Catalyzing AI adoption in healthcare: The Indian Scenario

While India has made significant strides in improving health indicators, the goal of achieving health for all remains a formidable challenge, particularly given the population surpassing a billion. Noteworthy progress has been seen in the doctor-to-population ratio, which currently stands at 1:854, surpassing the WHO's recommended standard of 1:1000. Initiatives like Ayushman Bharat Pradhan Mantri Jan Arogya Yojana and National Health Mission underscore India's commitment to universal health coverage. However, persistent challenges persist, including shortages in health workforce, infrastructure, and technology, as well as regulatory and policy hurdles. It is in this context that the transformative potential of artificial intelligence shines, offering a tool in expediting India's journey towards achieving accessible, affordable and quality healthcare for all.

At People Plus AI, we recognize the imperative of adopting a case-by-case approach to AI in healthcare, given its immense potential. We are advancing AI adoption through a systematic approach:

• Identify and incentivise use cases - by releasing prototypes; conducting workshops and hackathons

• Unlock data - By partnering with clinics/ hospitals and creating a pipeline for accessing training data while maintaining patient privacy

• Model testing and evaluation - By partnering with clinics/ hospitals for testing and evaluation in their clinical workflows

• Model adoption and continuous monitoring - By setting standards for periodic testing

Challenge in focus: Low Health literacy

Consider the challenge of health literacy, a significant issue in India where 9 out of 10 individuals have low health literacy. Here, AI holds the potential to make a transformative impact. Something as seemingly straightforward as a personalized video featuring a trusted celebrity could wield remarkable influence. However, it is imperative to ensure content authenticity through physician verification and celebrity endorsement. 

With AI, there is a notable potential to reach and influence individuals in ways not previously possible.

Celebrity video mock generated using Eleven Labs

Challenge in focus: Delay in disease detection

One of the pressing challenges within healthcare revolves around the delayed detection of diseases. This not only amplifies the overall burden of the disease but also adds complexity to a patient's life. Introducing AI into this scenario holds great promise. By implementing a simple yet effective conversational symptom checker accessible via phone, we can potentially revolutionize the early detection process. Given India's linguistic diversity, a multilingual AI application would be invaluable. It's worth noting that in this context, prioritizing accuracy in detecting potential issues (false positives) is of paramount importance, even if it means a slight increase in false alarms. 

Automated customer conversations with AI-Powered Chatbot from Kommunicate.io

Challenge in focus: Lack of post consultation support

The patient-doctor engagement doesn't conclude at the consultation; numerous patients have follow-up inquiries. Many of these are recurring and amenable to AI-driven responses. Introducing an AI-powered voice-based chatbot for post-consultation interactions could significantly alleviate the current burden on doctors, allowing them to focus on critical cases. Nevertheless, it's imperative to maintain structured interactions, limiting the scope to transcripts and predefined FAQs for optimal effectiveness.

Clinical Co-pilot PaCo - GenAI Lightspeed Hackathon Project by Medblocks and Clinikk

Challenge in focus: Manual transcription and summarisation of diagnosis and prescription

Physicians frequently face time constraints in thorough note-taking, impeding cross-consultations. In this regard, AI-driven transcription, prompts, and concise summaries could make a significant improvement. However, ensuring the precision of transcription is pivotal, ensuring trustworthy and efficient documentation, effectively tackling time constraints while maintaining rigorous healthcare information standards. The technological integration not only streamlines the process but also amplifies the accessibility of patient records.

Clinical Co-pilot PaCo - GenAI Lightspeed Hackathon Project by Medblocks and Clinikk

Challenge in focus: High cost of claim adjudication

The problem at hand revolves around the cost of claim adjudication, which often surpasses the economic benefits derived from providing coverage. AI could potentially mitigate this issue through fraud intelligence, aiming to detect and prevent fraudulent claims efficiently. There is aalso a crucial need to have established guidelines and protocols crucial for ensuring the accuracy and legitimacy of insurance claims. Physicians would play a pivotal role in defining these protocols, leveraging their expertise to determine the criteria for medically necessary and reasonable care. The incorporation of AI in this process could significantly augment the accuracy and efficiency of fraud detection, showcasing the immense potential of technology in revolutionizing insurance claim management.

Hackathon project; contributor: Anukriti Chaudhari, Surabhi Suman

The diverse applications of AI in the healthcare sector of India represent a significant leap towards revolutionizing patient care and administrative processes. Through targeted applications, such as bolstering health literacy, enabling early disease detection, and providing invaluable support to doctors in documentation, AI is catalyzing a transformative shift. With continued innovation and thoughtful implementation, we stand on the cusp of a brighter, more inclusive future for healthcare in India, where AI plays a pivotal role in enhancing the well-being of billions.