Private AI

for US Healthcare providers

Alexey Litvin | CEO

Trusted by:

Why using
Public AI might not be safe for Healthcare providers

Patient data privacy

Public AI models can risk exposing sensitive patient data, violating HIPAA regulations and compromising trust

Lack of control over data

With public models, you lose oversight of how sensitive data is stored and processed

Potential for data leaks

Public AI tools are vulnerable to cyberattacks, increasing the risk of data leaks and hefty penalties

Unpredictable model behavior

Public AI models can produce unpredictable and inconsistent results affecting patient care quality and trust in your organization

Relying on public AI models for healthcare-related purposes can pose significant risks

GreenM unlocks Private AI for Healthcare providers

How does it work? 

Taking Large Language Model like ChatGPT

Installing it on a private infrastructure

Integrating it with your system

Fine-tuning the model using your dataset

Built for US Healthcare providers to withstand cyber threats and retain complete control over data processing

results:

Data does not leave the boundaries of your organization; therefore, it is safe

Ensuring adherence to HIPAA compliance and other industry regulations

Streamlined AI adoption

Want to start working on your private AI?

Use your local AI model for

SUPER
STAFFING

Up to 63% of physicians report symptoms of burnout, reducing workforce retention

AI tools save 2 hours daily per clinician, enabling 20-25% more patient visits. Burnout rates drop by 30%, improving retention.

PATIENT SATISFACTION

In 2024, new patients in the U.S. waited an average of 26 days to see a doctor, with 71% seeking more personalized care and 30% citing poor communication as reasons for dissatisfaction

Boost engagement by 40%, cut wait times by 30%, and improve treatment adherence by 20-25%, fostering trust through personalized and efficient care.

EFFICIENCY MANAGEMENT

Hospital inefficiencies waste 25-30% of resources and drive costs, with facility and staffing expenses making up nearly 60% of healthcare expenditures

AI reduces costs by 20-25% and resource waste by 25-30%. Streamlined operations enhance patient satisfaction scores by 15-20%, with smoother transitions and reduced delays.

MEDICAL CODING AND BILLING

Around 80% of medical bills contain errors, and manual processes, increase costs and delays, with claims taking 7-14 days to process. Inaccurate billing causes significant revenue loss, and 48% of patients report confusion with bills

AI-driven billing reduces administrative costs by 40%, decreases denial rates by 30-50%, and boosts patient satisfaction by 20-30% through transparent billing systems.

Our case study

The AI-Powered Virtual Health Assistant provides:

• Instant access to critical information
• Automates repetitive tasks
• Ensures seamless system integration

What We Did

Built a secure AI Solution

We deployed the AI system directly within the organisation’s infrastructure, ensuring that all sensitive patient and operational data remains secure, fully controlled, and compliant with internal policies and healthcare regulations

Enhanced AI Accuracy

By fine-tuning the AI model with the organisation’s existing data, we made it highly precise and responsive to the specific needs of the organisation, ensuring more accurate and context-specific outputs

Simplified Data Access

We designed an intuitive interface that allows teams to ask questions in plain language and receive clear, actionable insights. This empowers decision-makers to retrieve key insights without relying on technical experts

RESULTS

The implementation delivered significant advancements in operational efficiency and routine automation, achieving the following outcomes:

Reduced Patient Waiting Times

Faster access to critical data enabled quicker decision-making and care delivery

Decreased Staff Workloads

By automating repetitive tasks, healthcare professionals could redirect their focus to high-value, patient-centered activities

Enhanced Patient Outcomes

Improved workflows and seamless access to information led to higher-quality care and increased patient satisfaction

GreenM:
Leading Healthcare innovation with generative AI expertise

10+

Years experience in Healthcare domain

Anton
Avramenko

Lead of AI Research

Vitalii
Samarskyi

Solution Architect

Lena
Kirienko

Head of Product

Alexey
Litvin

CEO, Founder

AWS certified

AWS certified

Azure OpenAI

Global AI community

Meta AI

Department focused on AI research

GitHub
Copilot

LangChain

AWS certified

AWS certified

Azure OpenAI

Global AI community

Meta AI

Department focused on AI research

GitHub
Copilot

LangChain

Discover how
Private AI helps
US Healthcare providers

USA office
Redwood city,
CA 541 Jefferson ave, #100

Ukrainian office
Kyiv, 3 Dorohozhytska str, #27
Kharkiv, 10 Nezalezhnosti ave, #716
Lviv, 11 Shpytalna st.

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