Artificial intelligence is rapidly reshaping healthcare, offering new ways to analyze data, support clinical decisions, streamline operations, and improve patient outcomes. From predictive analytics to ambient documentation tools, AI systems are becoming embedded in everyday workflows.
Yet as these technologies evolve, the legal and ethical frameworks governing their use remain grounded in long‑standing privacy and professional standards. In addition to HIPAA, which defines the federal rules for how Protected Health Information (PHI) may be used or disclosed, healthcare organizations must also navigate evolving state AI laws, ethical obligations embedded in professional codes of conduct, and their own organizational policies governing the responsible use of technology.
These frameworks emphasize responsibilities such as safeguarding patient confidentiality, exercising independent clinical judgment, and ensuring that technology does not replace the professional duties of licensed practitioners. Understanding how compliance with HIPAA and these broader obligations apply to the use of AI is essential for healthcare organizations seeking to innovate responsibly while protecting the confidentiality of health information.
How AI Is Being Used in Healthcare
AI tools now appear across nearly every corner of the healthcare ecosystem, but not all AI functions in the same way. Understanding these distinctions helps healthcare organizations assess risks, determine when PHI may be used or disclosed, and train workforce members on the appropriate use of AI tools.
Broadly, AI in healthcare can be grouped into four categories: artificial intelligence that performs tasks autonomously, augmented intelligence that supports human decision‑making, automation software with AI capabilities, and generative AI.
- Autonomous AI
This category includes systems designed to carry out specific tasks without continuous human involvement. These tools operate within defined parameters and produce outputs that may be used directly in clinical or operational workflows.
Examples include:
- Autonomous diagnostic tools that detect diabetic retinopathy without requiring a clinician to interpret the image
- Imaging analysis systems that independently identify abnormalities on radiology scans
- Continuous‑monitoring tools that detect patient deterioration and trigger alerts
These systems raise important questions about clinical oversight, liability, and the extent to which AI outputs can be relied upon without human review.
- Augmented Intelligence
Augmented intelligence is designed to enhance, not replace, human judgment. These systems provide recommendations, predictions, or insights, but a clinician or workforce member remains responsible for interpreting the output and making the final decision.
Examples include:
- Clinical decision support tools that suggest potential diagnoses or flag medication interactions
- Risk‑stratification models that identify patients at high risk for readmission or deterioration
- Population health analytics that help clinicians prioritize outreach or interventions
Because humans remain in control, augmented intelligence often fits more comfortably within existing professional and ethical frameworks, but it still requires careful oversight to avoid over‑reliance on algorithmic outputs.
- Automation Software with AI Capabilities
Many healthcare organizations use automation software to streamline administrative and operational tasks. When these systems incorporate AI such as machine learning or natural‑language processing, they can perform more complex functions than traditional rule‑based automation.
Examples include:
- Revenue cycle tools that extract data from clinical documentation, predict coding categories, or flag claims likely to be denied
- Prior authorization systems that help gather required documentation or identify missing elements
- Operational workflow tools that predict no‑shows or optimize appointment scheduling
These tools often fall under “healthcare operations” for HIPAA purposes, but they still require access and audit controls, training to prevent impermissible disclosures of PHI, and, when software is provided by a third‑party vendor, Business Associate Agreements.
- Generative AI
Generative AI tools create new content based on patterns learned from large datasets. In healthcare, generative AI is increasingly used to create text, summaries, images, or structured data to reduce administrative burden and support communication.
Examples include:
- Ambient documentation tools that draft clinical notes based on recorded patient encounters
- Drafting tools that generate patient instructions, referral letters, or summaries for care coordination
- Chatbots that answer patient questions or help navigate services, sometimes using PHI to personalize responses
- AI‑enabled translation tools that generate full sentences rather than translating inputs word‑for‑word
Generative AI tools can improve efficiency and accessibility, but they also raise concerns about accuracy, context, and whether PHI is transmitted to systems that lack appropriate safeguards. These risks make governance, vendor management, and workforce training especially important.
HIPAA’s Role in Governing AI Use
HIPAA does not contain AI‑specific provisions because the HIPAA Security Rule is designed to be technology‑neutral. As a result, HIPAA’s existing Privacy, Security, and Breach Notification Rules govern how PHI may be used or disclosed to AI tools. These requirements apply regardless of whether PHI is handled by a human, a traditional software system, or an advanced AI model.
Under HIPAA, the starting point is whether a use or disclosure of PHI is permissible. PHI may be shared with an AI system for treatment, payment, and healthcare operations without patient authorization. When PHI is used for operational purposes, HIPAA requires organizations to limit the information disclosed to the minimum necessary to achieve the purpose of the disclosure.
The HIPAA Security Rule’s administrative, physical, and technical safeguards also apply in full. These safeguards require organizations to assess risks, implement appropriate controls, and ensure the confidentiality, integrity, and availability of PHI, regardless of whether information is processed by humans or algorithms.
When an AI tool is provided by a third‑party vendor, HIPAA’s business associate requirements come into play. A Business Associate Agreement is required whenever a vendor creates, receives, maintains, or transmits PHI on behalf of a covered entity, including when the vendor uses AI to perform regulated functions.
If PHI is disclosed to a third‑party AI tool without a Business Associate Agreement in place, or if de‑identified information is re‑identified by a vendor’s AI system, the incident qualifies as a notifiable breach under the HIPAA Breach Notification Rule. Other events may also trigger breach notification obligations – for example, if an AI‑generated output includes more than the minimum necessary information and is then shared (even permissibly) with a third party without being validated for HIPAA compliance.
In other words, AI does not sit outside HIPAA. It is simply another mechanism through which PHI may be used or disclosed, and the same HIPAA compliance obligations apply. What changes with AI is not the legal framework, but the operational risks and the need for organizations to understand how these tools function so they can apply HIPAA’s requirements appropriately.
State Laws with Stricter Requirements
While HIPAA provides the federal baseline for privacy and security, multiple states have enacted more stringent laws governing disclosures to AI tools or automated decision‑making systems. Some states (i.e., Texas) have enacted multiple laws that impact the use of AI in different areas of healthcare.
These laws vary widely in scope and applicability but often include requirements such as explicit consent before sensitive information can be used for automated processing, restrictions on secondary uses of data (including model training), and transparency obligations requiring organizations to inform individuals when AI is used in their care. Several prohibit sharing sensitive categories of information with AI tools, such as mental health, reproductive health, substance use disorder, or genetic data.
For organizations operating across multiple states, these variations create a complex compliance landscape. Workforce training must reflect not only HIPAA but also the most protective state‑level requirements that apply to the organization’s operations.
The Risks of Using AI in Healthcare and How to Avoid Them
AI introduces new categories of risk that extend beyond traditional privacy and security concerns. Some risks arise from how AI systems process information, while others stem from how workforce members interact with these tools. Understanding these risks, and implementing safeguards to mitigate them, is essential for using AI in a manner that complies with HIPAA and protects the confidentiality of health information.
One of the most common risks is the inadvertent disclosure of PHI when workforce members enter identifiable information into public or non‑HIPAA‑compliant AI tools. Even when an AI tool is approved, staff may unintentionally disclose more than the minimum necessary, especially when copying AI‑generated outputs into emails, referral notes, or other communications.
AI systems also carry operational and clinical risks due to confabulations. Confabulations occur when an AI tool combines unrelated or partially related data elements into a single, inaccurate output. These errors can lead to incorrect summaries, misaligned recommendations, or misleading documentation if they are relied on without verification. AI tools may also behave unpredictably when encountering unusual inputs, edge cases, or ambiguous information.
To manage these risks, organizations should implement mechanisms that allow workforce members to report anomalies, unexpected behaviors, and inaccurate outputs. These reports help identify patterns, support continuous improvement, and ensure that AI tools are used safely. They can also support the development of standardized prompts, helping organizations determine whether inaccuracies stem from the tool itself or from the way a question is phrased or input.
Logging AI interactions is equally important. Audit logs allow organizations to review how AI tools were used, assess the accuracy of outputs, and investigate potential privacy incidents or operational errors. Logging also supports quality assurance, model monitoring, and compliance reviews.
Other risks include data leakage, model drift, and over‑reliance on automation. For example, if an AI model is trained on outdated data, its outputs may become less accurate over time. Similarly, workforce members may assume that AI‑generated content is always correct, leading to reduced vigilance and missed errors.
Organizations can avoid these risks by using only AI tools that support HIPAA compliance, configuring the tools to mitigate the risk of a HUIPAA violation, and maintaining clear policies on what staff may and may not input into AI systems. Strong governance structures are also essential to evaluate new AI tools, monitor performance, and ensure that safeguards remain effective over time.
Training the Workforce to Use AI in Compliance with HIPAA
As AI tools become part of everyday workflows, workforce members must understand how to use them in a way that protects patient privacy and complies with HIPAA. HIPAA AI training for healthcare staff should give staff a clear understanding of the risks associated with AI, the safeguards the organization has put in place, and the practical steps each person must take to ensure PHI is handled appropriately.
AI introduces several risks that staff need to be aware of. These include the inadvertent disclosure of PHI when information is entered into public or non‑HIPAA‑compliant tools, the possibility of confabulations that combine unrelated data into inaccurate outputs, and the risk of over‑reliance on AI‑generated content. AI tools may also behave unpredictably when encountering unusual inputs or ambiguous information, and outputs may contain more than the minimum necessary if not carefully reviewed.
As part of training, organizations should clearly identify which AI tools have been authorized and configured to support HIPAA compliance. Staff should be instructed to use only these approved platforms and to avoid entering PHI into any unapproved or public AI system. Training should also explain that approved tools have been evaluated for security, contractual protections, and appropriate safeguards, but that these protections do not eliminate the need for human oversight.
Training should also cover state‑specific requirements. Some states impose stricter consent rules, especially for sensitive categories of information such as mental health, reproductive health, substance use disorder, or genetic data. Workforce members must understand when consent is required before using AI tools and how these state‑level rules interact with HIPAA’s permissible uses and disclosures.
In addition, training should address operational workflows. Staff need to know how to use ambient documentation tools, clinical decision support systems, and revenue cycle automation platforms safely and appropriately. This includes understanding what information may be entered into these tools, how to review outputs, and when to escalate concerns. Training should also reflect role‑based access controls so that staff understand which AI tools they are permitted to use.
To support the compliant use of AI, workforce training should include the following best practices:
- Only use approved AI platforms. Do not enter PHI into any tool that has not been authorized by the organization.
- Fully de‑identify PHI before AI input whenever possible. Remove names, dates, contact information, and any other identifiers unless the task requires identifiable data.
- In all other cases, standardize minimum‑necessary inputs. Provide only the information needed for the task and avoid including extraneous details.
- Ensure you obtain consent when required. Some state laws or organizational policies require explicit consent before using AI for certain types of information or processing.
- Log AI interactions for auditing. Follow organizational procedures for documenting how AI tools are used so that outputs can be reviewed and any issues investigated.
- Always review and validate AI outputs before use. Never assume an AI‑generated summary, recommendation, or explanation is correct without checking it against the source information.
- Document decisions influenced by AI. When AI contributes to a clinical or operational decision, record what prompts were used, what outputs were generated, and how the outputs were validated.
- Flag anomalies, unexpected behaviors, and inaccurate outputs. Reporting these issues helps the organization identify patterns, improve tools, and prevent future errors.
- Never use AI to answer a HIPAA compliance question. Compliance questions must be directed to the organization’s privacy or compliance team, not to an AI system.
HIPAA AI training for healthcare staff should be scenario‑based, practical, and relevant to workforce members’ roles. Staff need to understand not only the rules but also the real‑world situations where errors occur. Organizations should provide concrete examples of how AI tools can produce incorrect, misleading, or incomplete outputs.
Seeing how AI gets it wrong in realistic scenarios reinforces the importance of validating AI‑generated content and encourages the vigilance needed to use these tools safely. Training should also be updated as AI tools evolve so that staff remain familiar with new features, changes in workflows, and updated organizational policies.
The post HIPAA, Healthcare Data, and Artificial Intelligence appeared first on The HIPAA Journal.
