Insights
Why AI and trust are the non-negotiables for healthcare's next century
Krishna Sudheendra, Chief Executive Officer, UST
The U.S. invests heavily in healthcare, spending nearly twice as much per capita as other developed countries, which creates a powerful opportunity for transformation.
Krishna Sudheendra, Chief Executive Officer, UST
The First Annual UST Health Summit was not just an event; it was a rallying cry. Over two intensive days, the brightest minds from the payer, provider, pharma, and technology sectors convened with a singular, urgent purpose: to confront the profound chasm between healthcare's cost and its outcomes.
The U.S. invests heavily in healthcare, spending nearly twice as much per capita as other developed countries, which creates a powerful opportunity for transformation. By focusing on improving life expectancy and reducing avoidable mortality, we can unlock the full potential of this investment and move toward a more sustainable and resilient healthcare system that delivers better outcomes for all.
For too long, healthcare has been content with incremental automation—a digitized form, a slightly faster process—while other industries have been utterly re-imagined. Where is the healthcare equivalent of an Uber, a service defined by transparency, simplicity, and immediate user value? It hasn't arrived because the underlying process hasn't fundamentally changed in 100 years.
My key takeaway from the Summit is clear: The industry is poised for a "reset and rewire," and AI is the force multiplier that makes this transformation possible. But AI's potential is a mirage without a foundation of secure data and absolute trust.
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AI: The orchestrator of a new care model
The sessions on AI and Data in Healthcare confirmed that the time for mere piloting is over; it's time to operationalize. We acknowledge the skepticism around Agentic AI—the concerns about job displacement—but we must recognize it for what it is: the driver of evolutionary industry change. AI is not here to cut costs; it's here to liberate human effort and fundamentally redesign the care delivery process.
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The hybrid model of care
A looming crisis drives the necessity of AI. The U.S. faces an acute shortage of primary care doctors, with utilization significantly lower than in other developed nations. This access issue contributes directly to our high overall costs, as patients resort to unnecessary, expensive emergency room visits.
The solution debated at length is a hybrid model: AI-enabled digital care seamlessly integrated with traditional care. AI isn't replacing doctors; it's optimizing the system so doctors can focus on complex, meaningful work. AI will enable primary care to become a proactive, wellness-focused function, shifting our focus away from reactive disease management.
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The challenge of fragmented ecosystems
We cannot realize this vision, however, if our data infrastructure remains stuck in the 20th century. The healthcare system lags due to a combination of legacy systems, lack of interoperability, and deeply fragmented data ecosystems. Many promising AI pilots have failed to scale precisely because the backend data—often residing in siloed systems, Excel files, or even paper cabinets—simply isn't ready.
To unlock AI's potential, we must prioritize data integration projects. This means balancing investments between the immediate value of "agentic islands" (isolated automation) and the longer-term necessity of foundational "systems of execution"—robust cloud migration and data lake projects that take years to build. We must also manage the proliferation of Agentic AI solutions in a harmonized way; a disparity in adoption rates between providers and payers, for instance, can create operational backlogs that undermine the value obtained by the early adopters.
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Trust and transparency: The bedrock of innovation
The most profound constraint on innovation is the erosion of trust—both within healthcare organizations and between them and their patients.
The crisis of patient experience
We also looked at the state of patient experience. Despite the convenience of online booking, timely access to care remains a significant challenge. We heard countless anecdotes of fundamental system failures: unknowingly visiting out-of-network providers, receiving surprise bills for services rendered years prior, and struggling to navigate a complexity that even industry insiders find opaque.
This lack of transparency is unacceptable. If the transportation sector can provide you with real-time location and cost information for your ride, the healthcare industry must offer clear, upfront information on costs and network status. The solution necessitates a comprehensive rethinking of the healthcare experience, centered on four key attributes: access, cost, health outcomes, and equity. Current satisfaction surveys often fail because they fail to track or address these core elements. True health equity means that a patient's race or socioeconomic status does not determine access, affordability, or outcomes.
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The power of confidential computing
The central obstacle to patient experience and organizational collaboration, which is essential for creating comprehensive patient profiles is privacy. To solve this, the Summit introduced confidential computing as a game-changing security mechanism.
Supported by major cloud and hardware vendors (Intel, AMD, NVIDIA, etc.), confidential computing utilizes hardware-based Trusted Execution Enclaves. This ensures that even the cloud vendor hosting the data cannot access the patient data or proprietary AI code being processed within that protected partition. This is the key to secure collaboration, allowing us to pool non-identifiable data for research (like infectious disease tracking) and for building secure, comprehensive patient portfolios necessary for value-based care.
The Mediverse Platform, presented at the Summit, is an example of this vision in action. Built on confidential computing, it unifies fragmented clinical, operational, and patient behavioral data. This unified data model is critical because up to 70% of relevant data is often missing from traditional patient records. Real-world case studies from our partners demonstrated the impact:
- In oncology, digital interventions led to reduced DVT-based readmissions and shorter hospital stays.
- In remote cardiovascular management, medication adherence support resulted in single-digit hospitalization rates.
- In primary care, data insights contributed to a 22% increase in capacity.
These outcomes confirm that combining real-time clinical and patient data securely is the path to better outcomes, improved efficiency, and effective resource allocation.
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The path forward: Transformation circuitry and collective action
The challenge of ROI and program sustainability persists. Many care management and technology programs are discontinued because organizations fail to demonstrate clear value. An overly complex and disconnected diabetes management program, for instance, can overwhelm providers and lead to data overload without providing actionable insights.
We must accept that effective AI in healthcare requires robust, interconnected data systems first. The focus must shift from simply collecting data to making disparate sources interoperable and insightful.
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The four components of transformation
Achieving genuine transformation requires an integrated strategy, a cohesive transformation circuitry:
- Data management & governance: Treating data as a product with a defined lifecycle, transparency, and strong governance.
- Cloud and infrastructure agility: Moving computation to data sources for real-time needs while centralizing data for analytics.
- End-to-end design thinking: Centering the experience on the four attributes of access, cost, outcomes, and equity.
- AI as a scalable intelligence layer: Leveraging AI for information retrieval, process automation (like EOB processing and fraud detection), and predictive insights.
These four elements must be intentionally and cohesively wired together. Any one aspect in isolation will fail to deliver systemic change.
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The role of leadership
This reset demands bold leadership. We must not let the difficulty of governance stifle innovation. Frameworks like the NIST AI RMF and ISO 42001 are necessary. Still, we must contextualize them for our organizations, ensuring human-in-the-loop systems are vital for effective AI deployment and feedback.
Furthermore, we must address the persistent threat of cybersecurity, with 92% of healthcare organizations experiencing attempted cyberattacks, as this directly erodes the trust we are striving to build. Enhancing these defenses is non-negotiable.
The UST Health Summit was a clear articulation of the challenges, but more importantly, a powerful endorsement of the solutions. The history of innovation, from the steam engine to the internet, shows that progress follows the liberation of human time and effort. AI is the next major force multiplier. Our collective capability and will exist to drive this change and transform lives.
Let us commit to moving beyond the "taxi phase" of preparation and into the phase of execution, prioritizing projects that have a broad, scalable impact, and ensuring that every incremental effort contributes to the goal of timely, affordable, and equitable care for all. The mandate is clear: Embrace AI, build trust through security and transparency, and commit to the whole-person care model. The future of health depends on it.
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