AI governance in healthcare is the practice of managing and continuously evaluating AI tools to ensure that they are used safely and ethically in clinical settings.
AI in healthcare is still relatively new, yet it is rapidly becoming essential in optimizing daily clinical operations. AI governance empowers care providers to enforce the maintenance of documentation compliance. This is why care organizations need to ensure human oversight at every step.
Governance of AI also supports risk mitigation, apart from its benefit of reducing burnout among clinicians. In modern healthcare ecosystems, AI enhances workflows without compromising care quality. This contributes to the shift toward a hybrid model where AI manages repetitive tasks while clinicians take care of final oversight and control.
Heidi follows the human-AI model closely. This has led us to craft a framework that provides a solid structure for AI governance.
What is Heidi's AI Governance Framework?
Heidi’s framework for AI governance ensures that our AI features are managed with human supervision every step of the way. For each feature release, we keep documentation about known limitations and the intended use of artificial intelligence.
We ensure that clinicians always have access to the source of the transcripts and that they are supported with audit trails for accountability. This leaves them the ease of approving all the content of the notes before finalizing or pushing them into the EMR.
Our dedicated clinical and technical review group sees to it that all our decisions are documented, securely implemented. Heidi’s governance pillars place patient well-being first, with human oversight embedded in all critical steps.
At San Luis Valley Health, an AI governance framework had to work in real conditions, not add friction to them. The organization serves a large rural region across US, so they needed documentation that supported access, safety, and clinician well-being at the same time.
“We’re a rural community, so access matters. The less time we spend documenting, the more patients we can see and the more care we can provide,” shares Laticia Hollingsworth, PA-C.
With Heidi embedded into their everyday workflows, governance and efficiency have naturally aligned. “The brain drain is dramatically reduced. I’m done by 5:00 now. That never used to happen.” Administrative tasks that previously required one to two hours after normal working hours are now completed by the end of the workday.
By enabling same-day documentation, the reduced cognitive burden and increased patient throughput result in clinician-led adoption. This way, Heidi demonstrates how a good healthcare AI governance framework can improve access and sustainability in real-world settings.
What Does It Mean That Heidi Adheres to An AI Governance Framework?
Heidi follows an AI governance framework that spans the entire lifecycle of AI development and deployment. This includes the close evaluation processes, such as data handling, how models are developed, how risks are assessed, and how performance is monitored after releases.
In practice, this means that safety is engineered into every stage of the system, rather than assumed. Healthcare data is high-stakes and sensitive, so every decision we make concerning AI undergoes necessary evaluations for ethical, safe, and proper clinical use.
Data Protection by Design
By design and by default, every feature is built with data protection in Heidi. Our designated feature owners document how they ideate and prepare data, and these processes are safeguarded to ensure compliance and technical soundness.
Evidence Over Promises
At Heidi, we maintain clear, auditable records of how our AI makes decisions and how controls perform in real-world use, with traceable logs, monitoring, and reviews that make accountability practical and not aspirational. When any fairness or bias signal appears, we act immediately: we investigate, document findings, implement corrective and preventive actions, and verify the fix, sharing outcomes with relevant stakeholders to ensure transparency and continuous improvement.
Independent Assurance
We document the intended use, known limitations, and risk considerations of the AI features included in each release. While we are responsible for the system’s performance and security, clinicians remain accountable for verifying outputs.
Independent assurance, like our attestations for SOC 2 Type 2 and ISO 27001, provides an additional layer of validation that these responsibilities are met.
How Does Heidi Practice AI Governance in Healthcare?
To support care operations by keeping them safe, Heidi assigns formal responsibility for maintaining AI systems used in clinical documentation. As the leading AI care partner for clinicians, Heidi upholds accountability and protects data privacy through data minimization.
Heidi implements the following AI governance measures to ensure the robustness of its system:
Heidi uses a human-in-the-loop approach for clinical documentation
Heidi is a clinical documentation support tool. Outputs are drafts intended for clinician review, editing, and approval before anything is saved or shared. This keeps clinical judgment with the clinician and helps avoid autonomous decision-making.
Heidi does not use any of your data to train our AI
Heidi is dedicated to ensuring that underlying models are never trained using customer data. We limit our processing of patient data strictly to what is necessary for note generation. Our improvements are driven by configuration and clinician feedback, not by training on live patient records.
Heidi does not (and will not) sell de-identified data to advertisers or third parties
Our public privacy policy highlights how we govern information use in a way that prohibits the onward sale of patient data. At Heidi, everything runs aligned with strong access controls. We focus on supporting clinicians legally and securely; we do not and will not sell patient data.
Reinforce Robust AI Governance in Healthcare with Heidi
Heidi's AI governance framework maintains an intentionally low risk appetite and prioritizes the impact of our platform on actual patient outcomes. This focus is in addition to ensuring the technical accuracy of our AI models, which are crucial for the seamless operation of care organizations.
We are pursuing ISO 42001 certification to further demonstrate our commitment to responsible and safe AI governance. This is in line with helping your care organization prepare for and proactively address potential regulatory gaps in healthcare.
