Most healthcare AI governance efforts produce a policy document that sits alongside the HIPAA compliance manual, gets reviewed once a year, and doesn’t actually change what happens when a department wants to deploy a new AI tool next month. A working governance framework does something different: it gives a specific, repeatable answer to what has to happen before any AI system touches Protected Health Information, and who’s accountable if it goes wrong.
A four-part structure that maps to how AI actually gets adopted
One useful way to structure healthcare AI governance follows a four-pillar approach — Govern, Map, Measure, Manage — that mirrors how AI systems actually move through a health system, from decision to deployment to ongoing use.
Govern means naming an accountable owner and setting an acceptable-use policy before the first tool is evaluated — which tools are approved, what data can and can’t go into them, and who signs off. Without this step, governance becomes reactive: a policy written in response to an incident rather than a standard applied before one happens.
Map means building an actual inventory of every AI system touching PHI — not a one-time list, but a maintained one — documenting what data each system processes, whether a Business Associate Agreement is in place, and what the vendor’s actual data-handling practices are. Most health systems don’t have this list complete, which means most health systems can’t currently answer “which of our AI tools would be exposed if this specific vendor had a breach” — a question worth being able to answer before it’s asked under pressure.
Measure means an AI-specific impact assessment before deployment: how sensitive is the data involved, what’s the risk if the output is wrong, is there patient-safety exposure, and how much is a clinical decision likely to rely on this system’s output versus treat it as one input among several.
Manage means the operational controls — BAAs in place, access restricted appropriately, usage logged, staff trained on the specific tool, and an incident response path that connects to the breach response process that already exists, rather than a separate, newly invented one.
Why this is more urgent than it was even a year ago
The proposed 2025 HIPAA Security Rule update explicitly names artificial intelligence, alongside quantum computing and augmented reality, as an emerging technology requiring risk analysis. The proposal would also eliminate the current “required versus addressable” distinction in the Security Rule, making controls like asset inventories, encryption, and multi-factor authentication mandatory rather than optional based on risk assessment. In practice, that shift makes the Map step above — the AI systems inventory — closer to a compliance requirement than a best practice.
Where governance frameworks actually break down
The Govern and Manage pillars get the most attention because they produce visible artifacts — a policy, a training record. The Map and Measure pillars get skipped more often, because they require ongoing maintenance rather than a one-time sign-off, and they’re the two most likely to reveal an uncomfortable gap, like an AI tool already in use without a BAA. That’s exactly why they matter most: the pillars most likely to get skipped are the ones most likely to be protecting against a real, current exposure.
A concrete starting point
Before writing or revising an AI governance policy, build the Map first: a real, current list of every AI tool touching PHI in your organization, with BAA status noted for each. That single artifact will surface more actionable governance gaps than a rewritten policy document, and it’s the piece most governance efforts skip.
SPAR builds AI governance around this operational structure — inventory and impact assessment first, policy language second — because that’s the order that actually reduces exposure rather than just documenting an intention to. If your governance framework exists mostly as a document rather than a maintained inventory, that gap is worth closing before the next AI tool gets adopted.
Zev is a Branding Manager who specializing in content writing at SPAR, he is passionate about crafting compelling narratives that bring brands to life. With a background in both marketing strategy and creative writing, he bridge the gap between data-driven insights and imaginative storytelling to create impactful, consistent brand experiences.
Most healthcare AI governance efforts produce a policy document that sits alongside the HIPAA compliance manual, gets reviewed once a year, and doesn’t actually change what happens when a department wants to deploy a new AI tool next month. A working governance framework does something different: it gives a specific, repeatable answer to what has to happen before any AI system touches Protected Health Information, and who’s accountable if it goes wrong.
A four-part structure that maps to how AI actually gets adopted
One useful way to structure healthcare AI governance follows a four-pillar approach — Govern, Map, Measure, Manage — that mirrors how AI systems actually move through a health system, from decision to deployment to ongoing use.
Govern means naming an accountable owner and setting an acceptable-use policy before the first tool is evaluated — which tools are approved, what data can and can’t go into them, and who signs off. Without this step, governance becomes reactive: a policy written in response to an incident rather than a standard applied before one happens.
Map means building an actual inventory of every AI system touching PHI — not a one-time list, but a maintained one — documenting what data each system processes, whether a Business Associate Agreement is in place, and what the vendor’s actual data-handling practices are. Most health systems don’t have this list complete, which means most health systems can’t currently answer “which of our AI tools would be exposed if this specific vendor had a breach” — a question worth being able to answer before it’s asked under pressure.
Measure means an AI-specific impact assessment before deployment: how sensitive is the data involved, what’s the risk if the output is wrong, is there patient-safety exposure, and how much is a clinical decision likely to rely on this system’s output versus treat it as one input among several.
Manage means the operational controls — BAAs in place, access restricted appropriately, usage logged, staff trained on the specific tool, and an incident response path that connects to the breach response process that already exists, rather than a separate, newly invented one.
Why this is more urgent than it was even a year ago
The proposed 2025 HIPAA Security Rule update explicitly names artificial intelligence, alongside quantum computing and augmented reality, as an emerging technology requiring risk analysis. The proposal would also eliminate the current “required versus addressable” distinction in the Security Rule, making controls like asset inventories, encryption, and multi-factor authentication mandatory rather than optional based on risk assessment. In practice, that shift makes the Map step above — the AI systems inventory — closer to a compliance requirement than a best practice.
Where governance frameworks actually break down
The Govern and Manage pillars get the most attention because they produce visible artifacts — a policy, a training record. The Map and Measure pillars get skipped more often, because they require ongoing maintenance rather than a one-time sign-off, and they’re the two most likely to reveal an uncomfortable gap, like an AI tool already in use without a BAA. That’s exactly why they matter most: the pillars most likely to get skipped are the ones most likely to be protecting against a real, current exposure.
A concrete starting point
Before writing or revising an AI governance policy, build the Map first: a real, current list of every AI tool touching PHI in your organization, with BAA status noted for each. That single artifact will surface more actionable governance gaps than a rewritten policy document, and it’s the piece most governance efforts skip.
SPAR builds AI governance around this operational structure — inventory and impact assessment first, policy language second — because that’s the order that actually reduces exposure rather than just documenting an intention to. If your governance framework exists mostly as a document rather than a maintained inventory, that gap is worth closing before the next AI tool gets adopted.
Recent Posts
Recent Comments
About Me
Zev Gomes
Zev is a Branding Manager who specializing in content writing at SPAR, he is passionate about crafting compelling narratives that bring brands to life. With a background in both marketing strategy and creative writing, he bridge the gap between data-driven insights and imaginative storytelling to create impactful, consistent brand experiences.
Popular Categories
Popular Tags
Archives