Health monitoring systems continue to evolve in this digital age. AI-powered health-tracking wearable devices today continuously monitor vital signs of a patient’s health and basis that generate a large number of observations per second. However, healthcare providers find it challenging to manage multiple data sets across a large volume of patient data. To address this, machine learning applications in healthcare provide real-time activity recognition within clinical systems and further help them with better diagnosis and treatment.
Read on to learn how Rishabh Software helped a UK based critical healthcare provider with an ML-based health monitoring solution. Through that, how we enabled them to achieve a better healthcare outcome to lower costs and achieve higher patient satisfaction.
Our customer was seeking a reliable health tracking system for critical care patient diagnosis. As part of this, they wanted us to develop an ML-based solution to collect and monitor the sequences of patient data recorded by connected devices. Further, the developed system would offer real-time insights from historical and current health data sets as and when needed.
To enable activity recognition through a reliable health monitoring system, we created data models for connected devices. Towards this, we first gathered the raw patient data from the installed devices of the healthcare provider. The readings were collected across various equipment, including critical care monitors, echocardiography, arterial blood gas (ABS) analyzer, and more.
Further, we implemented machine learning algorithms in the healthcare system, which would:
We divided the data into windows of one second of observations, with a 50% overlap. We deployed the system at scale on multiple users to map and analyze the extracted data using machine learning for healthcare applications. It would continuously monitor the vitals of the patient, such as VO2, heart rate, ECG, and more, to provide real-time insights to the medical professional.
To conclude, Rishabh Software facilitated a machine learning-based model for activity recognition within the healthcare system. It enabled the healthcare provider to analyze large sets of patient data in real-time to offer recommendations for better diagnosis. As a result, today, the customer can provide treatment for different ailments through a real-time health monitoring system while improving their operational efficiency.
UK based critical healthcare provider
We assist medical practices of all sizes in improving healthcare outcomes through ML-based systems