AI in Health Care Compliance: Risks, Data Analytics, and Safe Implementation

Government fraud investigators already use artificial intelligence (AI) to scrutinize billing patterns before claims are paid.^1,2 Inside your organization, similar technology is likely to influence which records receive attention and how staff interpret potential future compliance problems. These tools are so influential that, beyond accurately identifying problems, they must protect the patient information they process.

An online master’s in legal studies focused on health care compliance offers a foundation for weighing those decisions against the law. This post examines the legal risks and auditing uses of AI, followed by practical guidance for evaluating tools and overseeing their use.

Navigating AI in Health Care Legal Issues

Under the Health Insurance Portability and Accountability Act (HIPAA), patient privacy obligations extend to vendors handling protected health information (PHI) on your organization’s behalf. For example, a cloud provider that stores or processes those records is considered a business associate, even when the information is encrypted and the provider lacks the decryption key. Before sharing records with any entity, your organization needs a HIPAA-compliant business associate agreement.³

The agreement specifies how the vendor may use patient information and its responsibilities for safeguarding it. Any proposed use for model training must also comply with both the agreement and HIPAA’s Privacy Rule. When the business relationship ends, the agreement generally requires the vendor to return or destroy protected information, where feasible, and to protect any records it retains.³

Your organization also has its own security obligations. HIPAA requires covered entities and business associates to assess risks to electronic PHI. For an AI assistant, that assessment examines where patient records risk exposure and which safeguards address the vulnerabilities. The vendor might secure servers while your staff administers individual user accounts, with each party’s duties documented in written arrangements supporting HIPAA compliance.³

Beyond protecting the records, your organization needs to examine what the software does with them. For example, an AI model that makes clinical recommendations may be subject to Food and Drug Administration oversight as a medical device. Such oversight may not apply if clinicians can independently review the basis for the software’s recommendations and use their own judgment when making treatment decisions.⁴

For software involved in billing compliance, the legal concerns include whether an organization knowingly submits inaccurate government claims and whether the organization uses AI tools that inappropriately deny claims and appeals. While an isolated error does not establish liability, repeated AI-generated errors may trigger legal liability issues under the False Claims Act.⁵ If the government pursues an investigation, it will look to see whether employees were aware of these issues and yet continued submitting affected claims or processing the inappropriate denials.⁵

Using AI Data Analytics in Health Care Compliance

Medicare’s fraud detection program offers a documented example of AI-supported auditing, showing how investigators use automated findings to select claims for examination. Analysts compare providers’ billing with their peers, and some models use machine learning to flag unusual patterns for review.¹ The program also screens claims before payment, creating an opportunity to investigate suspicious activity before funds are disbursed.²

Within your organization, AI in health care claims processing could flag questionable entries before submission. Reviewers would examine the supporting documentation and correct confirmed errors before submitting those claims. The technology’s value depends partly on whether its alerts direct staff to real problems, with few false alarms that distract from its purpose.

Following an alert, reviewers will examine the supporting documentation and correct confirmed errors before those claims reach a payer. Their findings would also explain why a flagged claim was corrected or accepted as accurate, preserving the reasoning for subsequent audits.

Conducting an AI Compliance Risk Assessment

Your reviewers might catch inaccuracies before they reach a claim, but they lose that opportunity when workflows submit claims automatically without human review. The National Institute of Standards and Technology (NIST) offers a voluntary framework for examining such risks in your AI compliance risk assessment.⁶

Your team assesses how patient information is collected, used by the AI service, and ultimately stored or deleted. This assessment should also examine where patients may be exposed and how effectively existing safeguards protect them. The approving official can then weigh the likelihood and severity of harm against the proposed protections, document any unresolved risks, and assign them to someone responsible for addressing them.⁶

Before approval, testing needs to establish whether the tool performs reliably under the conditions your assessment identifies. Cases drawn from the patients and claims your organization handles allow evaluators to examine both missed errors and valid entries flagged unnecessarily. For overall accuracy, review results for each group separately so that smaller groups with errors don’t get missed.⁶

For a generative assistant, reviewers also need to examine the evidence behind its answers. AI can provide convincing explanations and citations that are entirely wrong, and even knowledgeable employees may not recognize the errors. As a result, frequent checks are necessary.

Best Practices for Implementing AI in a Compliance Program

Employees need a clear reporting route when they question an AI-generated answer, even if they cannot determine whether it is wrong. To address this, the federal government has created voluntary guidance. It recommends job-specific training and accessible channels for raising concerns.⁷

A designated specialist could investigate reports and document findings, including whether recurring errors warrant restricting or suspending the tool.^6,7 It is also essential, before the rollout of any new AI programs, to establish who can authorize a suspension and how staff will continue essential work while a problem is investigated. Having an agreed-upon manual fallback plan will allow operations to continue until the tool is ready to return to use.⁷

Master Health Care Regulation With Pitt Law

In a compliance leadership role, you may oversee vendor evaluations and technology policies that affect patient privacy.

In the University of Pittsburgh School of Law’s Online Master of Studies in Law (MSL) with a Health Care Compliance specialization, coursework in compliance program development and professional ethics examines how organizations put legal obligations into practice. The online format lets you continue your career while building the legal knowledge to lead compliance initiatives.

Pitt Law’s Health Care Compliance curriculum prepares you to work with artificial intelligence as it transforms the health care industry. You’ll explore the compliance implications of AI-driven developments such as AI-assisted diagnostics, clinical documentation and note-taking, patient monitoring systems, automated claims review, and AI-informed treatment protocols. Staying current with legal and technological developments is critical, and your coursework reinforces this. You’ll leave the program equipped to guide your organization through the evolving health care compliance landscape as AI adoption accelerates.

Bring informed judgment to the decisions affecting your organization and its patients. Reach out to an admissions outreach advisor to discuss your goals and the admissions process for Pitt Law’s Online MSL.