10 Best Healthcare Companies 2019
The Silicon Review
Leveraging the revolution in bio-sensor technology and personalized predictive analytics, Biofourmis is working towards empowering patients with tools for post-acute monitoring at-home to increase patient safety, thereby reducing avoidable readmissions.
Biofourmis houses an experienced team of healthcare innovators who addresses the crises in complex chronic disease management together by providing continuous physiology insights anywhere, anytime. Biovitals™, developed by Biofourmis is a powerful patented AI-based platform for therapeutic decision support. It continuously analyzes data from medical grade wearable sensors and other patient-generated data to create unique digital biomarkers reflecting the health status of an individual. These digital biomarkers have been assessed in several real-world studies and have demonstrated the ability to help detect changes in an individual’s health status, much earlier than possible before.
Thanks to the strength of the Biovitals™ platform, Biofourmis is now partnering with leading healthcare providers, payers, and pharmaceutical companies around the world to help reduce readmissions, limit ER visits, improve patient outcomes, and rethink the way care is managed and reimbursed.
Biovitals Analytics Ecosystem
At the core of Biofourmis is the Biovitals™ Analytics Engine, a highly sophisticated personalized physiology-based data analytics engine. Biovitals™ incorporates advanced machine learning and proprietary artificial intelligence to constantly evaluate individual patient health data generated from wearable sensors and monitors. It is designed to integrate readily available biosensors to monitor a patient’s physiology to build dynamically varying personalized physiology signatures which predict a patient’s health deterioration, health improvement or drug/ therapy’s utility.
Biovitals™ Digital Biomarkers: Biovitals™ Analytics accommodates the full suite of Biofourmis digital biomarkers resulting in disease/therapeutic specific models, which form the backbone of itsplatform. Using codified clinical expert knowledge and machine learning techniques, Biovitals™ processes raw electrical and optical bio-signals to derive digital biomarkers which indicate early signs of patients’ health deterioration.
These digital biomarkers support the development of a wide range of disease/therapeutic specific models including heart failure, pain, myocardial infarction, respiratory conditions, neurology, and oncology.
Biovitals™ Therapeutics Utility: The influence of specific interventions on an individual’s physiology can be not only quite variable but also unpredictable. In certain complex conditions that require combinations of medications, the drug-drug interactions, the drug-disease interactions, and adverse side effects can also be serious and unpredictable. Furthermore, some medications and/or interventions need to be closely monitored and/or titrated to find the optimal doses, time and frequency of administering the intervention or combinations, and effectiveness durations.
Biovitals™ therapeutic management system uses continuous physiology and other patient-generated data to monitor and quantify the effect of the prescribed therapy and side effects on patients while empowering clinicians with information to make better therapeutic decisions.
RhythmAnalytics™: RhythmAnalytics™ is a cloud-based platform that incorporates deep learning models trained by using over 2 Million annotated ECG data from actual patients to deliver unmatched accuracy in detecting dozens of cardiac arrhythmias. RhythmAnalytics™ can be integrated using its cloud-based APIs or directly into the cardiac monitoring device, making client integration flexible, simple and easy. The analytics engine is designed to process ECG data from Episodic monitors, Holter monitors or Mobile Cardiac Telemetry (MCT).
RhythmAnalytics™ transforms a labor intensive and inefficient process into a high-yield diagnostic tool ensuring high availability, freeing up physicians and technicians for more complex tasks. The platform is currently being trained to detect cardiac arrhythmias using PPG signals.
Preventing Heart Failure
With the prevalence of heart failure along with co-morbidities, patients struggle to manage their condition and adhere to their disease management regime.Also,clinicians and care teams do not have timeline information about patients’ decompensation and lack tools to better manage them.One in four heart failure patients aged over 65 years is re-hospitalized within 30-days accounting to the overall global cost of heart failure to more than 108 billion annually.
An AI-empowered personalized health analytics platform that optimizes engagement and uses physiology to predict heart failure exacerbation days before a critical event. It leverages robust physiology and bio-psychological analytics to identify deviating/contributing factors along with rules and deep content to generate personalized insights. It also enables personalized triggers and recommendations to empower patients to take control of their condition and helping clinicians monitor the effects of therapy and provide personalized care.
Meet the Leader
Kuldeep Singh Rajput, Founder, and Chief Executive Officer:Kuldeep is an active member of the healthcare innovation and entrepreneurship community in Singapore, with a specific interest in technology-enabled healthcare innovation. During his Ph.D. studies at the National University of Singapore (NUS), he was building bioelectronics implants, which aim to control biological processes and treat diseases by modulating electric impulses. Prior to moving to Singapore, he was a researcher at MIT Media Lab, where he was working on building diagnostic algorithms for cardiac arrhythmias and sleep apnoea in partnership with Massachusetts General Hospital. Kuldeep has been invited to speak at some prestigious events on how they are augmenting personalized care using digital therapeutics at TEDx, American College of Cardiology Conference (ACC 2018), SwissRe –Health Monitoring in Insurance, HIMS, etc.