Introduction

In the last few years, India’s healthcare sector has become one of the largest both in terms of revenue and employment. This growth is expected to reach USD 372 billion by 2022 with a CAGR of 16%. This immense growth has been possible due to the increasing income levels, ageing population, growing health awareness and changing attitude towards preventive healthcare. Given this background, the report highlights the ways through which AI solutions can break the classic healthcare iron triangle and provide quality care with improved accessibility and reduced cost. It also showcases the AI enablement ecosystem in healthcare and lays down strategic ways for healthcare organizations to accelerate their AI adoption journey.

Delving deeper, we find that the report specifically explores the following three questions: a) where should healthcare start their AI journey, b) what are the provider and healthcare organization side dynamics in healthcare AI ecosystem and c) how can healthcare organization move up the AI value curve. While it is true that despite its scale and growth, the Indian healthcare system still grapples with several challenges which have been further stretched by the ongoing pandemic, the good news is that an increasing number of healthcare organizations are turning towards AI to improve accessibility of services. 

The report points out the fact that healthcare organizations are leveraging AI to digitally transform and break the iron triangle of healthcare by simultaneously reducing cost, enhancing quality and improving availability of services. This has been possible because AI solutions has the potential to transform the iron triangle components into fluid parameters in which addressing one factor does not negatively impact the other. Some of the areas where AI implementation can be channelized are patient care and experience (personalized treatment, smart wearable sensors, patient assistance chatbot, one touch patient registration, prognosis assistant), operations (robotic assisted surgery, medical error avoidance, hospital patient flow, appointment assistant) and R&D (drug development, disease simulation).

However, on road to achieve this desired goal, healthcare organizations need providers for robust platforms and solutions along with right capabilities and resources for AI implementations. One way is to promote quality healthcare AI start-ups who offer point solutions or partner with service providers who are betting big on platform-based approach. 

Relevance of the Report

Unlike most reports, we find that there is much emphasis on focusing on solving real business problems and partnering with organizations based on the latter’s priorities, capabilities and mutual value creation opportunities. It also does not shy away from promoting long-term relationship as well as define risk and governance framework for collaboration. Most importantly, the report analysed the survey findings conducted across Indian enterprises, GCCs and large start-ups to gauge the evolution of healthcare organizations and their position in the AI journey. The insights reveal some interesting facts such as AI is not just a hype anymore and that trust is a key lynchpin in its adoption. However, the good part is that modern infrastructure is seen as a key enabler in accelerating AI implementations at scale. Above all, the report gives an encompassing view of the AI-backed ecosystem in healthcare along with a clear-cut guidance as to how healthcare organizations can move up the AI value curve. 

Key Takeaways

1. The fast-growing healthcare system in India is increasingly turning towards AI to deliver quality service. This is mainly due to the fact that the healthcare system is already suffering from limited skilled manpower and a low doctor-patient ratio.
2. Healthcare organizations are turning towards technology providers who are building product-as-a-service and offering enterprise grade AI platform with verticalized solutions.
3. Across all sectors, dedicated AI strategy and budget is a key imperative to scale AI initiatives organization wide.
4. While much of AI deployment is hindered due to low digitization / legacy systems and low ecosystem maturity and lack of skilled talent, implementation is marred by data security and brand reputation.