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09/30/2026

AI in healthcare is moving from generating answers to taking action.

As AI agents begin participating in workflows, healthcare leaders need to understand that capability does not equal authority.

The critical governance question is: What is AI allowed to do, and who remains accountable?

I use the framework Assist, Recommend, Decide, Act to help define that authority and determine where meaningful human governance must remain. |

Because AI may participate in the decision, but AI cannot own accountability for the decision.

Read my latest article, AI Agents in Healthcare: When AI Stops Answering and Starts Acting, for a closer look at what this shift means for healthcare AI governance - https://www.procodecs.com/single-post/ai-agents-healthcare-ai-governance

09/28/2026

AI implementation is not just about the technology. It is about the people who own the workflow, run the workflow, and live with the consequences of the technology.

Before deploying AI, healthcare organizations need to ask a different set of questions:
Who owns the workflow?
Who operates it?
Who depends on the output?
Who is affected when the AI gets it right—and when it gets it wrong?
And perhaps most importantly: Who has power over the AI, and who does AI have power over?

Stakeholder mapping should be part of responsible AI governance, not something added after the technology has already been selected.

As AI moves from assisting people to recommending, deciding, and taking action, understanding who is affected becomes even more important.

I explore this in my latest article: AI Stakeholder Mapping in Healthcare: Before You Deploy AI, Map Who It Will Affect

09/25/2026

AI adoption is not the same thing as AI value.

I see organizations counting how many people are using AI, how many claims it reviewed, how many documents it summarized, and how many hours it saved.

Those numbers are easy to measure.

The harder question is: What actually changed because we implemented AI?

If AI saves three hours, what happened with that capacity? Did we improve the workflow, address a backlog, increase audit capacity, reduce risk, or create meaningful financial value?

And we cannot look at the benefit without looking at the risk.

After more than four decades in healthcare compliance, I have learned that a number only tells part of the story. We also have to ask whether the outcome was accurate, defensible, and compliant.

That is why I believe we need to track value, not activity.

AI should not be considered successful simply because it is being used.

It should be able to demonstrate the value it was intended to create, and that the value is being achieved within acceptable risk.

That is the conversation healthcare leaders need to have now.

AI ROI in Healthcare: Track Value, Not Activity

09/23/2026

Your AI policy may be current today and outdated tomorrow. The problem is not necessarily that the policy was poorly written. The problem is that the technology it was written to govern may have changed.

I have spent more than four decades in healthcare compliance, and one thing I have learned is that effective compliance has never been about writing a policy and putting it on a shelf. It is about understanding the risk, establishing the right controls, monitoring whether those controls are working, addressing exceptions, and reassessing when circumstances change. AI requires that same discipline.

A tool may begin by summarizing information and later evolve to make recommendations. A vendor update may expand what the technology can access, change how it processes information, or introduce new functionality. An AI agent may eventually be authorized to take action rather than simply provide information. When the capability, authority, or risk changes, our governance has to change with it.

That is why I keep coming back to a principle I use often: Build the brakes before you need them.

The important question is not simply whether we have governance in place. It is whether the governance is still appropriate for what the AI is capable of doing today. As AI moves from Assist to Recommend to Decide to Act, the controls, oversight, monitoring, and human accountability need to evolve with it.

We cannot govern yesterday’s AI with yesterday’s assumptions. Good AI governance has to be designed to adapt as the technology, its authority, and the risks change.

I explore this in my latest article:https://www.procodecs.com/single-post/healthcare-ai-governance

09/21/2026

One thing I keep coming back to in healthcare AI is this:

Good AI governance has to be more than a policy, a committee, or a compliance requirement.

To me, governance is ultimately about accountability to the people affected by the decisions AI helps make.

As healthcare organizations bring AI into claims, reimbursement, patient communication, utilization review, employee evaluation, and other consequential workflows.

I believe we need to approach AI risk the same way we approach other compliance risks:

Identify it. Assess it. Control it. Monitor it. Correct it.

That means getting practical about the questions that matter:

Who could be affected when this AI acts?

What is at stake?

Who benefits—and who bears the risk?

What controls are actually in place?

Are those controls working?

And perhaps most importantly: Who can intervene when they aren't?

I've come to believe that a stakeholder concern without a corresponding control is not governance. It's awareness.

And a control that exists on paper but is never tested, monitored, or improved isn't enough either.

The AI may perform the work. The organization still owns the responsibility.

That, to me, is where healthcare AI compliance becomes operational and where responsible AI governance starts to mean something beyond documentation.

Proud to Keep Learning, Growing & LeadingI’m excited to share that I have completed the Harvard Data Science Review 3-Mo...
09/15/2026

Proud to Keep Learning, Growing & Leading

I’m excited to share that I have completed the Harvard Data Science Review 3-Month AI Leadership Intensive: Strategy, Governance & Agentic Systems and received my Certificate of Completion.

AI is moving at light speed, and healthcare cannot afford to approach it without strong governance, preparation, and accountability.

From AI scribes and clinical support tools to coding and billing, prior authorization, revenue cycle integrity, compliance, and administrative operations, AI is rapidly changing how healthcare works. The opportunities are tremendous, but so are the risks when these technologies are implemented without the right governance, controls, and human accountability.

At ProCode Compliance Solutions, we are committed to continuing to grow, stretch, and stay ahead of this rapidly changing landscape so we can bring our clients the most current thinking and practical guidance.

Whether we are advising on AI governance and compliance, revenue cycle integrity, regulatory risk, or audit services, our mission remains the same: help our clients innovate responsibly, mitigate risk, and protect the integrity of their organizations.

The technology may be moving at light speed. Our governance, compliance, and accountability have to keep pace.

09/10/2026

After 40+ years in healthcare compliance, I’ve learned that risk rarely stays where we first identify it.

AI is no different.

We can govern the technology inside our organization and still miss the people outside it who experience the consequences.

So I believe healthcare leaders need to ask:

Who has power over the AI?
Who does the AI have power over?
Who bears the impact when it is wrong?

Patients. Providers. Employees. Members. Caregivers. Communities.

As AI becomes more autonomous, responsible governance must look beyond the system and consider the people affected by its decisions.

Protecting stakeholders is part of protecting the organization.

I explore this further in my latest article, “AI Governance Doesn’t Stop at Your Organization’s Door.”

Here’s my full article to read: https://www.procodecs.com/single-post/ai-governance-doesn-t-stop-at-your-organization-s-door

09/08/2026

Before deploying AI, healthcare leaders should ask one question:
Who will this affect?

The first question is familiar.
Who approves it?
Who configures it?
Who monitors it?
Who can override it?
Who can stop it?

The second question is where I think we need to spend more time.
Whose work does it influence?
Whose information does it analyze?
Whose decisions could change?
Who experiences the consequences when it gets something wrong?

That’s why I believe stakeholder mapping should happen before AI deployment.

AI is never just a technology implementation.
It is an organizational ecosystem.

I explore what that means for healthcare leaders in my latest article, Before You Deploy AI, Map Who It Will Affect.

09/07/2026

As healthcare AI becomes more autonomous, governance has to evolve with it.

It’s not enough to ask whether an AI system is accurate.

What happens when it’s wrong?

A formatting error is one thing. A missed overpayment, PHI exposure, inappropriate billing recommendation, or patient-care error is something else entirely.

That’s why governance has to shift from reviewing outputs to governing the system.

As AI earns more autonomy, organizations need stronger boundaries, monitoring, escalation, auditability, and intervention.

The goal isn’t to keep a human reviewing every AI action forever.

It’s to make sure humans remain accountable and able to intervene when it matters.

AI autonomy should be earned, not assumed.

Read the full article: As Healthcare AI Autonomy Grows, Governance Must Grow With It.

09/04/2026

AI Literacy Is Not One-Size-Fits-All

Healthcare organizations need AI literacy across the enterprise.

But not everyone needs the same AI skills.

A board member needs strategy, risk, and governance literacy.
A physician needs practical AI knowledge and judgment.
An implementation team needs workflow, data, testing, and controls expertise.
The broader workforce needs to understand appropriate use, limitations, and when to escalate.

The goal isn’t to make everyone an AI expert.
It’s to make every role AI-ready.

Different roles. Different skills.
Shared responsibility.

AI transformation won’t happen inside IT.
It will happen across the organization.

Read the full article → https://www.procodecs.com/single-post/ai-literacy-is-not-one-size-fits-all-what-healthcare-leaders-ai-leaders-and-implementation-teams

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