There is a moment in every hype cycle when peak enthusiasm for a new technology gives way to crippling doubt. Technology analysts call this period the “trough of disillusionment,” and it is marked by widespread mistrust of a technology that has overpromised and underdelivered. It’s an apt description of the initial response to AI in healthcare. Originally touted as the panacea for everything that ails healthcare, AI’s real use cases have been decidedly more focused on behind-the-scenes advances in research, analytics, and drug development.

That is not to say that the impacts have been insignificant. AI is revolutionizing how clinical trials are designed, transforming drug development, and redefining commercial strategies, but it is doing so in a methodical, deliberate way as Life Sciences teams tweak their workflows and improve their processes over time. It’s a phenomenon that Sid Jain, Vice President, Global Development Data Science & Digital Health at Johnson & Johnson, compared to a Planet Fitness advertisement in a conversation with Komodo Health CEO Arif Nathoo at our One Day Summit.

“This guy gets on a treadmill, runs, then gets off to weigh himself; shakes his head and gets back on the treadmill, then jumps off to weigh himself again. When you try to measure these outcomes in such a short time, you’ll always be disappointed,” he explained. “Healthcare is complex by nature; much of what is captured is not easily codified, and it takes time to see results. But, when you think back to the early 2000s and all the paper records we had at the time — all the changes that have happened over the last 10 to 15 years enabled companies to digitize data in a way that was not possible [before]. When you take that longer-term horizon, you see that things just keep getting better.”

Jain should know. Charged with harnessing the power of data science and digital health technologies to transform drug development at Johnson & Johnson, he and his team have developed the algorithms that power breakthrough site selection and design strategies; they have been at the forefront of using AI and real-world evidence (RWE) to transform healthcare for the past 20 years. Jain explained how, even though his team has experienced significant efficiency gains thanks to AI and improved data and analytics capabilities, they’re still taking the long-term view.

“Two years ago, we were asking a question, and we didn’t even have the data in one place to be able to answer those questions,” he said. “Now, we have that data in one place, but it still takes a week or two to answer the question. Soon, we’ll be able to get those answers in two hours or two minutes — I think that’s definitely possible. But the first step to any of that was having the right data, clean data, in one place. And I think we have the foundation to do that now.”

Jain explained how, when digging into the underlying data, his team spends the bulk of its time trying to bring in multiple points of view, such as social determinants of health, political data, claims data, EHR data, labs data, and more, and then reconciling those insights across global teams so that everyone is working from the same source of truth. “It’s really all about enabling our data scientists and analysts with better tools,” he said. “These are very smart people; they should be spending their time on higher-order tasks, not spending days cleaning data or reconciling different datasets before they can even use them to start extracting insights.”

That concept of getting multiple different team members with multiple different remits to all work from a common data master is one that resonated for Nathoo, who shared his own challenges with coding in the early days of Komodo Health. “I’m a doctor-turned-businessperson, not an engineer. I was trying to build algorithms and learn Python on the fly by Googling things, and I realized I was spending all of my time on syntax. And my team members still made fun of my code,” Nathoo shared. “I think about a world where I no longer have to worry about that syntax, where I can just generate an algorithm and adjust it. What you’re describing is the notion of helping people by letting them focus on higher-order thinking, and I really love that point.”

That democratization of information and analytics capabilities is perhaps the most promising aspect of AI in healthcare. By giving everyone the ability to see the entire patient journey and incorporate all aspects of that journey into advanced analytics, it will soon be possible to create truly personalized treatments faster than ever.

“One day, I’m hoping everyone has all of the data,” Jain said. “Patients will be able to have very personalized treatment plans or recognize risk factors that they can align their lives around. The data is there today, but it’s still not accessible to everyone. This is all about health equity, where everyone has access to the same level of care no matter where they live or what kind of access they have. We’re getting closer to that goal every day.”

For more information about how Komodo is revolutionizing the use of RWE in healthcare, check out our latest piece, “Real-World Patient Data Enters the Third Dimension."

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