AI Accountability in Healthcare: Who Owns the Decision?

As AI becomes more capable of analyzing, recommending, and acting, healthcare organizations must clearly define where machine intelligence ends and human authority begins.

Artificial intelligence is moving beyond simply helping us find information. AI can now summarize complex regulations, analyze documentation, identify patterns, compare policies, prioritize risk, recommend next steps, and increasingly participate in multi-step workflows.

That creates enormous opportunity for healthcare. It also raises a question that I believe every healthcare leader should be asking: When AI influences a decision, who owns the decision?

The answer cannot be the technology. In healthcare, decisions have consequences. They can affect patient care, reimbursement, regulatory compliance, privacy, payment integrity, audit findings, and organizational risk. As AI becomes more sophisticated, organizations need more than a general expectation that “a human will review it.” They need to deliberately define decision authority, accountability, escalation, and oversight.

AI Can Sound Certain When It Isn’t

One of the most important things leaders need to understand about generative AI is that confidence and accuracy are not the same thing. An AI system can produce a response that is polished, logical, detailed, and wrong.

We commonly refer to incorrect or fabricated AI-generated information as a hallucination, but hallucinations are only part of the challenge. A system may misinterpret an ambiguous regulation, rely on outdated information, miss an important exception, fail to recognize payer-specific requirements, combine rules that apply to different circumstances, or draw a conclusion that goes beyond the underlying evidence. It may also provide an answer without adequately communicating uncertainty.

In healthcare compliance, those distinctions matter. A recommendation can be 95 percent correct and still create significant exposure if the remaining 5 percent involves the exception that applies to your organization. That is why AI output cannot automatically become organizational truth.

Retrieval Is Not Interpretation

This distinction becomes especially important when AI is used for regulatory intelligence. Imagine an AI agent identifies a new CMS publication. It retrieves the authoritative source, summarizes the change, compares it with previous guidance, identifies potentially affected providers, and classifies the issue as high risk. That could save a compliance team hours of work.

But the next question is more difficult: What does this mean for our organization or our clients? That is where retrieval becomes interpretation.

AI may be able to identify what changed and suggest possible operational implications. But determining whether the change applies to a particular provider, service, claim, contract, patient population, or set of circumstances may require professional judgment and additional facts. That distinction should be intentionally designed into the workflow.

AI can prepare the decision. A qualified professional may still need to make it.

Not Every AI Decision Carries the Same Risk

Healthcare organizations should avoid treating every AI-assisted decision the same way. There is a significant difference between asking AI to organize meeting notes and asking it whether a provider should refund a potential Medicare overpayment. Governance should reflect that difference.

A lower-risk activity might permit AI to complete a task with routine monitoring. A moderate-risk activity may require validation before work proceeds. A high-risk activity may require mandatory human review and approval. Some decisions should remain entirely human-owned. The key is to establish these distinctions before AI is deeply embedded in the workflow.

Define the Human-Owned Decisions

Every organization implementing AI should identify decisions the technology may inform but should not independently make. In healthcare compliance, these may include final regulatory interpretations and audit findings, determinations that an overpayment exists, decisions to refund or disclose, client-facing compliance recommendations, material changes to billing or coding practices, legal conclusions requiring counsel, and actions carrying significant financial, regulatory, privacy, or patient-care consequences.

AI may perform substantial work leading up to these decisions—and that is where much of its value lies. It can retrieve evidence, summarize information, identify discrepancies, organize records, compare requirements, highlight risk, and prepare preliminary analysis. But the organization should know exactly where AI’s authority stops.

Human-in-the-Loop Is Important—but We Need More

“Human-in-the-loop” has become one of the most common phrases in responsible AI. The concept is important because a person remains involved somewhere in the process. But as I have worked through the design of AI-enabled healthcare compliance workflows, I have become increasingly convinced that this description is not enough.

A human should not simply appear at the end of an automated process and click Approve. I believe we need to think in terms of Human-in-Governance™.

Human-in-Governance™ asks who establishes what AI is authorized to do, defines acceptable sources and risk thresholds, determines when human review is mandatory, and specifies which evidence must support a recommendation. It also establishes who can override AI, who decides when a workflow must stop, and who ultimately remains accountable for the outcome. The human is not merely “in the loop.” The human governs the loop.

AI Agents Need Boundaries

This becomes particularly important as organizations experiment with agentic AI. Instead of thinking about one system doing everything, consider a workflow involving several different capabilities.

An Assistant Agent might retrieve and summarize a new regulation. An Analyst Agent could determine what changed, classify potential risk, and identify affected populations. An Orchestrator Agent might route the matter based on risk and urgency, while a Guardian Agent verifies source traceability, required evidence, quality standards, and approval requirements. A Tasker Agent may perform an approved operational action.

This can become an extraordinarily powerful system, but power without clearly defined authority creates risk. The organization must determine which agents can recommend, route, act, or block—and where human approval is mandatory.

AI Should Sometimes Be Designed to Stop

We often evaluate AI based on what it can accomplish. In a governed healthcare environment, we should also evaluate whether it knows when not to proceed. Consider a regulatory intelligence agent that encounters conflicting guidance. The wrong response is to select whichever interpretation appears most likely and continue. The appropriate response may be:

Conflict detected. Human interpretation required.

Similarly, AI should stop or escalate when an authoritative source cannot be verified, information is incomplete, confidence falls below a defined threshold, guidance is ambiguous, sources conflict, a matter exceeds authorized scope, a high-risk determination is involved, or required human approval has not occurred.

In these circumstances, escalation is not evidence that AI failed. It is evidence that governance worked.

Transparency Matters Because Decisions Must Be Defensible

A healthcare organization may eventually need to explain an AI-assisted decision. It could be questioned internally—or by a payer, auditor, regulator, attorney, board member, or client. Simply saying, “The AI recommended it,” will not be sufficient.

Organizations should be able to understand and reconstruct the decision process. For significant AI-assisted decisions, I believe we should strive for a traceable chain: Source → Analysis → Evidence → AI Recommendation → Human Review → Decision → Action.

That chain allows an organization to explain what information was considered, where it came from, what AI concluded, how certain the system was, which exceptions or conflicts were identified, who reviewed it, what the human decided, and what happened next.

That is where transparency becomes more than an ethical principle. It becomes part of organizational defensibility.

What Happens When the Human Disagrees With AI?

This is another issue organizations should address before deployment. Suppose AI classifies a regulatory change as low risk, but an experienced compliance professional believes it is significant. Who wins?

The answer should be clear: the qualified human should have authority to override AI when professional judgment indicates that the conclusion is inappropriate. But that override also creates valuable information. Why did the human disagree? Was AI missing context? Was the source incomplete? Was the risk-classification rule poorly designed? Does the model need additional knowledge? Should the governance threshold change? Human disagreement should not simply disappear. It can become part of the feedback mechanism that improves the system.

Accountability Cannot Become Ambiguous

One risk of increasingly sophisticated AI is what I call the accountability gap. The AI generated the recommendation, the employee relied on it, the organization purchased the technology, the vendor built the model, and the data came from somewhere else. So who is responsible?

Healthcare organizations cannot allow accountability to become distributed so widely that no one ultimately owns the decision. Before deploying consequential workflows, they should define who owns the workflow and AI risk, validates performance, holds approval authority, monitors exceptions, investigates failures, can suspend the system, and reports significant issues to leadership. These are governance questions—not technology questions.

AI Should Strengthen Professional Judgment, Not Dilute It

I see enormous potential for AI in healthcare. But the most valuable future is not one in which it replaces healthcare expertise. It is one in which AI allows experts to use their expertise more effectively.

A compliance professional should spend less time searching ten websites for regulatory updates and more time determining what those updates mean. An auditor should spend less time assembling records and more time evaluating risk. A physician should spend less time performing administrative work and more time caring for patients. A revenue cycle leader should receive better intelligence for making decisions—not surrender those decisions to an algorithm.

That is augmentation, and that is where I believe healthcare AI can create tremendous value.

The ProCode Perspective

As AI becomes more capable, healthcare organizations will need to become more disciplined about defining authority. The question is no longer simply, “Can AI do this?” We also need to ask, “Should AI do this?” And then, “Under what conditions, with what controls, using what evidence, and under whose authority?”

That is a much more mature conversation. AI can help us see more, analyze faster, identify patterns humans may miss, remove enormous amounts of administrative burden, and help professionals make better-informed decisions. But in healthcare, consequential decisions require accountability.

AI can inform the decision. AI can prepare the decision. AI can even recommend the decision. But we must deliberately decide who owns the decision.

That is the foundation of responsible AI, and the reason I believe Human-in-Governance™ will become increasingly important as healthcare moves into the agentic AI era.