Healthcare AI Requires Data Governance Before Deployment

Healthcare organizations racing to deploy artificial intelligence are overlooking a fundamental prerequisite: trustworthy data governance that matches the discipline applied to financial management.

Robert Slepin, chief digital officer at SE Health, one of Canada's largest home health and community care providers, draws a stark contrast between how health systems treat different enterprise assets. Finance leaders can articulate cash positions, uses, and returns with precision. Few CIOs can offer equivalent confidence about their data inventory.

"From my experience, healthcare providers do not govern and manage data assets as well as they oversee and administer financial resources such as cash," Slepin said, according to Healthcare IT News.

That gap has become a strategic liability as organizations modernize technology infrastructure and pursue AI initiatives. Without foundational data governance and architecture, health systems struggle to produce consistent reporting, reliable analytics, and dependable decision support—let alone safe AI outputs.

Why it matters

Poorly governed data increases the likelihood of AI systems producing biased, unsafe, or confidently false results. As healthcare organizations invest heavily in AI capabilities, the absence of rigorous data management threatens both patient safety and return on investment. Organizations that cannot trust their underlying data cannot trust the AI systems built upon it.

From compliance to strategic capability

Slepin, who also serves as a CIO advisor in Epic's emeritus program, frames data governance as more than regulatory compliance. Confidentiality protects privacy. Data integrity helps clinicians avoid harmful decisions. Availability reduces delays, duplication, and unnecessary care.

He views data as an asset capable of generating measurable organizational value—transformed into information, insight, knowledge, predictions, and action that improve access, affordability, and health outcomes.

Architecture complements governance by providing the blueprint for organizing clinical, financial, operational, and research information across increasingly complex environments.

SE Health's practical approach

SE Health has elevated enterprise data governance and architecture to a pillar of its digital transformation strategy while modernizing legacy applications and moving toward new business intelligence and AI platforms.

The organization faces common challenges: users disagree on authoritative sources for key data elements, while decentralized analytics, spreadsheets, and multiple reporting tools create inconsistent outputs. Carrying those problems into new platforms simply institutionalizes existing weaknesses.

Rather than pursuing enterprise-wide perfection, SE Health established a Data Governance Council focused on practical objectives. Initial work includes clarifying HR and finance data ownership, resolving contested definitions, and building an evidence-based action plan before major system deployments.

Starting the journey

"Get going. Start from wherever you are. It's not a one-and-done project. Rather, it's a journey to establish data governance and architecture, and it's an ongoing process to maintain and evolve it over time," Slepin said.

He recommends beginning with a clearly defined business problem that executives already recognize as urgent. Connecting governance work to measurable organizational priorities helps secure sponsorship, builds momentum, and demonstrates value early.

For CIOs, the implications extend beyond AI readiness. Organizations that know what data they have, who owns it, how it's defined, and how it flows across the enterprise will be positioned to make better operational, financial, and clinical decisions.

In Slepin's view, governing data with the same discipline applied to cash is no longer an IT aspiration—it's becoming an executive imperative.

These details were first reported by Bill Siwicki at Healthcare IT News.