HATO provides a cloud-based ECG analytics platform that utilizes artificial intelligence and machine learning to deliver cardiologist-level analysis for healthcare professionals, including non-specialists. This technology enables accurate interpretation of electrocardiograms, improving diagnostic support and decision-making in clinical settings.
Funding
$440K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Healthcare professionals, especially non-specialists, often face challenges in accurately interpreting electrocardiograms (ECGs), potentially leading to delayed or incorrect diagnoses. Traditional ECG analysis can be time-consuming and requires specialized expertise, limiting its accessibility in various clinical settings.
Solution
HATO provides a cloud-based ECG analytics platform, SmartHeart®, that leverages artificial intelligence (AI) and machine learning (ML) to deliver cardiologist-level analysis to a broad range of healthcare professionals. The platform automates ECG interpretation, providing diagnostic support and decision-making assistance, even for users without extensive cardiology training. By combining visualization, machine learning, and technical innovations, HATO aims to improve the accuracy and efficiency of ECG analysis, ultimately enhancing patient care. The cloud-enabled system allows users to access previous recordings and scroll through stored data.
Target Audience
The primary target audience includes paramedics, general practitioners, non-cardiologist healthcare professionals, and other medical staff who require accurate and efficient ECG analysis.
Features
- Cloud-based platform for easy access and data storage
- AI-powered analysis for automated ECG interpretation
- Continuous improvement of analysis through cardiologist feedback
- Visualization tools for enhanced understanding of ECG data
- Patented HATO Heart technology for cardiologist-grade analysis