Healthcare payers and providers have different goals and use different data systems, which often make communication hard. Providers focus on patient health and clinical notes. Payers look at financial and claims data to control costs. Because of these differences, information is exchanged slowly and sometimes incorrectly. Many tasks are done by hand, which takes extra time.<\/p>\n
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One common problem is the prior authorization process. This process is meant to make sure medical services are suitable and cost-effective. But it often causes delays, more paperwork, and frustration for both doctors and patients. More than 90% of doctors say these delays hurt patient care. Almost 24% have seen serious problems from these delays, like hospital stays or worse.<\/p>\n
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These problems happen because prior authorization needs many manual steps. Doctors must send clinical papers, wait for payers to check them, answer extra questions, and get final approval. Doctors can spend up to two workdays a week on this, which keeps them from spending time with patients. Also, payer and provider systems don\u2019t easily share updates, which makes the process slower and more confusing.<\/p>\n
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Artificial intelligence and automation can help fix communication problems between payers and providers. AI can quickly analyze lots of clinical and financial data, find missing information, check if requests follow payer rules, and give real-time advice or alerts. This cuts down on manual work and speeds up decisions.<\/p>\n
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An example is a tool made by Baptist Health, Rhyme, and Availity. They created an AI-powered review system for prior authorizations that works with the Epic Electronic Health Record (EHR) system, which many U.S. medical practices use. This tool checks clinical notes against payer rules and usually approves 80% of cases on the first try, often in 90 seconds. Baptist Health lowered manual reviews by half for diagnostic imaging and saved money by cutting three full-time staff jobs.<\/p>\n
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Also, AI helps give almost real-time updates on authorization status. This makes it easier for doctors and patients to know where their requests stand. Faster updates help scheduling and treatments happen sooner and with more confidence, improving care.<\/p>\n
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Even though AI helps speed up processes, using it in healthcare needs good rules and checks. Some reports say AI might reject care requests much more than usual, up to 16 times higher. To keep things fair, experts like Dr. Jeremy Friese suggest that AI should approve requests automatically but let humans handle denials. This way, complex cases get proper attention, and patient safety is protected.<\/p>\n
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Another issue is that many providers don\u2019t have enough IT help, find it hard to set up AI, or resist change. Many healthcare groups hesitate to use AI fully because they don\u2019t fully understand what it can do. To fix this, clear communication, training, and fitting AI inside existing workflows are needed so it does not cause big disruptions.<\/p>\n
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Healthcare communication is also improving by creating platforms and systems that let payers and providers share data in real time. More places use industry standards like Fast Healthcare Interoperability Resources (FHIR) APIs. These help clinical data, eligibility, benefits, claims, and authorizations move accurately and quickly.<\/p>\n
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With better sharing, there is less repeated data entry and fewer mistakes. Both sides can expect the same information. A 2025 report by KLAS shows these tools can cut prior authorization approval times by up to 99%. They also help keep provider directories correct and automate patient registration. Baptist Health\u2019s use of AI inside the Epic system shows that putting AI tools in familiar programs helps staff take them up faster.<\/p>\n
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Sharing data and performance openly also helps support value-based care, where payers and providers share goals and results. When both see the same data and work together, care coordination gets better, extra tests get reduced, and patient care gaps close.<\/p>\n
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AI and automation speed up healthcare processes and make communication clearer between providers and payers.<\/p>\n
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The communication problem between payers and providers also comes from differences in how they work. Payers often have separate IT systems and communication ways that don\u2019t match those of providers.<\/p>\n
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Secure, real-time systems that connect both sides are needed to close this gap. These systems let users send messages, share data analytics, and coordinate work. This creates transparency and better understanding for both sides. For example, Aetna and NovaHealth’s work together cut hospital days by 50% and saved money for Medicare Advantage patients.<\/p>\n
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Social collaboration platforms include doctors, payers, and patients to share information clearly. This lowers repeated tests and billing problems. Having shared, accurate data helps care teams better support people with long-term diseases and improve health outcomes.<\/p>\n
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AI tools that help communication between payers and providers improve more than just operations. Faster prior authorization leads to quicker scheduling and treatment. This helps patients get better care and feel happier. Patients also know the status of their requests, which eases worries from waiting.<\/p>\n
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Doctors spend less time on boring paperwork and can focus more on patients. Payers see fewer mistakes in claims and save money from automated, rule-based reviews.<\/p>\n
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As healthcare moves toward payment models that focus on value, AI-powered communication tools become important for managing risk, coordinating care, and improving health at the population level.<\/p>\n
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The smart use of AI and automation in healthcare communication is changing how payers and providers work together. It breaks down old barriers and improves both operations and patient care across medical practices in the United States.<\/p>\n 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.<\/p>\n<\/p><\/div>\n<\/details>\n AI can optimize workflows for both providers and payers by automating clinical documentation compilation and enhancing review efficiency, leading to faster access to treatments.<\/p>\n<\/p><\/div>\n<\/details>\n Providers often hesitate due to IT resource availability, implementation challenges, and change management complexities.<\/p>\n<\/p><\/div>\n<\/details>\n The use of AI may lead to increased denial rates for care requests, raising concerns about unjustified denials and potential bias in decision-making.<\/p>\n<\/p><\/div>\n<\/details>\n Friese advocates a model where AI can approve requests but not deny them outright, ensuring that human review is retained for unique cases.<\/p>\n<\/p><\/div>\n<\/details>\n AI streamlines data submission, enabling providers to send only necessary information and allowing payers to process requests more efficiently.<\/p>\n<\/p><\/div>\n<\/details>\n Friese envisions that 90% of prior authorizations could be processed without human intervention, while maintaining oversight for complex cases.<\/p>\n<\/p><\/div>\n<\/details>\n By reducing misaligned expectations and clarifying required documentation, AI fosters more effective collaboration, reducing frustration for both parties.<\/p>\n<\/p><\/div>\n<\/details>\n Successful integration needs thoughtful governance, seamless collaboration, and a balance between automation and human oversight.<\/p>\n<\/p><\/div>\n<\/details>\n AI can enhance transparency by providing patients with real-time updates on their prior authorization status, which can build trust and reduce uncertainty.<\/p>\n<\/p><\/div>\n<\/details><\/div>\n<\/section>\n","protected":false},"excerpt":{"rendered":" Healthcare payers and providers have different goals and use different data systems, which often make communication hard. Providers focus on patient health and clinical notes. Payers look at financial and claims data to control costs. Because of these differences, information is exchanged slowly and sometimes incorrectly. Many tasks are done by hand, which takes extra […]<\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[],"tags":[],"class_list":["post-150175","post","type-post","status-publish","format-standard","hentry"],"acf":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/posts\/150175","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/comments?post=150175"}],"version-history":[{"count":0,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/posts\/150175\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/media?parent=150175"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/categories?post=150175"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/tags?post=150175"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Frequently Asked Questions<\/h2>\n
What is the primary challenge with prior authorization in healthcare?<\/summary>\n
How can AI improve prior authorization processes?<\/summary>\n
What are some common roadblocks to AI adoption in prior authorization?<\/summary>\n
What ethical concerns exist regarding AI in prior authorization?<\/summary>\n
What governance model does Jeremy Friese propose for AI use in prior authorization?<\/summary>\n
How can AI help bridge the information gap between payers and providers?<\/summary>\n
What future vision does Friese have for the integration of AI in prior authorization?<\/summary>\n
How does AI enhance collaboration between providers and payers?<\/summary>\n
What is required for the successful implementation of AI in prior authorization?<\/summary>\n
How can patients benefit from AI-driven prior authorization processes?<\/summary>\n