Ward 24/7 develops a wireless Clinical Support System that continuously monitors vital signs of high-risk patients using machine learning algorithms to provide real-time alerts for respiratory and circulatory distress. This technology enables healthcare professionals to detect patient deterioration earlier, improving response times and potentially reducing complications and healthcare costs.
Funding
Funding not disclosed
Founders
Product
Problem
High-risk patients in various settings, including post-operative care and those with chronic conditions, require constant monitoring for early signs of respiratory or circulatory distress. Traditional manual monitoring methods are labor-intensive, infrequent, and may not detect subtle but critical changes in vital signs, leading to delayed intervention and potentially adverse outcomes.
Solution
WARD 24/7 CSS (Clinical Support System) provides continuous, wireless monitoring of high-risk patients' vital signs, enabling early detection of deterioration. The system uses machine learning algorithms to analyze real-time data from patient monitoring devices and generate alerts for healthcare staff, facilitating timely intervention. The user-friendly interface displays current and historical vital signs, offering healthcare professionals a comprehensive overview of the patient's condition. This continuous observation aims to improve patient outcomes, optimize resource allocation, and potentially reduce healthcare costs by enabling proactive management of patient health. The system has been tested in post-operative settings, with COPD patients, and with COVID-19 patients, as well as for home monitoring after discharge.
Target Audience
The primary target audience includes doctors, nurses, and other healthcare professionals involved in the care of high-risk patients in hospitals, post-operative care units, and home healthcare settings.
Features
- Continuous, wireless monitoring of vital signs including peripheral oxygen saturation, heart rate, respiration rate, and blood pressure.
- Real-time alerts generated by machine learning algorithms to detect patient deterioration.
- User-friendly interface providing an overview of current and historical vital signs.
- Integration with patient monitoring devices for seamless data collection.
- AI-powered interpretation of vital signs for high-quality patient observation.
- Remote monitoring capabilities for use in hospitals and at-home settings.