India’s healthcare sector can be considered a sunrise sector, purely from the perspective of enhanced expenditure from public and private sectors, startup activity and its rapid growth. In addition, the government too has turned its efforts in recent years to make healthcare more accessible by launching the world’s largest funded healthcare scheme Ayushman Bharat, with a mission to insure 500 million people across the country.

However, despite the sector’s scale, growth and massive investments to bolster the quality and coverage of medical products and services, Indian healthcare grapples with several challenges and these were exacerbated by the COVID-19 outbreak. The need for immediate, affordable and accessible healthcare has never been more profound. Compared to developed economies, healthcare in India paints a different picture in terms of cost and expenditure. It has the lowest per capita healthcare spend, absolute and public healthcare expenditure as a percentage of GDP. But the sector now stands at an inflection point with AI poised to fill gaps that have existed and expanded over decades. How is it going to achieve this at scale?

NASSCOM in association with EY launched a report Unravelling AI For Healthcare In India, at the 8th edition of NASSCOM Center of Excellence IoT-AI's flagship healthcare initiative Lifesciences & Healthcare Innovation Forum (LHIF); which presents the integration of digital technologies, specifically AI in enabling care with ease. The report focuses on adoption opportunities in healthcare in key areas like hospitals, diagnostics, pharma and lifesciences.

Scope of Digital Adoption:

Currently around 31% of the patients use digital tools to search and schedule appointments and 27% use online booking for diagnostic services. More than 80% of the doctors claim that patients value convenience more and expect the doctors to answer queries through mobile (over SMS and WhatsApp apps). Primary source of information for doctors shifted to digital channels (83%) from peer discussions and medical representatives which are currently 73% and 62% respectively.

Over the years, non-communicable disorders (NCD) have increased, due to lifestyle choices and an aging population of the country - 6.4% of the population are above the age of 65. There are increasing instances of diseases like diabetes, cancer and heart ailments that require prolonged and specialized medical care. However, overall income levels has also gone up, and the government spend on healthcare has risen from 1.6% of the GDP in 2020 to 2.5% by 2025. More Indians are getting better medical advice through proper healthcare channels, and access to healthcare information online too has increased. This has indicated a much needed shift from reactive care to the preventive care mindset as the former has more costs associated with it.

How Can AI Help?

As the population aims to make the switch from reactive to preventive care, there are some immediate areas the change can begin with the help of AI. This has especially been highlighted keeping the immense pressure COVID has placed on the nation’s health infrastructure

• Hospitals: Hospitals are over worked in India, mainly due to the low doctor-patient ratio (1:1445). In addition, poor healthcare infrastructure (0.55 beds per 1,000 population), limited skilled manpower and capacity challenges - shortage of beds, ventilators, ICUs, etc during COVID have exposed the systemic flaws. AI powered digital triage and chatbot for self-assessment if the patient needs immediate care and Intelligent clinical note creation and documentation of the procedures are two viable options to be explored.
• Diagnostics: Lack of proper SOPs, regulations and skilled resources often result in diagnostic errors. Lack of harmony in test procedures across labs, shortage of medical staff, insufficient testing capacity and resources (PPE kits, testing kits), inadequate biosafety measures and practices have become pressing issues for diagnostic labs to explore during COVID. AI algorithms for rapid detection of COVID based on CT scans and patient symptoms and use of trained AI systems to aid in early prognosis and accurate diagnosis
• Pharma and life sciences: Lack of stable pricing and policy environment have resulted in thin margins and low R&D investments. Moreover, there is heavy reliance on China for APIs, and rising costs due to supply chain disruption of APIs. AI can be used for computational drug design and lab validation that speed up the drug discovery process with trials using AI simulations to fast-forward drugs to market
• Medical devices and equipment: The high cost of product design and development, as well as stringent government regulations have led to the need to develop quality ventilators, test kits, PPE, etc. with crashed timelines. Moreover, supply chain disruptions due to the nationwide lockdown delayed delivery cycles. Instead, AI and ML could be used to monitor chronic patients using IoT and automate delivery of treatment. Additionally, AI can be used to predict device downtime with the help of IoT.
• Health insurance: Less than 20% of the population is insured, and even that segment is plagued with challenges like inefficiency in claims processing and fraudulent claims. With COVID, there has been a surge in renewal requests and fraudulent claims as well. Mobile based data image verification using AI, and fraud detection using AI and ML can exacerbate these issues.

To read the entire report, click here