In today’s data-driven healthcare environment, the question is no longer whether hospitals should adopt Data and AI, but how to do so in a way that improves patient outcomes, clinician efficiency, and system sustainability. Hospitals are under pressure to deliver better care with limited resources while meeting strict privacy and compliance regulations. A strategic approach to Data & AI helps bridge operational efficiency and quality of care.

However, the journey is filled with challenges: fragmented data, legacy system lock-in, and ethical complexities specific to the hospital and life science sector. Overcoming these hurdles requires a disciplined, strategic approach, and likely a third party that has industry expertise and an understanding of internal and government regulations. In North America, many health systems are also navigating cloud adoption, data residency, and patient trust challenges - reinforcing the need for a structured, ethical Data & AI roadmap

Moving from ad-hoc experiments to scalable, reliable AI starts with establishing foundational practices across your technology, people, and governance structures. These practices ensure that every investment moves the organization closer to truly intelligent and equitable care.

If you’re looking to get started with AI or know you can make more out of your data, these are the five best practices for hospitals looking to build a data-driven and AI- AI-enhanced ecosystem.

1. Create a Unified, Strategic AI Governance Committee

One of the greatest risks in healthcare AI is the lack of clear accountability and ethical oversight. The best practice is to move governance out of the IT silo and into a centralized, cross-functional body.

When you get started on your transformative data and AI journey, the first best practice to follow is forming a data and AI governance committee. This should report to executive leadership and be comprised of a variety of roles such as clinical leaders, data scientists, legal counsel, and ethics/compliance officers. AI should be seen as an extension of clinical and operational data governance, subject to the same standards of safety, privacy, and accountability applied to all clinical systems.

Think of this group as the beacon that guides your data and AI journey. They’ll be responsible for establishing a formal AI data handling policy, setting the threshold for algorithmic bias acceptability, and approving every AI model before it moves from pilot to clinical use. This structure ensures that ethical principles (like fairness and transparency) are technical requirements, not afterthoughts. It is a crucial step to mitigate the legal and reputational risks associated with AI deployment.

This step aligns with HIMSS AMAM Stages 2 and 3, where analytics governance becomes formalized and integrated into operational workflows.

2. Prioritize Interoperability Across Systems

“Recent data indicate that fewer than one in three hospitals are able to electronically find, send, receive, and integrate patient information from another provider” (source). The challenge of data fragmentation and siloes can only be solved by committing to modern standards that enforce data liquidity.

There are various hospital-focused data strategies you can follow, such as FHIR-First (Fast Healthcare Interoperability Resources-First). This is the standard that enables disconnected systems to “speak” the same data language. A good way to start with this practice is to begin systematically migrating critical data layers (such as patient demographics, lab results, and medication lists) into a centralized, cloud-based platform (Data Lake or Warehouse).

The coexistence of legacy and modern tools complicates interoperability; standardized integration and hybrid data architectures are essential for building a unified data analytics and reporting platform. This foundational step not only helps overcome the interoperability barriers of legacy EHRs but also establishes AI-ready, standardized datasets suitable for machine learning and large language models (LLMs).

Adopting FHIR and HL7 v2 standards, while integrating with provincial exchanges such as Ontario Health HIE or Alberta Netcare, supports interoperability and prepares data for responsible AI. Many hospitals still operate hybrid infrastructures with on-prem EHRs and emerging cloud layers. Interoperability is the bridge between legacy and future-state systems.

3. Integrate AI with Your Clinicians in Mind

Many successful AI models fail because they disrupt clinical workflows. The best practice is to design AI for the point of care, making its presence intuitive and its uses immediate. The AI insight should flow to the clinician, not the other way around. This means models should be delivered as passive, intelligent clinical decision support integrated directly within the existing interface.

A good place to begin to maintain clinical integrity is to start integrating AI into administrative and documentation tasks. After your team gets more comfortable with AI integrating into their work, you can start using AI for clinical applications, such as diagnostics or condition predictions. But to truly integrate AI with your clinicians in mind, you’ll want to keep it to as few steps as possible. AI should appear as a trusted companion within the clinician’s existing workflow, not an additional screen or alert competing for attention.

With this comes the design conundrum: designing for clinicians versus designing with clinicians. Hospitals that co-design AI solutions alongside their clinical and informatics teams consistently see higher adoption, trust, and measurable impact on care delivery. When solutions are developed in isolation, they often become more of a technical showcase than a true clinical asset. Co-creation ensures that AI tools enhance efficiency, support clinical decision-making, and ultimately improve patient outcomes.

4. Establish a System to Maintain Data Quality and Governance

AI models are not static; their performance degrades as underlying data sources change. Successfully managing data quality requires an ongoing commitment to hygiene and governance. In healthcare, maintaining data quality is a matter of patient safety and clinical reliability - it must be embedded into every data pipeline and governance review as a continuous operational function.

You’ll want to determine Key Performance Indicators (KPIs) for data quality, and maybe even deploy toolsets that can automatically track data lineage and provide alerts to members of your established data governance committee. This continuous feedback loop will help ensure that data fueling your AI models is reliable and maintains the integrity of clinical recommendations.

Quality dashboards should track data completeness, timeliness, and clinical validity: the same inputs that determine how trustworthy your AI models will be. When the underlying data deteriorates, AI performance and reliability degrade too. Embedding continuous data quality monitoring and governance ensures that every algorithm trained or deployed in the hospital remains clinically sound and ethically safe. These practices align with HIMSS AMAM Stage 3–4 standards for data integrity and analytics governance.

5. Invest in Enterprise-Wide AI Literacy and Training

Addressing the talent and literacy gap requires investment in your existing workforce and a change management strategy that fosters an innovation culture. This doesn’t mean you need to hire a new team of AI experts or replace departments. Rather, you should provide regular and current training for your existing team to ensure they continue to follow these best practices, understand compliance requirements, and know how to use AI. This training will likely look different for the varying roles at your organization:

• Leadership: Training in strategic value realization, data-driven decision-making, and responsible AI governance

• Clinicians: Training on how to interpret and trust AI outputs, along with support on how to prompt for what is possible.

• IT/Analysts:

  Training in data engineering, machine learning operations, and cloud technologies.

AI literacy should extend across clinical, administrative, and research teams. In hospitals, the ability to interpret AI outputs can directly affect patient care, so training must emphasize responsible use, explainability, and bias awareness. Hospital networks face unique staffing challenges; building internal AI literacy reduces reliance on external vendors and fosters long-term resilience.

How Keyrus Can Establish Your Data & AI Roadmap

Embarking on the journey to become a data-driven hospital can be overwhelming, but a structured approach can make the complex manageable. Trying to implement all these best practices simultaneously is a recipe for stalled progress and wasted resources. Keyrus specializes in transforming the complex reality of healthcare data into a pragmatic, high-value strategic roadmap.

By conducting a comprehensive Data & AI Maturity Assessment, we help your hospital:

• Pinpoint Your Position:

  Accurately measure where you stand against these five best practices.

• Define the Next Logical Step:

  Identify the foundational data and governance work you

  must

  complete before moving on to advanced clinical AI.

• Ensure ROI:

  Design a phased roadmap that prioritizes investments with demonstrable, short-term return, building the necessary organizational momentum to achieve long-term transformation and ethical, intelligent care.

Keyrus partners with hospitals to translate technical potential into clinical impact - improving outcomes, optimizing resource use, and enabling sustainable care. Let Keyrus help you lay the foundation for a successful, sustainable AI future.