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Scalefresh AI

Scalefresh AI

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.

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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

  1. 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.

  1. 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.

  1. 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.

  1. 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.

  1. 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.

  1. 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.

  1. 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.

  1. 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.


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.


How We Build This

woman signing on white printer paper beside woman about to touch the documents
woman signing on white printer paper beside woman about to touch the documents

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.

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

person using macbook pro on table
person using macbook pro on table
a white vase filled with lots of wrenches
a white vase filled with lots of wrenches

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.

The Technology Behind It

The Technology Behind It

Technology Partnerships That Reduce Risk

01.

Microsoft Copilot Studio & AI Foundry

Healthcare-specific implementations with built-in HIPAA compliance and EHR integrations.

02.

LangGraph

Complex orchestration for multi-agent workflows requiring custom logic and branching.

03.

Custom MCP Servers

Specialized healthcare integrations for payer systems, fax protocols, and legacy platforms.

What Actually Changes

What Actually Changes

1

For Clinicians

Before: 2 hours of documentation for every 1 hour of patient care. Burnout from administrative burden. After: 70% less documentation time. Focus on complex cases and patient relationships. Improved job satisfaction.

1

For Clinicians

Before: 2 hours of documentation for every 1 hour of patient care. Burnout from administrative burden. After: 70% less documentation time. Focus on complex cases and patient relationships. Improved job satisfaction.

2

For Administrative Staff

Before: High-volume data entry and repetitive form completion. Chasing missing information. Manual status tracking. After: Exception handling and quality assurance. Patient support for complex situations. Oversight of autonomous processes.

2

For Administrative Staff

Before: High-volume data entry and repetitive form completion. Chasing missing information. Manual status tracking. After: Exception handling and quality assurance. Patient support for complex situations. Oversight of autonomous processes.

3

For Operations

Before: Variable processing times. Inconsistent routing logic. High denial rates. Limited visibility into bottlenecks. After: Predictable throughput. Evidence-based routing. Proactive denial prevention. Real-time performance dashboards.

3

For Operations

Before: Variable processing times. Inconsistent routing logic. High denial rates. Limited visibility into bottlenecks. After: Predictable throughput. Evidence-based routing. Proactive denial prevention. Real-time performance dashboards.

4

For The Organization

Before: Administrative costs consuming 25-30% of revenue. Physician burnout driving turnover. Patient access constrained by operational capacity. After: Measurable cost reduction in administrative operations. Improved clinician retention and satisfaction. Increased patient throughput without staff expansion.

4

For The Organization

Before: Administrative costs consuming 25-30% of revenue. Physician burnout driving turnover. Patient access constrained by operational capacity. After: Measurable cost reduction in administrative operations. Improved clinician retention and satisfaction. Increased patient throughput without staff expansion.

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

Do you work with health systems that are still early in their AI journey?

How is Scalefresh different from the large consulting firms that also offer AI services?

Do you replace our internal IT or data teams?

What does a typical engagement look like?

How do you handle AI safety and regulatory compliance in healthcare?