Three Lessons on How Healthcare AI Transformation is Taking Shape: Insights from HLTH 2024
Healthcare is on the brink of a seismic transformation, and we’re building at its epicenter. At Team8, our role as investors and company builders is to stay ahead of the curve, crafting the theses that marry our data, tech, and cyber strengths with the industry’s next big waves. HLTH 2024 provided a front-row seat to the shifts already unfolding and, quite satisfyingly, validated much of what we’re betting on. Let’s break down three key takeaways that confirm our direction and set the stage for what’s next.
Lesson 1: AI Automation, Precision & Personalization
The conversation around AI in healthcare has matured. It’s not just about making processes faster or more efficient; it’s about laying the foundation for long-term innovation and precision. But let’s be realistic—while the future of AI is undeniably personalized care, the immediate value lies in automating the repetitive, non-personalized workflows that burden healthcare teams today.
Where are we seeing this? Clinical trials are a prime example. Amgen’s Atoma system, spotlighted at HLTH, has revolutionized site enrollment processes, cutting times by an impressive 50%. This kind of backend automation accelerates critical pathways, getting life-saving treatments to patients faster. It’s practical, impactful, and achievable now.
Personalization, while exciting, is still on the horizon and not what will most dramatically move the needle in the short term. The promise of integrating genomics, behavior, and clinical data into hyper-individualized care plans is immense, but these applications require more time, trust, and refinement to fully scale. For now, the focus should be on the basics – automating and streamlining the back end of non-personalized work flows, building trust in AI systems—ensuring transparency, mitigating bias, and demonstrating tangible value in everyday workflows. By solving the present-day bottlenecks, we pave the way for the personalized future
Lesson 2: Vendor Integration Is Healthcare’s Achilles’ Heel
Let’s talk data. If AI is the brain of healthcare’s future, data is the lifeblood — yet it’s still bleeding out into a million disconnected silos. The frustration was palpable at HLTH: we’ve got oceans of health data from EHRs, wearables, and diagnostics, yet the data remains siloed, and critical patient data is often locked away, inaccessible when it’s most needed.
This isn’t just a tech problem; it’s a patient care problem. If data can’t move seamlessly between platforms, then digital health’s promise falls flat. We can think about how this has been tackled in other sectors. Take the cybersecurity industry, for example. Companies there faced similar challenges with fragmented and unwieldy datasets. The breakthrough came when they figured out how to package and flatten data—transforming complex, multidimensional streams into accessible formats that systems could process and analyze in real time. This approach democratized data use, enabling faster threat detection and response across platforms.
The companies that figure out how to democratize their data are the ones that will define the next decade of healthcare innovation. This isn’t just about efficiency — it’s about unlocking real-time, data-informed care that can change patient outcomes on the spot.
Lesson 3: Trust and Security Are the Bedrock of AI Adoption by Physicians
As AI becomes a cornerstone in healthcare, from diagnostics to clinical decision support, earning the trust of physicians is paramount. For AI to make a meaningful impact, clinicians need to feel confident in the tools they’re using — confident not only in the technology’s outputs but also in how those outputs are generated.
At HLTH 2024, the spotlight was on privacy-preserving technologies like federated learning and differential privacy. These innovations allow AI to train on vast datasets without compromising sensitive information. But for physicians, it’s not just about privacy; it’s about explainability and reliability. Doctors need to understand how AI reaches its conclusions and have confidence that it can consistently deliver accurate, unbiased results.
Trust isn’t built through compliance alone. It requires rigorous testing, transparent methodologies, and a proven track record of reliability in clinical settings. Companies that prioritize explainable, secure, and clinically validated AI are more likely to be embraced by healthcare professionals. Trust is quickly becoming a prerequisite, not a bonus, in this space
The Road Ahead: Connected, Personalized, and Secure
HLTH 2024 underscored that healthcare is in a period of rapid, tech-driven transformation. AI’s evolving role, the persistent challenge of data interoperability, and the absolute necessity of robust security practices point to a future where healthcare is not only more efficient but more responsive and secure. Yet we are just before the inevitable rise of push-back and regulation that will be required to govern these monumental changes at scale. Excelling at the fundamentals will ensure that companies will come out on top when governance creates margins.
