Generative AI’s rapid evolution is reshaping healthcare partnerships and contracting models. Vendors are moving beyond technology provision to act as strategic partners, supporting organizations to navigate the complex compliance frameworks and creating tailored solutions that enhance operational, financial, and clinical workflows.
By partnering with AI-driven tech innovators, healthcare organizations are leveraging targeted solutions that require minimal change management while delivering near-term returns such as improving clinical documentation, automating prior authorization, and enhancing patient/consumer engagement. Together, these stakeholders are driving advancements in both clinical and non-clinical workflows to align with dimensions of the quintuple aim of improving healthcare.
Frost & Sullivan’s Healthcare IT Webinar Series recently hosted an in-depth session titled, “Large Language Models and Generative AI in Healthcare: Exploring Best Practices & Competitive Positioning.” Insights from industry leaders and experts provided valuable perspectives that are helping shape the future of healthcare AI.
Featured Experts:
- Greg Caressi, Growth Coach & Senior Vice President, Frost & Sullivan
- Nitin Manocha, Growth Expert & Senior Industry Analyst, Frost & Sullivan
- Rom Eizenberg, Chief Revenue Officer, Kontakt.io
- Alex MacLeod, Director, Healthcare Solution Innovation Development, InterSystems
- Nirmal Ranganathan, VP of Engineering – Artificial Intelligence, Rackspace Technology
Note: Click here to access the recorded session and gain valuable insights from these experts.
Key Takeaways from the Discussion:
- Emergence of GenAI v2.0 in Healthcare: While the initial phase of generative AI (GenAI v1.0) has primarily improved administrative workflows, the industry is on the brink of GenAI v2.0. This new phase will utilize AI’s analytical and deductive capabilities, creating a powerful tool that augments workforce productivity and supports clinical decision-making.
- Importance of Localized Language Models: Publicly accessible language models are effective for quick-launch products that don’t directly impact patient health. However, the industry seeks localized models trained on regional data for improved accuracy, greater adoption, and user trust. Transparency around the data sources and lineage also enhances solution credibility.
- Managing AI Hallucinations in Clinical Use: While hallucinations are an inherent part of AI, controlling them is critical for clinical applications. Some vendors are tackling this challenge by layering language models, ensuring that insights are validated through multiple checks. In some cases, different LLMs are employed within various solution components to bolster content reliability.
- Defining Liability Across Multi-Party Relationships: In healthcare’s complex AI ecosystem, which includes LLM developers, health IT solution providers, and end users, clarifying each party’s responsibilities and liability is crucial. This clarity is especially important for AI solutions intended for clinical or patient-facing applications.
- A Surge in GenAI Adoption: Healthcare has embraced GenAI at an impressive rate, showing an openness unmatched with previous technologies. As adoption accelerates in the coming year, the industry anticipates even broader and more sophisticated applications that will likely require forward-thinking strategies from payors, providers, and vendors alike.
Is your organization exploring new technology partnerships and AI-driven business models to capitalize on GenAI’s transformative potential in healthcare?
Generative AI is paving the way for a more efficient, responsive, and innovative healthcare system, though its adoption must be balanced with vigilance over data privacy and solution transparency. By prioritizing targeted use cases and fostering collaboration across the ecosystem, healthcare organizations can effectively integrate AI to meet clinical, operational, and financial goals. As the technology matures and moves toward GenAI v2.0, the potential for enhanced patient care and streamlined processes is immense. Healthcare leaders who stay ahead of these advancements will be well-positioned to harness AI’s full capabilities and drive significant value for patients, providers, and payors alike.
“Demystifying AI is crucial because so many people understand it differently. Today, AI isn’t just about language generation or machine learning—it’s a powerful assistant that can take notes, make deductions, and streamline tasks that are often burdensome. This technology is crossing boundaries, from enhancing healthcare workflows to passing the Turing test, blending consumer and enterprise innovation in ways that are reshaping industries. The goal? To empower teams, from engineers to healthcare providers, with a tool that transforms how we work, think, and care for others.”
Rom Eizenberg, Chief Revenue Officer, Kontakt.io
“Demystifying AI is crucial because so many people understand it differently. Today, AI isn’t just about language generation or machine learning—it’s a powerful assistant that can take notes, make deductions, and streamline tasks that are often burdensome. This technology is crossing boundaries, from enhancing healthcare workflows to passing the Turing test, blending consumer and enterprise innovation in ways that are reshaping industries. The goal? To empower teams, from engineers to healthcare providers, with a tool that transforms how we work, think, and care for others.”
