Medical necessity determinations are decisions made by insurers and healthcare groups to check if a treatment or service is right for a patient based on medical evidence. These decisions affect whether patients can get care, how much it costs, and how services are used.
Recently, healthcare providers and payers have started using AI to help with utilization management and prior authorization processes. AI can look at lots of data, check if a treatment meets guidelines, and speed up responses to prior authorization requests. The Centers for Medicare & Medicaid Services (CMS) see this trend and have made policies to allow AI to help, but not replace, decisions made by licensed clinicians.
The CMS Interoperability and Prior Authorization final rule, which begins January 1, 2027, requires payers to use a Prior Authorization API to speed up decisions—within 72 hours for urgent cases and seven business days for standard ones. AI can help meet these deadlines, but humans must still review the decisions to keep them medically accurate and ethical.
Using AI in medical necessity decisions is watched closely by federal and state rules to protect patients, prevent unfairness, and make sure AI is used responsibly in healthcare.
Together, these laws show that AI should be a tool to help humans, not replace them. Human review is needed to keep decisions fair, clear, and reasonable.
AI can use data to help with decisions, but human clinical reviewers do work AI cannot. They look at individual patient situations, understand complex medical details, and make sure decisions follow laws and ethics.
Here are some reasons why human reviewers are still needed:
Healthcare leaders and IT staff who work with AI in medical necessity face several challenges to follow the rules:
AI is also used to automate work in medical offices, like patient communication, prior authorization requests, and follow-ups.
The CMS Interoperability and Prior Authorization rule says payers must use a Prior Authorization API by 2027. This allows faster sharing of data with providers. AI can help process requests, check data, and give updates in real time.
Companies like Simbo AI use AI for phone automation and answering services. This helps reduce routine work in healthcare offices. AI can handle calls and follow-ups, lowering mistakes and saving staff time. This is important because responses must happen quickly – within 72 hours for urgent cases and 7 days for standard ones.
Automation speeds up clerical tasks, but all medical necessity decisions still need human clinical review. Reviewers check AI’s findings or look closer at cases flagged by the system. This mix helps get things done faster without losing accuracy or fairness.
Even with automation, organizations need to watch out for:
Regular checks keep automation fair and clear, and make sure human judgment stays key.
Regulators suggest healthcare groups set up oversight teams with legal, clinical, technical, and ethics experts for AI use.
This oversight team:
This type of oversight helps avoid breaking rules and builds AI systems that match patient care goals.
Medical practice managers and IT staff have many tasks in using AI while following rules:
They help build systems and rules that keep providers responsible while using AI to improve efficiency.
The use of AI in medical necessity decisions in the United States is increasing to help decision-making. But AI helps only; it does not make final decisions. Laws at all levels require that licensed human clinical reviewers take part. This protects patients, keeps decisions fair, avoids bias, and follows rules. Healthcare organizations and tech providers like Simbo AI that use AI for workflow automation and answering services must make their tools support licensed clinical judgment, not replace it. This way, they can meet rules and make healthcare work better both in the clinic and behind the scenes.
The Medicare Program; Contract Year 2024 Policy and Technical Changes to the Medicare Advantage Program final rule issued by CMS mandates that Medicare Advantage organizations ensure medical necessity determinations consider the specific individual’s circumstances and comply with HIPAA. AI can assist but cannot solely determine medical necessity, ensuring fairness and mechanisms to contest AI decisions.
Effective by January 1, 2027, this rule requires payers to implement a Prior Authorization Application Programming Interface (API) to streamline the PA process. Decisions must be sent within 72 hours for urgent requests and seven days for standard requests. AI may be deployed to comply with timing but providers must remain involved in decision-making.
Signed on October 30, 2023, it mandates HHS to develop policies and regulatory actions for AI use in healthcare, including predictive and generative AI in healthcare delivery, financing, and patient experience. It also calls for AI assurance policies to enable evaluation and oversight of AI healthcare tools.
Examples include Colorado’s 2023 act requiring impact assessments and anti-discrimination measures for AI systems used in healthcare decisions; California’s AB 3030 requiring patient consent for AI use and Senate Bill 1120 mandating human review of UM decisions; Illinois’ H2472 requiring clinical peer review of adverse determinations and evidence-based criteria; and pending New York legislation requiring insurance disclosures and algorithm certification.
Plans must navigate varying state and federal regulations, ensure AI systems do not result in discrimination, guarantee that clinical reviewers oversee adverse decisions, maintain transparency about AI use, and implement mechanisms for reviewing and contesting AI-generated determinations to remain compliant across jurisdictions.
Regulations emphasize that qualified human clinical reviewers must oversee and validate adverse decisions related to medical necessity to prevent sole reliance on AI algorithms, assuring fairness, accuracy, and compliance with legal standards in UM/PA processes.
AI systems must be tested on representative datasets to avoid bias and inaccuracies, with side-by-side comparisons to clinical reviewer decisions. After deployment, continuous monitoring of decision accuracy, timeliness, patient/provider complaints, and effectiveness is critical to detect and correct weaknesses.
Insurers and healthcare providers should disclose AI involvement in decisions to patients and providers, including how AI contributed to decisions, ensuring individuals are informed and entitled to appeal AI-generated determinations, promoting trust and accountability.
Engagement with regulators, healthcare providers, patient groups, and technology experts helps navigate regulatory complexities, develop ethical best practices, and foster trust, ensuring AI in UM/PA improves decision quality while adhering to evolving standards and patient rights.
Continuous review of regulatory changes, internal quality assurance, periodic audits for algorithm performance, adherence to clinical guidelines, and responsiveness to complaints are necessary to ensure AI systems remain compliant, fair, and effective in prior authorization and utilization management.
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