Avyantra Health Technologies Private Limited

If selected for the Elevate Prize, I will use the funding support to scale up the AI-ML platform PreSco, Predictive Scoring, that I and my organization have been developing. More specifically, I shall deploy the fund for testing, validation and commercialization of the platform.

PreSco is a cloud-based application for early detection of Neonatal Sepsis. It can be accessed from any device (mobiles/desktops). It uses data analytics and machine learning algorithms for an early assessment of a baby’s condition. This solution is ideal for low resource countries like India where 80% health specialists are concentrated in urban areas. 

My name is Hyma Goparaju. I am Founder-CEO of Avyantra Health Technologies, a startup that I founded in the year 2017 in India. I am passionate about healthcare. I am a PhD and an MBA with industry experience close to 20 years. Under my leadership, Avyantra Health Technologies graduated from the Unicef Innovation Fund’s global cohort of 2019 focused on Data Science. Hyma also led her company to secure funding support from HDFC SmartUp in India for development of the platform. Our company is focused on developing affordable and accessible healthcare solutions for low resource countries like India. Our application is built using open-source tools and technologies. The same have been made publicly available in GITHUB platform with the GNU license option. https://github.com/kvklrao/Avyantra_Presco_Project

Problem & Scale – Every year, 25 million babies are born in India and 10 million of them are at the bottom of pyramid. Six lakh babies die annually due to neonatal infections and 30% are due to Neonatal Sepsis. Neonatal mortality rate (NMR) is 25 while it is 1 or 2 in developed countries. During the recent COVID-19 pandemic, mothers and babies have been reported to be badly affected across the globe.

Causes – Primary factors for high neonatal mortality in India due to neonatal sepsis are delayed diagnosis (culture reports take between 48 and 72 hours), limitations of current blood and culture tests (non-specificity and non-sensitivity), low access to specialised healthcare in rural areas and low affordability. More than 40% neonatal deaths are reported to occur within the first 24 hours of birth chiefly due to low resources and time lost in transportation to urban centres. A recent study also reveals that administration of antibiotics from the time of birth causes metabolic and immunological disorders in the long run

Our Solution - Our integrated cloud-enabled platform uses machine learning algorithms for prediction of Neonatal Sepsis infections to address the shortfall of resources in rural areas. The platform has a referral system that connects urban health infrastructure with rural areas. It also provides a cost-effective electronic medical records platform that can be used by both practitioners and patients. 

Our platform PreSco is a cloud-based application for early detection of Neonatal Sepsis. It can be accessed from any device (mobiles/desktops). It uses data analytics and machine learning algorithms for an early assessment of a baby’s condition. Our solution is ideal for low resource countries like India where 80% health specialists are concentrated in urban areas.

Predictive Algorithms – Our uniqueness is centred around three models of Predictive algorithms that are customized as per the availability of resources at a healthcare centre. They work with a combination of non-invasive (observable clinical parameters) and invasive (blood reports, X-rays etc) health data as inputs to the algorithms.

• Frontline health volunteer model is primarily a non-invasive model.
• PHC or primary health model and
• Tertiary level model use both non-invasive and invasive clinical parameters.

Data Analytics – Our platform has a wide range of data analytics dashboards that can be used by clinicians to assess and track the health of babies. These are available on both web and mobile versions or our application.

• Descriptive analytics – analyses retrospective data.
• Predictive analytics – provides a future analysis through a risk estimate using prospective data.
• Prescriptive analytics – recommends treatment actions based on data of babies.

Our platform primarily addresses a critical area of ‘maternal and child’ health which traditionally has been side-lined as a female issue. Child birth and child care are viewed as women’s issues. Additionally, most frontline volunteers working in the maternal and child space in India are women. They have limited access to advanced technologies and tools. We strongly believe that our platform has the potential to empower them and redefine the way their work and healthcare delivery at point of care.

Health resources are constrained in developing countries like India. More than 40% of babies die during transit to urban centres in India. India also has the highest first 24-hour mortality of neonates. Digital and cloud platforms like ours have the ability to deliver quality health services to all nooks and corners. Our platform’s referral system to provide emergency treatments.

Our platform addresses the existing gap of low health resources and facilitates early identification of babies with infections and aids in optimal decision making for timely diagnosis.

Clinical decision support system (CDSS) can bring down usage of antibiotics significantly by at least 50% upon its consistent use and reduce cost of treatment, antibiotic resistance and mortality. We plan to impact at least 50,000 babies post launch.

Sustainability: The project is being undertaken with an objective of making diagnosis accurate, reliable, affordable, and sustainable with a well-defined business model. Our application was built using open-source tools and technologies.