Prior authorization is a step used by health insurance companies to decide if planned medical treatments, tests, or medicines are covered by a patient’s insurance before they happen. The main aim is to stop unnecessary or unsafe services and control costs. But in reality, this process often causes delays in care. According to the American Medical Association (AMA), more than 90% of U.S. doctors say prior authorization delays block needed treatment. Nearly 24% have seen delays cause serious harm to patients, such as going to the hospital, lasting damage, or death.
The process is often done by hand. It includes lots of paperwork, phone calls, and back-and-forth communication between healthcare providers and insurance companies. For medical practice administrators, this means a heavy administrative load, many staff hours spent on insurer requirements, and more chances of mistakes or denials.
Because administrative costs in U.S. healthcare are very high—about $950 billion each year—there is a strong need to fix inefficiencies in prior authorization. Artificial intelligence (AI) offers solutions, but there are many problems to solve before it can be fully used.
Putting AI technology into healthcare practices, especially for prior authorization, is not just about installing software. Medical offices, especially smaller clinics, face technical and infrastructure problems that slow down AI use.
Many healthcare groups have only a small IT staff and a tight budget. Smaller offices often do not have special IT teams to manage AI setup, support moving data, or fix software problems. Buying AI tools without good internal IT help can mean the technology is not used well or fails.
Healthcare data in the U.S. exist in many formats and systems that often do not work well together. This creates problems for connecting systems. AI tools need to access and compare electronic health records (EHRs), insurance databases, and clinical documents, but different data standards slow this down.
Robert Chhugani points to the need for good digital platforms to help collect data and make decisions. Without smooth data sharing, AI cannot improve prior authorization much.
Handling protected health information (PHI) needs strict rules to follow laws like HIPAA. Healthcare providers must make sure AI systems keep patient data private while using sensitive information. These rules make the process harder and require trust in AI vendors.
Prior authorization steps differ a lot between insurance companies and states. AI needs to be customized to fit different plan rules and medical standards. This makes adapting AI more complicated for healthcare administrators and IT staff.
Besides technical problems, healthcare organizations face other challenges when trying to use AI for prior authorization.
Adding AI to current workflows means teaching staff new steps and handling resistance. Many providers are unsure or do not fully understand AI. Some fear losing jobs or making mistakes with new technology.
Alivia Kaylor, MSc, says that fast and useful prior authorization is needed, but success with AI depends on users accepting it.
AI can automate routine parts of prior authorization, but healthcare leaders say human judgment must stay part of decisions. Dr. Jeremy Friese, CEO of Humata Health, suggests AI should approve some requests but not outright deny them. If a case is unclear, people need to review it to avoid wrong denials.
Friese imagines a system where AI approves requests it is confident about. More complex decisions stay with humans. This type of system takes teamwork between providers and payers and time to set up properly.
Prior authorization is closely linked to billing and revenue processes. WhiteSpace Health says more than 1,300 clients use AI tools to improve revenue management and handle denials better. But matching prior authorization AI with current systems needs careful planning to avoid problems. IT managers must check that AI tools work well with financial software.
Good AI results need good, clean data. McKinsey & Company notes that slow AI use in healthcare is partly due to the effort needed for data cleaning and integration. Fixing and standardizing health data before AI can use it is a big task.
Despite these problems, new AI developments offer practical ways to make prior authorization work better in medical practices.
Anterior (formerly Co:Helm), an AI company built by clinicians, is developing generative AI that does more than predict outcomes. It includes clinical reasoning from the start. This means AI understands the medical context, making automated prior authorization more accurate and useful.
This change lets AI workflows run smoothly and quietly, reducing paperwork for providers. Anterior recently raised $20 million in funding, showing trust in this approach.
Fred Camacho of HealthcareGPS AI highlights the need to combine AI with design focused on humans. Their platform uses AI to make health systems easier to work with and to automate workflows while keeping a human touch. This mix, called augmented intelligence, keeps providers central in care decisions while AI does routine tasks like documentation and data entry.
AI systems can now give patients and providers live updates on prior authorization status. This helps lower confusion and frustration for patients waiting for care decisions. It also helps administrators better manage expectations.
Gathering and grouping the right clinical data for insurance approval is a slow, manual job. AI can do this quickly and accurately, speeding up prior authorization requests.
AI can help connect providers and payers by clearing up what documents are needed and reducing misunderstandings. Sending clear and consistent data with AI lowers the back-and-forth delays in authorization reviews.
For healthcare groups in the U.S., moving past common problems needs careful planning and smart choices.
Before using AI, offices need to check their IT setup. Hiring or contracting IT experts with experience in healthcare AI is important. Small practices might work with health IT vendors who offer special support for AI tools in prior authorization.
To avoid big disruptions, administrators should test AI in small areas or departments first. This lets them find workflow problems, training needs, and data issues before rolling it out everywhere.
Clear communication about what AI does and doesn’t do can ease staff worries. Training should focus on how AI helps staff rather than replaces them. It should stress improving daily tasks instead of risking jobs.
AI companies must follow privacy laws and be open about rules and controls. Organizations should work with firms that support ethical AI use and balanced human oversight, like the model Dr. Friese suggests.
Choose AI tools that fit smoothly with existing EHR and billing systems. Practices should ask AI vendors to show they can handle different data standards common in U.S. healthcare.
Working closely with insurers helps AI adoption because both sides will use matching digital systems and agree on approval rules. This can lower denial rates and improve communication.
By understanding IT limits and the hard parts of implementing AI, medical practices in the U.S. can get closer to using AI to improve prior authorization. Though challenges remain, mixing AI into current workflows while keeping human judgment can help patients get care faster and reduce the burden on staff. With good planning and teamwork across healthcare groups, providers, payers, and AI vendors, prior authorization can become less complicated and quicker.
Prior authorization, intended to ensure appropriate medical service use, has been criticized for causing delays in patient care, which can lead to adverse health outcomes.
AI can optimize workflows for both providers and payers by automating clinical documentation compilation and enhancing review efficiency, leading to faster access to treatments.
Providers often hesitate due to IT resource availability, implementation challenges, and change management complexities.
The use of AI may lead to increased denial rates for care requests, raising concerns about unjustified denials and potential bias in decision-making.
Friese advocates a model where AI can approve requests but not deny them outright, ensuring that human review is retained for unique cases.
AI streamlines data submission, enabling providers to send only necessary information and allowing payers to process requests more efficiently.
Friese envisions that 90% of prior authorizations could be processed without human intervention, while maintaining oversight for complex cases.
By reducing misaligned expectations and clarifying required documentation, AI fosters more effective collaboration, reducing frustration for both parties.
Successful integration needs thoughtful governance, seamless collaboration, and a balance between automation and human oversight.
AI can enhance transparency by providing patients with real-time updates on their prior authorization status, which can build trust and reduce uncertainty.
Simbo is using AI agents to automate all phone related workflows. Connect now to know more.
Schedule AI Agents Demo Now© Since 2019, Simbo AI.
