AI Governance for Healthcare Implementers: From Heuristics to Infrastructure

As AI tools move rapidly into healthcare, most organizations aren’t developing new algorithms—they’re implementing third-party solutions into clinical, operational, or administrative workflows. That means they don’t control how the AI was built, but they are fully responsible for how it’s used.

Effective AI governance at this level is less about inspecting models and more about applying data integrity, quality assurance, and compliance principles to outputs the organization depends on. But that’s where it gets tricky: AI-generated outputs often lack the reliability safeguards we take for granted in traditional systems. And without clear governance standards, implementers can inadvertently take on significant—and avoidable—risk.

1. AI Is a Data Integrity Problem—With a Twist

AI outputs are still data—and like any data used in healthcare, their integrity matters. But the familiar tools of managing integrity don’t always apply.

Consider how we manage data in an EHR. Access is controlled through unique login credentials, system-level authentication, and an indelible audit trail tracking authorship and amendments. Errors can be traced, corrected, and explained. This makes regulatory compliance, patient safety, and quality assurance manageable—even when the data is imperfect.

By contrast, AI tools generate new data, often via models whose behavior is non-deterministic or partially opaque. The user may not fully understand the inputs the system relies on, the logic it uses to generate results, or the environmental factors that affect its performance. That makes data integrity more difficult to define—and harder to guarantee.

2. When Good Data Goes Bad: A Risk Scoring Case Study

This isn't hypothetical. A widely cited example involves a predictive analytics tool used by hospitals to prioritize patients for care coordination based on health risk scores. The tool relied heavily on claims data—which meant patients with documented chronic conditions were scored as higher risk, while those with similar or greater needs but less robust claims histories were deprioritized.

Because of structural inequities in healthcare access and utilization, this led to racial disparities in how patients were flagged for follow-up. Black and Hispanic patients were less likely to be identified—even when clinical records suggested similar or worse health status. Neither the developers nor the implementers intended this outcome, but it arose because no process was in place to test how the tool’s logic aligned with the goals of the clinical program.

Avoiding this kind of harm requires more than good intent. It requires implementers to proactively examine the assumptions and limitations of the tools they deploy—and build mitigation into the implementation process.

3. Know Your Risk Surface Area

AI tools come in many flavors. Some simply use machine learning models to optimize back-end infrastructure. Others rely on large language models (LLMs) or opaque algorithms to generate outputs that directly affect patient care or business decisions.

A key step in governance is to define the risk surface:

• Does the tool follow explicit, rule-based logic (deterministic), or does it generate probabilistic responses?

• Can the vendor explain how it works—and under what conditions it fails?

• Is the tool used in clinical workflows, backend operations, or general administrative functions?

Black-box systems that can’t be fully explained—or that vendors refuse to disclose—require stronger governance. If the tool’s output affects clinical decisions, reimbursement, or resource allocation, you must understand what it was designed to do, what gaps exist, and how to supplement or monitor it to meet your standards of care and compliance.

4. Quality Management Still Applies

Using an AI tool doesn’t eliminate responsibility—it redistributes it.

In regulated healthcare environments, organizations must supervise tools the way they would supervise staff. If a licensed professional uses AI-generated outputs (e.g., draft documentation, summarizations, or clinical suggestions), they are still fully responsible for reviewing, correcting, and finalizing the content in line with their professional duties.

Most general-purpose LLMs (like those powering chatbots or summarizers) are not medical devices and are not approved to diagnose or treat patients. But that doesn’t preclude their use. It simply means any use must occur under professional oversight and within the bounds of a defined scope.

5. Data Protection Is Still Data Protection

If your organization discloses protected health information (PHI) to an AI vendor—such as through an API integration with OpenAI, Google, or other platforms—you must evaluate:

• Whether a Business Associate Agreement (BAA) is required

• Whether your HIPAA Security Rule risk analysis covers the relevant endpoints and data flows

• Whether the vendor’s security controls meet your baseline

A useful heuristic: treat AI vendors the same way you’d treat a bricks-and-mortar contractor. If you wouldn’t send patient data to an external call center or billing firm without a BAA and a security review, don’t send it to a machine learning platform either.

6. From Heuristics to Governance Infrastructure

Many healthcare organizations are in a transitional state—moving from ad hoc heuristics (e.g., “just have someone double-check it”) to more structured governance models. Fortunately, frameworks like the NIST AI Risk Management Framework and recent HHS guidance provide principles and controls that can be adapted to your organization’s risk tolerance and operational footprint.

Practical governance steps include:

• Creating an AI governance committee with cross-functional representation (compliance, IT, clinical ops)

• Maintaining an AI tool inventory that identifies what tools are in use and how they are deployed (e.g., clinical vs. administrative vs. infrastructure)

• Defining the intended use and context of each tool, including how its outputs will inform decisions, who will rely on them, and what assumptions or constraints apply

• Conducting risk-based impact assessments before implementation, including clinical relevance, bias risk, regulatory scope, and quality requirements

• Instituting periodic review cycles to evaluate whether the tool’s performance, reliability, and use remain aligned with your goals and obligations

• Ensuring traceability and auditability of outputs used in regulated workflows, especially where clinical or legal accountability is required

Governance doesn’t have to be burdensome—it just has to be deliberate and documented.

Final Thought: AI Is Here. So Is Accountability.

Healthcare organizations don’t need to reverse-engineer every model—but they do need to understand what a tool does, how it behaves, and what’s at stake if it behaves unexpectedly.

That starts with defining the tool’s intended use, understanding its limitations, and establishing internal guardrails for how its outputs will be trusted, reviewed, or overridden. It also means ensuring that outputs used in regulated workflows are traceable and auditable, especially when they inform clinical decisions or business operations with downstream risk.

You wouldn’t hire a contractor without a background check or delegate a medical judgment to an unlicensed assistant. The same logic applies here: AI tools can be powerful assets—but only with the structure, supervision, and governance to match.

This post is for general informational purposes only and does not constitute legal advice. For questions about your organization's use of AI, consult qualified counsel or AI governance professionals.