From early diagnosis to faster clinical integration, Maneesh Goyal discusses building a continuous learning ecosystem that improves outcomes worldwide.
Artificial intelligence has moved beyond experimentation in healthcare, yet sustainable return on investment remains elusive for many organisations. Success depends less on algorithms alone and more on how health systems redesign clinical and operational processes to embed intelligence into everyday care.
Maneesh Goyal, Chief Operating Officer, Mayo Clinic Platform, outlines why process reengineering, disciplined measurement, and global collaboration are critical to unlocking measurable value.
Goyal discusses how federated data networks, independent clinical validation, and pre-integrated solutions reduce risk while accelerating deployment across diverse healthcare environments.
From redefining ROI metrics and overcoming adoption barriers to safeguarding patient privacy and supporting digital-first strategies in markets such as the UAE, Goyal shares how a collaborative, continuously learning ecosystem can transform clinical decision-making worldwide.
Interview excerpts:
From your vantage point, what separates AI pilots that deliver real clinical and operational ROI from those that fail to move beyond experimentation?
AI pilots succeed when organisations rethink how care is delivered rather than simply layering technology onto existing processes. If you put AI on top of a manual or outdated workflow, it usually increases cost instead of creating value. What is required is clinical or business process reengineering. For example, if AI can predict surgical complexity in advance, the scheduling process must be redesigned to allocate the right operating room time. If that change is made, throughput improves and wasted time is reduced, which creates measurable ROI. At Mayo Clinic, we focus on two outcomes. Either the technology fundamentally changes a clinical or operational process, or it becomes part of the standard way care is delivered, like an MRI machine or a stethoscope. Many AI projects fail because organisations expect the technology to be a magic solution without transforming the underlying processes. We currently run more than 320 algorithms that diagnose or predict conditions continuously. Some of these help identify diseases in patients who have no symptoms yet.
“Moving from symptom-based care to early intervention changes outcomes and creates real value.”
How are leading health systems defining and measuring impact when evaluating AI in digital health?
Each solution must be evaluated against a clearly defined objective. That objective could be clinical outcomes, financial performance, operational efficiency, or patient access. Healthcare organisations need to decide which lever they are trying to pull and then measure success against that specific metric. There is no single universal measure for AI. The key is clarity on the intended outcome and disciplined tracking of results.
What are the biggest adoption barriers healthcare providers face when scaling AI, and how can platforms like Mayo Clinic Platform reduce risk and build trust?
One of the biggest risks is repeating mistakes from other industries, such as pharmaceuticals, where solutions are tested on narrow patient populations before being deployed globally. Many AI tools are developed using limited data sets, often from a single geography. When applied to diverse populations, they may not perform as expected. Our approach is to build a global, federated data network from the start. Today, the Mayo Clinic Platform includes more than 55 million patient lives across multiple continents. Solutions developed on this broader data set are more likely to work across different populations and care models. We also run a qualification process where our clinical teams evaluate each solution’s claims. We compare what vendors promise with what the data actually shows, and we produce a clinical report. This independent validation reduces risk for healthcare providers. Finally, we pre-integrate validated solutions into the platform. This allows hospitals, whether small or large, to deploy tools quickly without heavy integration costs, enabling faster and safer adoption.
How is Mayo Clinic Platform operationalising data and AI to deliver measurable outcomes for global hospital partners?
Mayo Clinic invests more than a billion dollars annually in research. Within the platform ecosystem, more than 150 companies are also building solutions, representing a combined investment that likely exceeds $10 billion. By running these solutions against a global data set, we have reduced the time from idea to clinical integration from about three years to nine months. Once a solution is validated, it can be deployed across partner hospitals worldwide. For example, we have developed cardiology algorithms that enable earlier diagnosis and made them available to partners in countries such as Nigeria. This approach allows patients to benefit from high-quality clinical insights regardless of where they receive care. Instead of waiting a decade for research to translate into clinical practice, validated insights can be embedded directly into clinical workflows, improving outcomes much faster.
How will data-driven clinical intelligence reshape decision-making and benchmarking across global health systems?
The shift will be towards collaborative, continuous learning systems. The Mayo Clinic Platform is designed not as a technology product, but as a shared learning environment. A hypothesis may start at Mayo Clinic and be validated locally. It is then tested across global partner institutions. As the model is deployed in different geographies, it continues to learn and improve. That feedback loop benefits all participants. This is not a top-down model where one institution dictates best practices. It is a collaborative ecosystem where every partner contributes data, insights, and improvements, creating a collective intelligence that raises the quality of care globally.
What is your perspective on the UAE’s digital-first healthcare strategy?
The UAE is starting from a strong position because it is not constrained by legacy infrastructure, as many other countries are. The nation’s focus on large-scale genomic sequencing and digital health records creates the foundational data sets required for AI-driven healthcare. These components enable knowledge generation, better disease understanding, and more efficient service delivery. Globally, healthcare systems face a supply-and-demand imbalance as populations age and require more care. Digital-first strategies, like those in the UAE, can help distribute knowledge and services more evenly, improving access and outcomes.
How does the Mayo Clinic Platform address patient data privacy in global collaborations?
Our approach is based on the principle of “data behind glass.” Patient data never leaves the institution that owns it. Each partner retains its data within its own environment and under its own regulatory controls. Instead of moving data, we send questions to the data. The results returned are aggregated and de-identified, ensuring compliance with regulations such as GDPR (General Data Protection Regulation). Patients are consented into the model, and if they withdraw consent, their data can be removed immediately. This approach minimises risk and ensures that institutions, regulators, and patients retain full control over their data. It is a collaborative model designed to enable global learning without compromising privacy or regulatory compliance.
