Leveraging AI in the Indian healthcare system: Opportunities and challenges
- According to NASSCOM, data and
AIhas the potential to contribute $25-$30 billion to India’s GDP by 2025
- AI can reduce the cost of healthcare delivery without compromising quality or accessibility
- Regulatory, security and privacy challenges must be addressed before advanced and widespread adoption of AI
AdvertisementThe pandemic and widespread adoption of technology in healthcare has ushered in a sea change in India and around the world. The healthcare sector in India has begun to integrate AI across the healthcare value chain, from patient care to medical management to disease prevention.
AI is being widely used as a tool for case assessments to support specialists such as doctors and radiologists. According to NASSCOM, data and AI have the potential to contribute $450-$500 billion to India’s GDP by 2025 and the healthcare sector is a significant beneficiary of the potential value add with a contribution of $25-$30 billion. In the post-COVID era, NASSCOM believes, AI will be instrumental in timely epidemic outbreak prediction, remote diagnostics, and optimized health resource allocation.
In India, leading hospitals are already leveraging the technology for advanced use cases.
Last year, Apollo Hospitals launched an AI tool to predict the risk of cardiovascular disease aimed at initiating early intervention. The tool, developed on Microsoft Azure, is built on algorithms based on ten years of anonymized data from around 400,000 individuals across the country. Manipal Hospital, for example, is one of the first hospitals to use IBM Watson, a cognitive technology platform, in Oncology to improve cancer treatment.
AI aids professionals to review images and scans with more accuracy and precision, helping diagnose conditions earlier, which can lead to better patient outcomes. Experts point out that AI can potentially reduce the cost of healthcare delivery without compromising quality or accessibility, which can eventually revolutionize the
“The gap between the required and current growth in healthcare infrastructure and care-providing staff in India cannot be addressed through an incremental and linear approach. Latest advancements in technology like AI, Internet of things (IoT) and blockchain can enable the healthcare industry to adopt disruptive technology-led service and business models, scale up for access and affordability, and take the winning leap to make India a global healthcare hub,” writes Arnab Basu, Partner and Technology Consulting Leader, PwC India.
AI addresses acute healthcare challenges
Shortage of qualified healthcare professionals is an acute challenge that the country has been facing over the years. This is an area where AI can make an immediate difference.
“According to the WHO, the doctor-to-patient ratio in India is 1:1456 as against the 1:1000 recommendation. Doctors have significant administrative responsibilities, including documenting patient encounters, answering inbox messages, ordering medications or tests, and much more. AI-powered voice assistants can help physicians complete many of these tasks more quickly, enabling them to spend more time with their patients,” Nitin Gupta, Vice President- Engineering and India Head, Suki – an AI powered, voice enabled digital assistant for doctors.
Gupta says that the increasing adoption of electronic health records (EHR) among Indian healthcare providers will add more administrative burden for doctors and AI will play a key role in taking these clerical tasks away from doctors and healthcare professionals. Currently, healthcare providers are focusing on similar AI use cases that have ease of implementation and immediate RoI.
In the long run, however, AI has the potential to transform care delivery, operational excellence and customer experience. R&D and healthcare innovation are another top priority for AI implementations.
Data complexities abound
While the possibilities of AI in transforming healthcare are almost endless, Indian healthcare providers must deal with regulatory concerns or more precisely, the absence of regulation in the private sector. The complex and variable nature of healthcare data, along with growing ethical and privacy issues, makes it highly challenging to build AI algorithms.
Another critical challenge, says Gupta of Suki, is around interoperability. AI requires vast datasets to operate, so enabling systems to share data will result in richer, more comprehensive datasets to power AI solutions.
“At the same time, healthcare providers must appreciate the importance of data security and privacy, and permissions and protocols must be put in place to guarantee that people only have access to the information they need to do their jobs. To reduce the risk of bad actors, infrastructure must be developed. Finally, it's critical to communicate to the market exactly what data can and cannot be used for,” he adds.
Furthermore, because AI-generated models can be difficult to explain, doctors now have the additional burden of understanding and having confidence in recommendations from AI-powered solutions. For clinical recommendations, regulatory oversight should be heavily considered to help ensure the safety of these solutions.
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