Artificial intelligence in the healthcare field is full of promise but still under-adopted, according to Chirag Shah, partner at Define Ventures.
Last month, the venture capital firm published its AI thesis, arguing that the healthcare industry must start moving beyond narrow use cases for AI and embrace more workflow-integrated platforms in order to achieve lasting impact. A convergence of factors — ongoing money problems, rapid technical advances and growing readiness among healthcare customers — is giving way to what Define calls a “once-in-a-generation moment” in which the best workflow-integrated AI startups can transform how care is delivered, paid for and experienced.
Define uses the “house of healthcare” as a framework for understanding healthcare innovation. This includes the front door, where patients first interact with the system; the foundation, made up of data and infrastructure; and the rooms, representing care delivery.
When it comes to the front door, AI can make outreach and engagement more personalized by combining clinical and personal data. Innovation for the foundation has historically centered on digitization and aggregation — with AI, healthcare organizations are turning that data into insight, Shah explained.
As for the rooms, AI is already starting to offload administrative tasks such as charting, documentation and messaging so providers can focus more on their patients, he said.
Define’s portfolio companies span all areas of the house, Shah stated. One of these startups is Luminai, which uses AI to automate routine tasks like patient intake, eligibility checks and documentation, freeing up healthcare staff to focus on direct patient care. Another is Layer Health, which sells an AI engine to quickly abstract and organize clinical data from charts.
There’s a lot to be excited about in terms of the future of AI in the field, Shah noted, saying that the technology is still in the early stages of demonstrating its full potential.
As innovation continues, he believes the most successful AI startups will be the ones that are able to integrate quickly into provider, payer and pharma workflows without creating any extra burden.
Shah added that while it’s easier than ever to build a point solution, it is much wiser for startups to expand into second, third and fourth use cases with customers, evolving their tools from wedges into platforms. As he sees it, companies that only solve one narrow pain point risk being displaced.
Portfolio company Cohere Health is a good example of a startup that expanded the capabilities of its AI. The company began with prior authorization in musculoskeletal care and then expanded into oncology, cardiology, drugs and software-based models, Shah explained.
“In the world of AI, when everybody else can move just as fast, if not faster, than you can, one of the mistakes that we see is that people haven’t done enough of the customer discovery work to understand what’s going to come next. After that wedge, what else are your customers going to need? At some point, the competition is going to come in, and the last thing you want is for your wedge to be your only product. We think it’s really important to be building that — your product development cycles have to get really accelerated now, especially as compared to prior years,” he remarked.
From his perspective as a digital health investor, Shah thinks the key to success in healthcare AI lies not in simply developing a strong product — startups need to expand beyond their initial use cases and move faster than the competition.
Photo: z_wei, Getty Images
