Public health systems are under pressure from rising costs, workforce shortages, increasing demand, and limited resources. Data and AI can help organizations use their resources more effectively, improve care decisions, and achieve better outcomes.
But the next phase of AI in healthcare will depend on more than what the technology can do. It will depend on whether health leaders, doctors, regulators and patients can trust it.
This was the main topic of the webinar I moderated with Christian Hardal, EMEA Healthcare Industry Leader at SAS and Saad Rais, Senior Director of Health Data Sciences at the Ontario Ministry of Health. What emerged was a frank and practical conversation about where healthcare organizations stand on their AI journey and what it takes to move forward with confidence.
The real obstacle is not technology
The challenge facing healthcare organizations is not just about adopting new technology. The real opportunity lies in strengthening data foundations, improving data quality, and getting the fundamentals right before AI can have a meaningful impact.
It’s not an AI problem. It is a system issue or a data quality issue. Christian Hardal, Healthcare Sector Leader EMEA, SAS
Across the world, digital maturity varies widely, not only between countries but also within the same health region. A fragmented data landscape, lack of interoperability, and workflows not designed for data-driven decision making are all slowing progress. AI is being explored and adopted, but innovation occurs in small pockets, and the journey from pilots to real-world deployment remains slow.
Hardal also pointed to the broader preparedness gap. in HIMSS Europe 2026, the newly released State of Healthcare, AI and Digital Transformation report It revealed that in the US, only 7% of organizations have a mature AI governance strategy, and only 21% of leaders believe their data is being fully leveraged.
The meaning is clear. Before organizations can scale AI, they need to get the fundamentals right, strengthen their data foundations, improve interoperability and then integrate AI and analytics into their daily decision-making process.
From “Can we build it?” to “Can we trust him?”
Even in places where AI is being deployed successfully, healthcare leaders are asking different questions about AI than they were just a few years ago.
We’ve gone from asking, “Can we build it?” to “Can we trust him?” Christian Hardal, Healthcare Sector Leader EMEA, SAS
This shift reflects a growing recognition of the importance of trust in AI for health leaders. Trust, grounded in governance, transparency and data quality, is the foundation for safely and effectively scaling AI. It should be designed from the beginning, not bolted on after sawing.
Christian outlined some simple but clear questions that organizations should be able to answer:
- Can you identify all of your AI algorithms and who is responsible for each?
- Can you test algorithms on updated data in a controlled, automated process?
- Have you evaluated your models for bias and is this bias at an acceptable level?
- Do you know how your data was created and where it originates from?
- Do you have a governance model and quality assurance board?
The regulatory landscape adds further urgency. With regulations like EU law on artificial intelligencethe European Health Data Space (EHDS), General Data Protection Regulation and HIPAA ESG and auditability are no longer optional. They are strategic imperatives. C-level leaders should be responsible for AI.
It is encouraging that leading organizations are already taking this seriously. SAS is involved in Responsible and ethical artificial intelligence in the healthcare laboratory (REAiHL), a collaboration between Erasmus Medical Centre, Delft University of Technology and SAS, and supported by the World Health Organization.
As expectations from regulators, clinicians, and patients grow, health systems must ensure that trust is built at every stage of AI deployment, rather than later.
Artificial Intelligence in Action: Real Results from Ontario
The Ontario Ministry of Health provides a compelling example of what trustworthy analytics and AI at scale can look like. Advanced analytics and artificial intelligence are embedded throughout the system to support policy decisions, operational efficiency and health outcomes. All of this is supported by strong data management and quality controls.
One notable example is AI-based image recognition applied to ultrasound invoice validation. What was previously a time-consuming, manual process has been transformed:
What used to take four weeks manually can now be done in less than 30 minutes with near-perfect accuracy. Saad Rice, Senior Director, Health Data Sciences, Ontario Ministry of Health
This kind of efficiency gain goes beyond speed. It frees up clinical and administrative capabilities, reduces error rates and allows teams to focus on higher value activities.
Forecasting is another crucial area. Predictive modeling is used in Ontario to:
- Predicting and monitoring diseases such as COVID-19, influenza, and respiratory syncytial virus.
- Identify patients at risk of adverse outcomes and predict high-cost users.
- Forecast service demand and capacity pressures.
- Support emergency management and public health response to outbreaks.
By identifying at-risk patients early, we can improve outcomes and avoid unnecessary cost pressures on the system. Saad Rice, Senior Director, Health Data Sciences, Ontario Ministry of Health
Ontario’s experience demonstrates what can be achieved when data quality, governance and analytical capacity are treated as foundational investments rather than afterthoughts.
Emerging technologies: RAG, generative AI, and digital twins
The conversation also explored several technologies that are quickly moving from experimentation to practical application.
Recovery Augmented Generation (RAG) This approach has emerged as a particularly promising approach in health care, precisely because it addresses the problem of trust directly.
Rather than relying on general-purpose models trained on external data, RAG relies on the knowledge base of the examined organization, to generate citation-supported, source-based responses. This eliminates the risk of hallucinations, ensures that output is traceable and auditable, and means that sensitive data remains internal.
In Ontario, generative AI is already being used to interpret unstructured clinical notes from laboratory technicians, turning previously inaccessible data into actionable insights.
Digital twins It is also generating increasing interest. SAS has partnered with Epic Games To create powerful, realistic 3D visualizations that allow healthcare teams to design, test and improve real-world scenarios.
In Denmark, SAS is working with a central sterilization service in the capital region to operate a 1:1 digital twin of an entire sterile processing facility, enabling real-time operational optimization and “what-if” scenario planning, including improved routing for autonomous guided vehicles.
Trust is the foundation and the future
As healthcare organizations continue to modernize, the direction of travel is clear: toward interoperable health systems that use trustworthy data and artificial intelligence to enable more personalized, preventive, and equitable care.
In the short term, this means expanding proven use cases, improving governance, and bridging the gap between innovation and deployment.
But getting there requires thoughtful investment in the foundations. Human oversight and governance remain essential at all times, and governance enables organizations to scale innovation responsibly. The organizations that move forward most effectively are those that prioritize three things:
- Interoperability – Break down data silos and ensure information flows where it is needed
- Governance – Build accountable and transparent structures for data and AI governance
- Trustworthy AI – Design explainability, auditability, and ethical safeguards into every stage of deployment
Because when data and AI are used in the right way, they not only improve efficiency, they help create health systems that are more responsive, more sustainable, and ultimately more effective for the populations they serve.
