Last week, ViVE 2024 brought together digital health decision makers from across the country with top thought leaders in the healthcare technology space. Unsurprisingly, AI had a significant presence over the three-day conference, with speaker sessions shifting noticeably from last year’s more abstract, theoretical—and, yes, unproven—discussions of AI and its applications, toward more concrete use cases for AI tools and technologies in the healthcare space.
While this year’s agenda signaled an important shift in the GenAI conversation, aligning it more closely with tangible patient and payer outcomes, less clear was the path for data leaders to bridge the gap between these exciting new technologies and their existing data stacks.
In this blog, we will share some of the exciting AI takeaways from ViVE 2024. With support from Hakkoda’s forthcoming State of Data Healthcare report, we will also highlight some of the major barriers healthcare organizations face when it comes to successful AI implementation.
ViVE Was Crammed Full of Concrete AI Use Cases
The ViVE 2024 agenda overflowed with practical use cases and applications backed by artificial intelligence, running a wide gamut across the patient lifecycle. From revolutionizing oncology, to reshaping the surgical theater, to transforming payment models, the show floor was abuzz with focused, concrete applications for GenAI models.
This gels with major findings from Hakkoda’s forthcoming healthcare report, to be released next week, which found that healthcare organizations were more likely to have urgent and defined use cases for their data as a whole. According to the report, 34% of healthcare organizations also strongly agree that they have defined use cases for GenAI that they are ready for implementation.
Speaker sessions at ViVE also closely aligned AI use cases with the empowerment of medical professionals. “For providers, there’s now an ability to operate at the top of their license,” Shiv Rao, CEO and Founder of Abridge, poignantly put it during a talk titled “Generative AI: The Provider, The Entrepreneur, The Inventor and The Practitioner.”
With GenAI still emerging from its relative nascency, the most useful applications are those where the stakes are low and the rates of occurrence are high—in other words, where technology can help eliminate the “busy work” that stands between human professionals and their mission-critical work.
Leveraging AI in a supporting role that automates cumbersome secondary processes means that surgeons can focus on performing life-saving procedures, that doctors and nurses can focus on their patients because their notes are being automatically captured, and that revenue cycle managers can focus on solving the big challenges like reducing the number of claims denials.
Put simply, AI is at its most transformative when it directly equips human professionals with the ability to perform their duties better.
Life After ViVE: Closing the Data Maturity Gap
Buried in the frenzy of disruptive GenAI applications displayed on the show floor, however, one important question went all but unaddressed: how do healthcare organizations begin to implement state-of-the-art AI tools and technologies into their existing data stacks?
The unfortunate reality for many organizations is that there’s a lot of work to be done before they’re ready to start building and deploying their own AI models. According to the State of Data, just 23% of healthcare organizations had centralized their data on a primary cloud platform in 2023, putting them at a disadvantage compared to other industries when it comes to modernizing their data stacks.
42% of healthcare leaders also reported that integrating data across multiple silos was a major challenge for their organization, and another 44% reported that they struggled with data quality and governance measures. In short, it is virtually impossible to develop a strategy for validating and monitoring AI when organizations lack a strategy for validating and monitoring their existing data and analytics. Furthermore, AI models trained on low quality data are famously prone to hallucinated outputs and low overall reliability.
To capitalize on the wealth of emerging AI developments, then, most healthcare organizations must first climb their way out of a state of data chaos.
Hakkōda’s Data Innovation Journey: A Roadmap to AI Success
ViVE 2024 breathed new life into the conversation about how AI can be a transformative force in healthcare. With new and innovative GenAI use cases emerging almost daily, the rush to develop and deploy new models isn’t likely to slow down anytime soon.
For organizations still in early stages of data maturity, this proliferation of AI use cases provokes a mixture of excitement and anxiety. They understand that AI interventions can open a path to improved patient outcomes and stronger revenue, but don’t know how to get there.
Hakkoda’s Data Innovation Journey is a pragmatic framework built to help organizations address exactly this kind of uncertainty: identifying exactly where they stand along the road to data maturity and providing them with a clear roadmap to achieving their data goals.
Our industry experts have years of experience working for major payer and provider organizations, and bring together a deep knowledge of industry-specific challenges with a robust understanding of the modern data stack. We have helped healthcare clients at every stage reach their data goals, from migrating to Snowflake off of a legacy data platform, to accelerating the development of machine learning models capable of predicting cancer, to leveraging image classification to detect disease.
To learn more about the Data Innovation Journey and how Hakkoda’s data experts can help your organization make its game-changing AI use case a reality, contact us today. You can also visit us at the Snowflake both at HIMSS 2024 in Orlando, where members of our healthcare team will present “Data Chaos to Innovation: Making the AI Journey a Reality” on March 14th at 2:00 pm.
