PreSco

India has a high neonatal mortality rate of 25 and the global highest first 24-hour neonatal deaths. Low healthcare resources in rural areas, concentration of neonatal specialists in urban areas, low access to culture tests and delays in getting lab results have resulted in higher mortality, morbidity and antibiotic resistance among newborn babies creating a strong need for digital health platforms that especially work well in low resource areas.

Close to 70% Indians live in rural areas while about 80% specialist services are concentrated in urban areas of the country. People have to travel at least 50 to 100 Kms distance to avail of quality healthcare from tertiary health centres. However, Smartphone usage is increasing rapidly especially in rural India. Launch of 5G is expected to drive digital healthcare adoption in the coming years. 25 million babies are born in India and 10 million of them are located at the bottom of pyramid. 95% of clinical data in India exists on paper today, a large portion of which could be used for deriving meaningful insights and improving digital health in India. India’s electronic medical records (EMRs) market is expected to touch USD 315 million by 2026, growing at an average of 7%.

This large untapped base for neonatal health solutions in India provides a strong case for feasibility of a digital platform developed by us called PreSco. PreSco aids in reducing the existing gaps in early diagnosis and treatment of neonatal health through a digital platform that uses electronic medical records and AI-ML based risk prediction algorithms.

Our solution, PreSco is a digital health platform with electronic medical records and machine learning algorithms for predictive risk scoring of neonatal infections. PreSco can be accessed from any computing, mobile or touchscreen device. The current architecture of PreSco is such that – available clinical parameters / reports can be entered into the application which are transmitted to a cloud server where the platform’s EMR platform and machine learning algorithms are housed. The algorithms compute a neonatal sepsis / infection risk score. This risk score which is colour coded - red for high risk, orange for medium risk and green for low risk is returned to the device. Finally, a referral window pops up which provides the option for expert opinion from a far-off health centre.

PreSco is suitable for both B2B as well as B2C segments. PreSco is an end-to-end platform that covers all aspects of primary health starting from early diagnosis, treatment, storage of medical records, mechanisms for follow-ups and feedbacks, tracking medical supplies and essentials at clinics, referral connects for expert advice etc., all of which are achieved using cloud and ML at the edge technologies to ensure efficient operations cost, affordability and security. PreSco runs on open-source technologies enabling affordability, interoperability and low-cost operations to all stakeholders involved.

Recently, during our preliminary validation at a remote hospital in rural India, we found a strong need for a digital platform that works with minimal power and low bandwidth requirements. Concerns around healthcare data privacy and security were also raised by healthcare practitioners. In view of the learnings gained, we are enhancing our current cloud platform by including an intermediary, an at the edge computing device for improvised usability in low resource areas. This enhanced at-the-edge platform would jointly address data privacy and security concerns as well.

Payment System – PreSco has an active payment system (with existing mode of payments) for score generation and analytics.

PreSco is suitable for frontline and nursing staff and other medical practitioners involved in neonatal care provision, who can enter data into the platform for storage, retrieval and analysis. Types of data include basic demographic data (encrypted wherever applicable), vitals and other clinical information, laboratory reports, images such as X-Rays, CT scans, medicine & antibiotic details and all other relevant information pertaining to electronic medical records and admission data.

PreSco’s affordable electronic medical records platform can be accessed from mobiles, desktops or any touch devices easily by mothers for checking status of their babies check-up follow-ups, vaccine alerts, etc., and practitioners for regular updates and maintaining clinical continuity.

PreSco digital health platform can be used by both B2B as well as B2C segments. 

For B2C, PreSco, aids parents with affordable electronic medical records application, medicines and doctor visit reminders, connect with nearest primary health center or health volunteer and other emergency services. 

For B2B segment, PreSco aids clinicians and hospitals with an affordable and exhaustive electronic records system integrated with risk algorithms for early assessment of neonatal infections and their timely and effective management. A referral system embedded in the platform helps health volunteers and practitioners to obtain expert advice from a far-off health expert. PreSco serves to integrate a primary healthcare provider with practitioners and patients in a seamless manner to ensure delivery of quality healthcare and its continuity.

Avyantra Health Technologies was founded in 2017 and is based at Hyderabad. The founders are passionate about healthcare and are committed to the vision of building innovative healthcare solutions. We have a well-rounded team with a wide range of experience in healthcare, marketing, and Full-stack development. We are advised by domain experts from healthcare and data analytics sectors. We have recently received BIRAC-BIG funding, one of the biggest early stage funds in India, and have graduated from UNICEF Innovation’s global cohort of Data science startups in 2019.

We are a team of passionate healthcare entrepreneurs and enthusiasts with a common aim to make quality healthcare accessible and affordable. We have been working in this area for close to 4 years. We have a good understanding of the issues and challenges existing in delivery of healthcare services in the primary health and frontline space which we expect will give us an edge and place us on a better footing to drive our solution.

With support from MIT Solve – Gates Foundation, we aim to achieve the below objectives -

1.     Enhance our existing cloud only digital health platform, PreSco, with ML at the edge capabilities, to make it suitable for low resource areas and test and validate it for commercial adoption.

2.     Improvise EMR features such as text to voice conversion, unstructured to structured data conversion, improvised dashboards, Image analysis

3.     to enable ease of uses.

4.     Extend payment options for B2C segment.

5.     Integrate the existing platform with laboratories (diagnosis), pharmacies, health insurance service, hospital supplies and other administrative functions to facilitate seamless movement of information and provide a one-stop-solution for an entire hospital or clinic.

6.     Improve features on collection and integration of local epidemiological data tp provide alerts on any outbreaks.

7.     Enhance the overall security of the platform.

We strongly believe that associating with MIT Solve and Gates Foundation will provide us with the necessary resources to handle the various barriers we face in the market.

Our Novelty is provision of an EMR Platform that come with risk prediction algorithms that can be accessed from any device and is hybrid operated (cloud as well as edge devices). At-the-edge computing capabilities aid in improvised usability in low resource areas. Our IP is based on a unique computing device and server based analytical and scoring platform with multiple parameters of mothers and new-borns that facilitate digital health in low resource areas. This enhanced at-the-edge platform jointly addresses data privacy and security concerns too. ML-at-the-edge is a hybrid platform that works with both cloud and local server infrastructure. At-the-edge models are decentralised with no requirement for data transfer to a central server. Since, data need not be transmitted to a cloud server every time, data privacy and security are taken care of. Additionally, at-the-edge devices consume less power and internet bandwidth compared to a cloud model, providing an edge over existing models and thus work well in low resource areas. These features make our solution unique which also help us in overcoming the issues around low power, bandwidth and concerns on data security and privacy.

In the next 12 to 18 months, post completion of our platform development and validation, we expect to comply with India’s National Digital Health Mission’s (NDHM) guidelines and move towards commercialization.

In the next five years, we will focus on expanding our user base in the domestic market initially, moving to international markets, soon after.

Financial impact of PreSco – PreSco aids in bringing the cost of delivering primary healthcare down by a significant percent. PreSco aids in early diagnosis of neonatal infections which in turn promises reduced usage of antibiotics, upto 50%. Digital health market is expected to go up to USD 350 million by 2026 from the current USD 200 million. Digital health aids in improving operational efficiency, reducing costs and urban-rural divide in healthcare service provision.

Scale through large Rural Demand – Rural market in India is very large with more than 60% of the population residing in these areas. India’s neonatal market is expected to witness an incremental growth by USD 4 billion from 2022 to 2026 every year. Electronic medical records sector is a sunrise market with a large potential for growth. Due to lack of quality healthcare resources in rural areas, need for an integrated digital platform that can reduce the existing gaps in healthcare delivery is high.

Scale through Cost-effectiveness – Smartphone usage and penetration in rural India is increasing at a faster pace over that of urban towns. Launch of 5G services is further expected to drive demand for mobile health and related digital services. Options for easy scale-ups and use of open-source technologies aid in cost-effectiveness of our solution and is are expected to accelerate growth.

Scale through Operational and Financial Growth by Partnerships - Our project is aligned with Universal health coverage programmes in India, and Sustainable Development Goals (SDGs) that together aim to reduce neonatal & infant mortality and provide universal health coverage to maternal and child health requirements in India and across the globe. We are right now partnered with domestic institutions and incubators to test and pilot PreSco and continue to explore opportunities to expand.

INDICATORS FOR EMRS – Ease-of-use, language options, download and upload time, Referral time, latency issues, transfer of reports, Comparable speeds on cloud and edge device, data security and interoperability.

 INDICATORS FOR RISK PREDICTION ALGORITHMS – sensitivity, specificity, accuracy, F1 Ratio, Latency and security.

During our prototype validation recently at a remote town in India, we found a strong need for a digital platform that works with minimal power and low bandwidth requirements. Concerns around healthcare data privacy and security were raised by healthcare practitioners. During our PoC stage, we conducted in-depth interviews of 15 practitioners in the field including neonatologists at Government and private hospitals and a district medical officer to understand the issues in Neonatal health management in India. 70% of the sample expressed interest to use PreSco.  

Our theory of change – Our solution, PreSco, provides a digital health platform for newborns and mothers that integrates all stakeholders of primary healthcare delivery (frontline health volunteer, nursing staff, technicians, clinicians, doctors, laboratories, pharmacies, insurance providers etc). Presco enables seamless flow of information between patients, clinicians and all other stakeholders, thereby resulting in improved delivery of healthcare services in primary care especially in rural and semi-urban areas in low resource areas.

Activities

Outputs

Short term outcomes

Long term outcomes

Add ML-at-the-edge capabilities to address low power and internet bandwidth issues for existing cloud platform of PreSco

Continuous access to PreSco’s EMR platform, local processing of data, reduced security concerns.

Faster run time, Reduced expenses, saving power and hardware investments, Reduced delays in providing treatment.

Improved quality of neonatal healthcare and seamless  delivery of services in rural areas.

Digital Training of Users

Ease of use by practitioners.

Increased use of platform leading to improved efficiency in delivery of treatment

Well-established linkages between rural and urban health resources. Reduction in morbidities and mortality.

Integrate all stakeholders of primary healthcare delivery (frontline health volunteer, nursing staff, technicians, clinicians, doctors, laboratories, pharmacies, insurance providers etc) in a single platform.

Enable seamless flow of information among all stakeholders.

Reduced time delays between workflows, enabling easy connect between departments, and reduction in overall time and improving efficiency.

Single use platform for addressing the gamut of healthcare services, used by clinicians, hospitals and patients.

Edge AI-ML (Artificial intelligence – Machine Learning) is a hybrid platform that works with both cloud and local server infrastructure. At the edge models are decentralised with no requirement for data transfer to a central server. Since, data need not be transmitted to a cloud sever every time, edge computing helps in computing time-sensitive due to low latency and privacy denominated data. Thus, data privacy and security are taken care of. Additionally, at-the-edge devices consume less power and internet bandwidth compared to a cloud model, providing an edge over existing models and thus work well in low resource areas and reduce overall cost of operations.

Healthcare practitioners, nursing assistants and frontline health volunteers are expected to be actively involved in collecting primary data using PreSco.

 Incentives for using PreSco include ease and reduced time of diagnosis, easy retrieval and analysis of data, enhanced decision making, achieving operational efficiency, and monetary incentives (if any, provided by the healthcare provider).

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. Along with addressing gender equality, our platform is focused on the goals of equitable health and well-being and reduction in overall inequality through use of cloud-based platform technologies that are affordable and accessible. Our team has a fair representation of women members. Male members of the consortium are sensitive to gender issues and strongly uphold values of gender equality in all spheres of life.

Our revenue model is based on 1. pay-per-use and 2. annual subscription. We expect to hit positive margins (greater than 30%) from the second year of our operations.

For the near term, we are applying for grant funding till we reach go-to-market and scale-up stages.

As we move towards commercialization, we expect to draw in a greater portion of our revenues from the B2C platform (about 60 to 80 percent) and the rest from B2B platform. This flow of consistent revenues will help us to achieve operational self-sufficiency and sustainable growth

In the long term, i.e., two to five years from now, as we move towards scale-up of our operations, we will pitch to venture capital and other institutional funding for our growth. 

Ever since we conceptualised our solution, we have so far received grant funding upto INR 14000000 (approx. USD 150000). Recently, we received BIRAC-BIG funding, have been shortlisted for AIM-ARISE fund, and have graduated from UNICEF Innovation’s global cohort of Data science startups in 2019. In the last one year, we also received piloting opportunities from Telangana State Innovation Cell and NASSCOM Jancare.