Alexey Litvin | CEO
Trusted by:
Why using
Public AI
might not be safe for Healthcare providers
Patient data privacy
Sharing information with public AI risks exposing patient and internal data to unauthorised access. This not only affects trust but also risks non-compliance with regulations like the GDPR, DPA 2018 and NHS Digital standards
Lack of control over data
Healthcare providers may lose control over data management and processing with public AI models, leading to potential unauthorised access or misuse of sensitive information
Potential for data leaks
Public AI platforms are vulnerable to cyberattacks, increasing the risk of significant data leaks leading to reputational damage and heavy penalties
Unpredictable model behaviour
Relying on public AI models for healthcare-related purposes can pose significant risks
GreenM unlocks Private AI for Healthcare organisations
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 UK Healthcare 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
Adherence to UK GDPR and NHS Digital compliance
Streamlined AI adoption
Use your local AI model for
SUPER
STAFFING
More information
- Virtual medical assistants to handle routine patient interactions, reducing the load on medical professionals
- AI models to predict patient inflow and resource demand to optimise scheduling
Achieve better staff utilisation with a 30% reduction in unfilled shifts and overtime costs. Optimise resource allocation to unlock up to a 25% increase in productivity, ensuring a more balanced workload and improved efficiency across healthcare team.
Waiting Times and patient satisfaction
The NHS is struggling with record-long waiting lists. Patients often face delays of 8-12 weeks or longer for elective procedures and specialist consultations
More information
- AI-Powered Scheduling: Automates and optimises appointment scheduling, reducing bottlenecks and improving efficiency
- Predictive Analytics: Forecasts demand for services and dynamically allocates resources to meet patient needs
Leveraging AI can lead to a 30% reduction in patient wait times and a 25% improvement in patient satisfaction, addressing critical challenges in healthcare delivery.
Medical Invoicing Accuracy
Billing errors lead to delays, claim rejections, and financial losses, with up to 15% of claims affected
More information
- Automated Medical Coding: AI assigns accurate billing codes by analysing clinical documentation, reducing manual errors
- Error Detection and Validation: Identifies and corrects discrepancies in claims before submission, minimising rejections
AI solutions can improve billing accuracy to 98%, reduce administrative workload by 40%, and accelerate payment cycles by 30-40%.
Data Integration
Over 70% of healthcare professionals identify system incompatibility as a major barrier to care coordination
More information
- AI-Driven Integration Platforms: Harmonise disparate systems and enable seamless information sharing, creating a unified data environment
Enhance data integration and reduce manual workloads by 50%. These improvements drive a 20% boost in operational efficiency across departments and up to a 15% reduction in unnecessary diagnostic tests through better data availability and streamlined processes.
Our case study
The AI-Powered Virtual Health Assistant provides:
• Automates repetitive tasks
• Ensures seamless system integration
SHOW DETAILS
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
Our team
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