CIO Bulletin

Exponential AI: Accelerating AI Adoption in Healthcare

Artificial intelligence (AI) is reshaping technology strategies and investments across industries today. Even though there’s been progress and success with AI, many companies still face big challenges in integrating AI throughout their operations. Additionally, recent legal challenges have introduced more complexity, underlining the vital importance of transparency, explainability, and robust governance in the scaling of AI technologies. This is extremely critical in the healthcare sector, where the effective and ethical deployment of AI is more than a goal; it’s a necessity.

Exponential AI has emerged as a leading force in accelerating AI adoption within healthcare companies. Over the past five years, the company has distinguished itself by investing in AI capabilities that take AI to the heart of every transaction. Their products and solutions are focused on reducing medical and administrative spend, reflecting their deep commitment to impactful and practical healthcare AI.

In our conversation with Nikhil Mendhi, President and COO of Exponential AI, we explore the importance of real-time AI, the need for robust AI governance, the rise of Gen AI, and the practical challenges of implementing AI in healthcare settings.

Q. Thanks for being here today. Today, Exponential AI is synonymous with Healthcare AI. What led you to focus on healthcare AI?

Exponential AI is a result of the founding team’s collective experience of digital transformation and innovation across healthcare.

Healthcare has traditionally been slow to adopt new technology compared to other industries. Slower adoption rates are primarily due to rule-based approaches, antiquated information infrastructure, fast-changing regulation, and market fragmentation.

For AI to really work in healthcare, it’s got to handle both structured and unstructured data and work effortlessly across diverse environments (on-premises and in the cloud), all while staying secure within client firewalls and delivering real-time results. This is what inspired Exponential AI’s Healthcare AI Journey.

Q. That’s fascinating. So, how is Healthcare AI different from AI in other industries?

As you know, healthcare is local — delivered and consumed differently across various populations and geographies. This inherent nature of healthcare challenges the traditional one-solution-fits-all approach.

At Exponential AI, we’ve designed an offering that acknowledges and addresses this unique challenge of healthcare. Domain-specific AI solutions built using our platform can learn and respond to any market-specific behaviors and trends. By combining the probabilistic nature of AI with deterministic rules, we are able to deliver decisions unique to each context.

But what really differentiates us is our capability to deliver these decisions in real-time. We don’t just analyze data and provide recommendations; we convert insights into real-time actions. This maximizes the impact of AI, whether it’s scrutinizing healthcare claims, identifying fraud, optimizing care, or triggering proactive interventions.

Q. What roadblocks did you hit when rolling out AI in healthcare, and how did you tackle them?

Making AI work in healthcare can be a challenge. You encounter not only technical challenges such as data silos, fragmented systems, and unstructured data but also issues in how people understand AI — their limited use of AI as analytics, treating AI like software, and ultimately, dealing with change management.

We strongly believe that unstructured data, combined with probabilistic algorithms and deterministic rules, will lead to better, more consistent, and faster decisions at scale.

ENSO, our platform, has been instrumental in helping us deliver this approach to enterprises. It is a healthcare decision intelligence platform for building, training, orchestrating, and managing AI solutions. It offers a suite of pre-built, pre-trained agents that can be assembled and orchestrated into solutions. These agents adapt to client-specific data, are reusable across various processes, and continually evolve with each decision and piece of data.

Q. But the expansion of AI in healthcare is bringing its fair share of challenges. This is particularly evident in the face of concerns and legal challenges around biases. How does Exponential AI address and mitigate these biases in its AI solutions?

We’ve developed a robust model-management platform that helps our clients manage both intended and unintended biases. This is especially important given that biases can be introduced through client-specific data and the constantly changing regulatory and market environments.

Our platform is engineered to send proactive alerts about patterns indicating potential biases. Looking ahead, we see a big future for platforms that enable the transparency, governance, and security of AI applications.

Q. While we’re talking AI, what’s your take on the rise and role of Generative AI in healthcare?

Absolutely! Generative AI is creating quite a stir, rightfully finding its place on every CEO’s agenda.

Gen AI holds the promise to revolutionize healthcare processes, fundamentally transforming how care is delivered and administered.

Today, Gen AI adoption is still in its early stages. Processes, including prior authorization, provider contract management, benefits management, and member and provider communication, stand to gain substantially from its capabilities. For healthcare organizations, the priority should be on establishing the necessary infrastructure to build, train, integrate, deploy, and maintain Gen AI solutions.

The large language models that are currently available are too generic for healthcare and will require significant training and calibration to be ready for healthcare applications. At Exponential AI, we focus on domain-specific Large Language Models (LLMs) and integrating them with existing workflows and applications.

The future of Gen AI in healthcare is promising, and we’re actively engaged in shaping its potential impact and facilitating its adoption within the sector.

Q. What advice do you have for top executives on integrating AI into their business models?

For enterprise leaders, instead of viewing artificial intelligence (AI) as a tool, we encourage them to think of it as a differentiator. Instead of buying one AI solution at a time, they need to envision and plan a comprehensive, enterprise-wide AI journey. This means taking a hard look at the organization’s approach to data, adaptability of legacy infrastructure and the development and governance of AI solutions.

The first step is to recognize that organization’s operating model must evolve to effectively scale and accelerate AI adoption. This evolution will involve a strategic shift – from merely generating AI-enabled insights to integrating these insights into actions across various enterprise workflows and environments. It’s about creating an ecosystem where AI co-exists with and enhances legacy solutions. This synergy, I think, is key to achieving the desired business outcomes.

Meet the President and Chief Operating Officer

Nikhil Mendhi, the President and COO of Exponential AI, brings over two decades of leadership experience in healthcare technology innovation. As a distinguished figure in healthcare AI, Nikhil is a strong advocate and practitioner of using AI responsibly. He stresses balancing technology with human insight to ensure equity and diminish bias and holds that this equilibrium is essential for AI to positively impact healthcare. His experience and thought leadership are pioneering a healthcare future that is more insightful, transparent, efficient, and accurate. Through his leadership, Exponential AI is leading the charge in accelerating the adoption of AI across healthcare organizations.