About the client
The client sought our expertise in developing an innovative software solution capable of seamlessly integrating with their existing clinical workflows and digital health devices. They were looking for data security, adherence to HIPAA compliance, and the proprietary zero-knowledge system, which ensured secure encryption of Protected Health Information, instilling confidence in the safety and privacy of patient data. With a shared vision for advancing healthcare through advanced analytics and AI, the client partnered with us to pave the way for more accurate diagnoses, timely interventions, and ultimately, improved patient outcomes.
In this research and development project, QIT engineers have developed a cutting-edge tool that leverages artificial intelligence (AI) and deep learning to level up the interpretation of electrocardiograms (ECGs) mechanisms. By integrating advanced technology with cardiac patient care, monitoring, and diagnostics, our innovative solution enhances efficiency and accuracy in the medical field.
This software has all potential to be a game-changer in remote patient monitoring and clinical diagnostics, empowering continuous cardiac monitoring and comprehensive patient health surveillance. By seamlessly integrating with digital health devices and applications, the tool’s task was to effectively interpret and annotate electrocardiograms, supporting healthcare professionals in delivering exceptional care.
Given the healthcare focus of the product, we prioritized key requirements, including compliance with the Health Insurance Portability and Accountability Act (HIPAA), adherence to quality and performance standards for medical equipment, near real-time processing accuracy, and reliable functionality in remote and challenging environments.
QIT dedicated Python engineers have successfully developed an unprecedented tool that revolutionizes the interpretation of electrocardio diagrams. Utilizing deep learning techniques, the end product was supposed to detect and annotate a wide range of cardiac events in accordance with the HL7® aECG standard. Solution should be integrated into an Electronic Health Record (EHR) system or used in conjunction with a mobile health device. To ensure the utmost security, we have implemented a proprietary zero-knowledge system that securely encrypted Protected Health Information.
Here's how it works
Electrocardiogram data is obtained through a compatible digital recorder via API or a web-based platform. The data is automatically transferred to our system for interpretation.
Our advanced algorithms analyze the data, recognizing patterns and detecting cardiac events. The software automatically calculates amplitudes and intervals. The findings are presented in visual formats such as charts, diagrams, and tables, highlighting points of interest.
Deep Learning and Artificial Intelligence: At the heart of our product lies the power of pattern recognition. Our team has employed deep learning techniques and harnessed big data from a comprehensive ten-year population study. Through it, all records were carefully labeled and verified by qualified cardiologists. These labeled records were then used to train our system, leveraging artificial neural networks.
As a result of our QIT developers’ work, a system capable of detecting subtle patterns that even experienced doctors may overlook has been created. It has the potential to significantly improve patient monitoring and care in the medical field.
By reimagining electrocardiogram interpretation through AI and advanced analytics, our client is paving the way for a future where healthcare professionals can deliver more accurate diagnoses, timely interventions, and ultimately, better patient outcomes.
Curious but not convinced?
If you don’t know where to start, we will be happy to guide you with a free estimate for timeline and price.