians stop doing data entry and start doing medicine.
Tool-Using Agents
Agents work within your existing systems:
Epic, Cerner, athenahealth EHR platforms
Real-time payer eligibility APIs
Fax systems for provider communications
Documentation templates and coding engines
Multiple platforms with seamless error handling
No system replacement required. Agents connect to the infrastructure you already have.
Confidence-Based Escalation
This isn't about replacing clinical judgment. It's about freeing humans from repetitive work so they can focus on cases requiring expertise, empathy, and nuanced decision-making.
High confidence (>90%): Process autonomously
Medium confidence (70-90%): Flag for review with recommendation
Low confidence (<70%): Escalate immediately with full context
Clinical validation rules check that recommendations align with evidence-based guidelines. Intervention triggers create alerts when agents detect potential safety issues.
The system knows what it doesn't know.
Continuous Learning
Agents learn from human corrections and adapt to changing requirements.
Example: When a human overrides an agent's prior authorization recommendation, the system analyzes why. Was it a new payer policy? An edge case the agent hadn't encountered? A clinical nuance requiring judgment?
The agent updates its decision-making logic accordingly. Next time it encounters a similar scenario, it performs better.
Result: ROI in year two exceeds year one, which exceeds the pilot period.
Human-in-the-Loop by Default
80-90% of cases: AI handles autonomously from start to finish
10-20% of cases: AI routes to humans with full context and recommended actions
This isn't about replacing clinical judgment. It's about freeing humans from repetitive work so they can focus on cases requiring expertise, empathy, and nuanced decision-making.
What changes: Clinicians stop doing data entry and start doing medicine.
Tool-Using Agents
Agents work within your existing systems:
Epic, Cerner, athenahealth EHR platforms
Real-time payer eligibility APIs
Fax systems for provider communications
Documentation templates and coding engines
Multiple platforms with seamless error handling
No system replacement required. Agents connect to the infrastructure you already have.
Confidence-Based Escalation
This isn't about replacing clinical judgment. It's about freeing humans from repetitive work so they can focus on cases requiring expertise, empathy, and nuanced decision-making.
High confidence (>90%): Process autonomously
Medium confidence (70-90%): Flag for review with recommendation
Low confidence (<70%): Escalate immediately with full context
Clinical validation rules check that recommendations align with evidence-based guidelines. Intervention triggers create alerts when agents detect potential safety issues.
The system knows what it doesn't know.
Continuous Learning
Agents learn from human corrections and adapt to changing requirements.
Example: When a human overrides an agent's prior authorization recommendation, the system analyzes why. Was it a new payer policy? An edge case the agent hadn't encountered? A clinical nuance requiring judgment?
The agent updates its decision-making logic accordingly. Next time it encounters a similar scenario, it performs better.
Result: ROI in year two exceeds year one, which exceeds the pilot period.
Where This Works Today
Where This Works Today
Where This Works Today
01.
Prior Authorization End-to-End
Current state:
Staff manually gather clinical documentation, complete payer forms, track submission status, follow up on denials. Average processing time: 45-60 minutes per authorization. Denial rates: 15-20%.
With agentic AI:
Agent extracts relevant clinical data from EHR, determines payer-specific requirements, completes authorization forms, submits electronically, tracks approval status. Escalates only when clinical judgment is needed.
01.
Choose a Plan
Current state:
Staff manually gather clinical documentation, complete payer forms, track submission status, follow up on denials. Average processing time: 45-60 minutes per authorization. Denial rates: 15-20%.
With agentic AI:
Agent extracts relevant clinical data from EHR, determines payer-specific requirements, completes authorization forms, submits electronically, tracks approval status. Escalates only when clinical judgment is needed.
01.
Choose a Plan
Current state:
Staff manually gather clinical documentation, complete payer forms, track submission status, follow up on denials. Average processing time: 45-60 minutes per authorization. Denial rates: 15-20%.
With agentic AI:
Agent extracts relevant clinical data from EHR, determines payer-specific requirements, completes authorization forms, submits electronically, tracks approval status. Escalates only when clinical judgment is needed.
02.
Referral Management with Intelligent Routing
Current state:
Coordinators receive referral orders, determine appropriate specialists, verify insurance coverage, find available appointments, communicate with patients. Routing logic varies by coordinator experience.
With agentic AI:
Agent analyzes referral indication and patient context, matches to appropriate specialist based on clinical needs and insurance network, checks availability and schedules appointments, sends patient notifications automatically.
02.
Referral Management with Intelligent Routing
Current state:
Coordinators receive referral orders, determine appropriate specialists, verify insurance coverage, find available appointments, communicate with patients. Routing logic varies by coordinator experience.
With agentic AI:
Agent analyzes referral indication and patient context, matches to appropriate specialist based on clinical needs and insurance network, checks availability and schedules appointments, sends patient notifications automatically.
02.
Referral Management with Intelligent Routing
Current state:
Coordinators receive referral orders, determine appropriate specialists, verify insurance coverage, find available appointments, communicate with patients. Routing logic varies by coordinator experience.
With agentic AI:
Agent analyzes referral indication and patient context, matches to appropriate specialist based on clinical needs and insurance network, checks availability and schedules appointments, sends patient notifications automatically.
03.
Clinical Documentation from Conversations
Current state:
Physicians document encounters manually after visits. Average documentation time: 1-2 hours daily. Quality varies based on physician fatigue and time constraints.
With agentic AI:
Agent listens to patient-provider conversation, generates clinical documentation meeting coding requirements, presents draft for physician review and approval in under 2 minutes.
03.
Clinical Documentation from Conversations
Current state:
Physicians document encounters manually after visits. Average documentation time: 1-2 hours daily. Quality varies based on physician fatigue and time constraints.
With agentic AI:
Agent listens to patient-provider conversation, generates clinical documentation meeting coding requirements, presents draft for physician review and approval in under 2 minutes.
03.
Clinical Documentation from Conversations
Current state:
Physicians document encounters manually after visits. Average documentation time: 1-2 hours daily. Quality varies based on physician fatigue and time constraints.
With agentic AI:
Agent listens to patient-provider conversation, generates clinical documentation meeting coding requirements, presents draft for physician review and approval in under 2 minutes.
04.
Pre-Visit Intake Automation
Current state:
Staff call patients to collect medical history, current medications, insurance information, and reason for visit. Time-intensive. Patients frequently unavailable during business hours.
With agentic AI:
Agent sends digital intake forms with intelligent follow-up, verifies insurance eligibility automatically, flags prior authorization needs before visit day, updates EHR with complete validated information.
04.
Pre-Visit Intake Automation
Current state:
Staff call patients to collect medical history, current medications, insurance information, and reason for visit. Time-intensive. Patients frequently unavailable during business hours.
With agentic AI:
Agent sends digital intake forms with intelligent follow-up, verifies insurance eligibility automatically, flags prior authorization needs before visit day, updates EHR with complete validated information.
04.
Pre-Visit Intake Automation
Current state:
Staff call patients to collect medical history, current medications, insurance information, and reason for visit. Time-intensive. Patients frequently unavailable during business hours.
With agentic AI:
Agent sends digital intake forms with intelligent follow-up, verifies insurance eligibility automatically, flags prior authorization needs before visit day, updates EHR with complete validated information.
How We Build This
01
Discovery and Design (Weeks 1-3)
We start by understanding your reality, not imposing templates.
What happens:
Workflow analysis of current processes
Identification of automation opportunities with measurable ROI
Multi-agent system architecture modeling
Documentation of handoff protocols and escalation criteria
Success metrics defined before building anything
What you get:
Clear picture of what's automatable, what requires human judgment, and what ROI to expect.
Discovery and Design (Weeks 1-3)
We start by understanding your reality, not imposing templates.
What happens:
Workflow analysis of current processes
Identification of automation opportunities with measurable ROI
Multi-agent system architecture modeling
Documentation of handoff protocols and escalation criteria
Success metrics defined before building anything
What you get:
Clear picture of what's automatable, what requires human judgment, and what ROI to expect.
Discovery and Design (Weeks 1-3)
We start by understanding your reality, not imposing templates.
What happens:
Workflow analysis of current processes
Identification of automation opportunities with measurable ROI
Multi-agent system architecture modeling
Documentation of handoff protocols and escalation criteria
Success metrics defined before building anything
What you get:
Clear picture of what's automatable, what requires human judgment, and what ROI to expect.
Development and Integration (Weeks 4-16)
We build agents iteratively, testing against real scenarios continuously.
Core development:
Custom agent development for your specific workflows
EHR integration through FHIR APIs
Payer API connections for real-time eligibility
Clinical validation rules and safety guardrails
Human review interfaces with full context presentation
Delivered incrementally:
Working agents every 2-3 weeks, not a big-bang deployment at the end.
Development and Integration (Weeks 4-16)
We build agents iteratively, testing against real scenarios continuously.
Core development:
Custom agent development for your specific workflows
EHR integration through FHIR APIs
Payer API connections for real-time eligibility
Clinical validation rules and safety guardrails
Human review interfaces with full context presentation
Delivered incrementally:
Working agents every 2-3 weeks, not a big-bang deployment at the end.
Development and Integration (Weeks 4-16)
We build agents iteratively, testing against real scenarios continuously.
Core development:
Custom agent development for your specific workflows
EHR integration through FHIR APIs
Payer API connections for real-time eligibility
Clinical validation rules and safety guardrails
Human review interfaces with full context presentation
Delivered incrementally:
Working agents every 2-3 weeks, not a big-bang deployment at the end.
02
03
Deployment and Optimization (Weeks 17-20)
Pilots start small and expand based on proven performance.
What happens:
Pilot deployment in controlled environment
Performance monitoring and accuracy measurement
Agent fine-tuning based on real-world results
Gradual expansion to additional use cases
Knowledge transfer to your teams for ongoing maintenance
What you get:
Production-ready agents handling real workload with documented performance and maintained by your teams.
Deployment and Optimization (Weeks 17-20)
Pilots start small and expand based on proven performance.
What happens:
Pilot deployment in controlled environment
Performance monitoring and accuracy measurement
Agent fine-tuning based on real-world results
Gradual expansion to additional use cases
Knowledge transfer to your teams for ongoing maintenance
What you get:
Production-ready agents handling real workload with documented performance and maintained by your teams.
Deployment and Optimization (Weeks 17-20)
Pilots start small and expand based on proven performance.
What happens:
Pilot deployment in controlled environment
Performance monitoring and accuracy measurement
Agent fine-tuning based on real-world results
Gradual expansion to additional use cases
Knowledge transfer to your teams for ongoing maintenance
What you get:
Production-ready agents handling real workload with documented performance and maintained by your teams.
The Technology Behind It
The Technology Behind It
Technology Partnerships That Reduce Risk
What Actually Changes
What Actually Changes
When You Need This
You need agentic AI workflows if you're experiencing:
Administrative burden affecting clinician satisfaction and retention
Prior authorization delays impacting patient access and revenue
Inconsistent referral routing leading to suboptimal outcomes
Documentation quality issues affecting coding accuracy and compliance
Operational bottlenecks limiting patient throughput despite adequate clinical capacity.
You need agentic AI workflows if you're experiencing:
Administrative burden affecting clinician satisfaction and retention
Prior authorization delays impacting patient access and revenue
Inconsistent referral routing leading to suboptimal outcomes
Documentation quality issues affecting coding accuracy and compliance
Operational bottlenecks limiting patient throughput despite adequate clinical capacity.
The question isn't whether to automate. It's what to automate first.
Common Questions
Common
Questions
How much does comprehensive evaluation cost compared to the AI system itself?
Can't our AI vendor handle evaluation?
When should we start evaluation?
What if we've already deployed AI without systematic evaluation?
How do we know if our evaluation is sufficient?
How much does comprehensive evaluation cost compared to the AI system itself?
Can't our AI vendor handle evaluation?
When should we start evaluation?
What if we've already deployed AI without systematic evaluation?
How do we know if our evaluation is sufficient?
How much does comprehensive evaluation cost compared to the AI system itself?
Can't our AI vendor handle evaluation?
When should we start evaluation?
What if we've already deployed AI without systematic evaluation?
How do we know if our evaluation is sufficient?