Prior authorization has usually meant that doctors or office staff send proof to insurance companies. They do this before a service or medication can be given. This often involves sending faxes, making phone calls, and using several complicated payer websites.
The American Medical Association (AMA) says:
These numbers show how much work prior authorization adds for healthcare workers. For example, one big hospital system spends $17.5 million every year just to handle these requirements. Another group of 20 hospitals has 24 full-time staff working only on prior authorization. Spending time and money like this takes attention away from caring for patients.
Also, patients in the U.S. often wait longer to get needed treatments because of prior authorization. Sometimes these delays last from days to weeks and can seriously affect health. A study of cancer patients showed 70% had delays from prior authorization. One-third had delays up to a month, which might increase death risk by 13% for some cancers.
Many problems come from no clear standards and poor communication between providers and insurers. Often, healthcare providers do not know if prior authorization is needed or what papers to send. This leads to sending requests again and again.
This makes providers spend too much time sending, re-sending, explaining, and calling again. It also causes more denials and extra work, which delays patients from getting treatments on time.
This problem is worse in fields like cancer care and imaging. For example, prior authorization for chemotherapy often needs a lot of paper forms and phone calls. Getting approval for imaging tests can also be slow, leading to late diagnoses.
Recently, regulators and industry groups have worked to change prior authorization rules. They want to cut down paperwork and help patients get care faster.
The Centers for Medicare & Medicaid Services (CMS) made a new rule in 2024, called the Interoperability and Prior Authorization Final Rule. This rule asks for standard Application Programming Interfaces (APIs) and data formats for electronic prior authorization, or ePA. It aims to make health records, payers, and providers work better together.
Important updates include:
Even with these rules, many insurers still use older HIPAA X12 278 standards. This slows down new technology use. The American Hospital Association (AHA) wants CMS to fully accept FHIR-based transactions without needing to convert to old standards. This change could make prior authorization easier and reduce mistakes and costs.
Groups like the AMA and Medical Group Management Association (MGMA) keep pushing for national standards for automation. They also want better teamwork between payers and providers and ongoing training to lower delays caused by prior authorization.
New technology like automation and artificial intelligence (AI) helps fix problems with old prior authorization methods. Automation cuts down on tasks like entering data, making documents, and checking status. AI helps by understanding messy clinical data and supporting decisions.
Connecting ePA systems with electronic health records (EHRs) lets requests be sent automatically. It also gives real-time updates and checks if patients qualify. This reduces human mistakes and saves time.
The Council for Affordable Quality Healthcare (CAQH) says that using full electronic PA systems could save providers more than $350 million every year. Practices using ePA say they spend less time on processing, talk better with insurers, and give patients care faster.
Some companies, like RISA Labs, created platforms such as the BOSS system. BOSS breaks big prior authorization requests into small pieces and lets AI handle them at the same time. This cuts approval time a lot. At one big cancer center, using BOSS cut times from 30 minutes down to less than 5 minutes. It freed up 80% of staff time and helped approve over $1 million in medications within months.
Other AI tools use Natural Language Processing (NLP) to pull important info from patient records automatically. They create medical paperwork faster and more accurately, which reduces delays. For example, Stanson Health’s ImagingAssure works inside EHRs to simplify imaging prior authorizations by collecting proof based on medical guidelines automatically.
Advanced analytics show patterns in prior authorization approvals and denials. This helps providers learn what each payer wants and prepare correct documents. Real-time tools can give instant advice during visits about coverage and prior authorization needs. Doctors can start the process early and avoid waits.
RPA tools copy human actions for repeated tasks. They can read forms using optical character recognition (OCR), check information, and send requests. PCH Health shows how this works by handling over one million claims daily with less than 24-hour turnaround. Their system lowered pending claims by over 30% and improved payer-provider work while following privacy laws.
Medical offices and IT teams can gain many benefits from using automation and AI in prior authorizations:
To successfully add automation for prior authorizations, medical offices should keep these in mind:
AI offers new abilities in automating prior authorization beyond just typing data. Large Language Models (LLMs), like those in other healthcare AI tools such as Tempus One, can read and understand messy clinical notes and patient records. They pull out important information for prior authorization requests.
This helps automation handle requests with different and complex rules that used to need a lot of human review. AI can also flag or speed up urgent cases so they meet CMS rules that urgent requests be answered within 72 hours. Integration with EHRs using FHIR standards lets providers check and send information instantly during patient visits.
In cancer care, where prior authorization is very complex, AI tools shorten delays from weeks down to minutes. This lowers risks for patients and helps providers work better and spend less. Examples include RISA Labs’ BOSS system and Atlas Health’s Atlas Auth.
Healthcare organizations using AI-based prior authorization tools can run more smoothly, follow changing rules, and improve experiences for both doctors and patients. Industry groups and regulators keep pushing for electronic and standardized systems to help more places use these tools.
Automating prior authorizations is not just a technical update. It is an important change in U.S. healthcare management. With ongoing challenges in handling prior authorization, medical offices that use new tools can work better, save money, and most importantly, give needed care to patients faster.
Tempus One is a generative AI assistant by Tempus AI, Inc. that provides AI-enabled services for physicians and researchers, facilitating data-driven decision support and advancing research in precision medicine and patient care.
Tempus One offers several capabilities, including patient trial matching, creating patient timelines from health records, automating prior authorization processes, and enabling data exploration from unstructured datasets.
The patient query feature analyzes structured and unstructured data to identify and enroll patients in clinical trials, matching them with appropriate treatments based on their health information.
The patient timeline feature utilizes generative AI to compile disparate health records into a cohesive timeline, presenting clinical events, diagnostic results, and treatment changes for individual patients.
Tempus streamlines the prior authorization process by automating the gathering of necessary guidelines and patient information, creating customized support documents to facilitate timely treatment coverage.
Tempus enables researchers to query de-identified curated datasets and unstructured data efficiently, providing rapid insights that were previously difficult to obtain, such as adverse events and symptoms.
Tempus has introduced new AI capabilities that allow clinicians and researchers to derive insights from unstructured data and automate various processes, enhancing both clinical care and research efficiency.
Both clinicians and researchers benefit from Tempus One’s features as they address the needs of personalized patient care and expedite research efforts to develop new therapies.
Large language models (LLMs) in Tempus One are adapted to analyze unstructured healthcare data, providing insights that enhance decision-making in clinical care and research.
The strategic vision for Tempus One focuses on the continuous evolution and scaling of its AI capabilities to meet the evolving needs of healthcare professionals and improve patient outcomes.
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