{"id":12841,"date":"2026-04-22T23:42:13","date_gmt":"2026-04-22T23:42:13","guid":{"rendered":"https:\/\/medical-article.com\/?p=12841"},"modified":"2026-04-22T23:42:13","modified_gmt":"2026-04-22T23:42:13","slug":"how-are-payers-and-providers-using-ai-and-why-isnt-ai-reducing-administrative-cost","status":"publish","type":"post","link":"https:\/\/medical-article.com\/?p=12841","title":{"rendered":"How are payers and providers using AI? And why isn\u2019t AI reducing administrative cost?"},"content":{"rendered":"

The Peterson Health Technology Institute (PHTI<\/a>) recently released a report titled: \u201cAdministrative AI: Current Use and Potential Impact<\/a>.\u201d The report was based on a workshop PHTI convened with senior leaders from health systems, health plans, technology developers, investment firms, and federal agencies in order to discuss how technology and policy can enable AI to reduce administrative costs, accelerate payment cycles, and promote appropriate high-value care. Here are is a summary of the consensus findings from the PHTI workshop: <\/p>\n

Prior Authorization<\/strong><\/h3>\n

AI may reduce the cost for individual organizations to execute prior authorizations, but it has not reduced overall system-level costs.<\/em>Real-time prior authorization at the point of care is an emerging model, but current proofs of concept are narrow and not yet scalable.<\/em>Data standards and digitization of medical policies can reduce information asymmetry, but AI\u2019s impact is limited by variation across medical policies.<\/em>AI is exposing and exacerbating fundamental issues within the underlying prior authorization process.<\/em><\/p>\n

Medical Billing<\/strong><\/h3>\n

Provider deployment of AI is increasing billing intensity and inflating medical spending.<\/em>Health plans are beginning to respond to AI-driven increases in billing intensity with across-the-board downcoding and other reimbursement reductions, but the impact of these cuts is not yet known.<\/em>Reimbursement policy is the strongest lever to drive administrative efficiencies and system-level cost savings.<\/em><\/p>\n

How can it be the case that AI is making prior authorization review faster for payers and providers, but health system cost is not going down? <\/p>\n

There are a number of reasons. First, AI enables additional coding comprehensiveness (i.e., upcoding). Upcoding would increase the payer cost for the same services, and also potentially increase patient out-of-pocket cost for the same service simply due to coding differences. Second, if provider AI is getting better, there may be fewer prior authorization rejections (good for patients!), but that would lead to increased health system costs as payers deny fewer services. Third, the most clinically complex cases often still needed to be decided by individuals making tough decisions. Thus, AI is helping most to automate the simplest (but least costly) prior authorization cases; the more complex ones are still complex. Fourth, savings from increased efficiency may be partially or wholly offset by the cost of AI itself. Fourth, while cost per prior auth is going down, the volume of interactions needed is going up. One provider, talking about the back-and-forth of prior authorization requests, stated: <\/p>\n

AI tools are flooding the system. Bots don\u2019t get tired of asking questions, so my review queue keeps growing.\u201d<\/p>\n

What prior authorization policy issues are relevant to AI? <\/p>\n

The CMS Interoperability and Prior Authorization Rule (CMS-0057-F) requires Medicare Advantage, Medicaid fee-for-service, Medicaid managed care, Children\u2019s Health Insurance Program(CHIP), and federally facilitated Marketplace plans to implement a prior authorization application programming interface (API) by January 1, 2027\u2026The rule also establishes decision timeframes, requiring health plans to respond to urgent prior authorization requests within 72 hours and nonurgent requests within seven days<\/p>\n

A more fundamental question is why is prior authorization administratively complex in the first place?<\/p>\n

The PHTI provides some guidance:<\/p>\n

Varying prior authorization requirements<\/strong>: There are more than 5,000 procedure codes requiring prior authorization across four major U.S. insurers, and only 3% of those codes require prior authorization across all four payers.Varying medical necessity policies<\/strong>: Definitions of medical necessity are inconsistent across plans; clinical guidelines and procedural steps required to demonstrate medical necessity vary by plan; and within a health plan, determinations can vary based on the discretion and interpretation of individual reviewers.Benefit design and intermediaries<\/strong>: Employers and plan sponsors shape benefits packages, and intermediary vendors (for example, clinical criteria vendors) control the intellectual property of medical policies.Enrollment data lag<\/strong>: Prior authorization requires verifying patient enrollment in a health plan, but delays in enrollment data availability limit providers\u2019 ability to confirm coverage status at the point of care.Data mismatches<\/strong>: Inconsistent or erroneous data across health plan and provider systems (for example, provider names, addresses, plan names) are common, and many mismatches are due to manual input.Manual submission process<\/strong>: Only 40% of prior authorization transactions are automated, so the majority still rely on manual, phone- or fax-based workflows.EHR workflow limitations<\/strong>: Electronic health record (EHR) vendors control most workflows used to identify patient coverage and match requests to health plan policies, reducing providers\u2019 ability to directly access payer-specific prior authorization requirements at the point of care.<\/p>\n

You can read the full report here<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"

The Peterson Health Technology Institute (PHTI) recently released a report titled: \u201cAdministrative AI: Current Use and Potential Impact.\u201d The report was based on a workshop PHTI convened with senior leaders from health systems, health plans, technology developers, investment firms, and federal agencies in order to discuss how technology and policy can enable AI to reduce…<\/p>\n","protected":false},"author":0,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-12841","post","type-post","status-publish","format-standard","hentry","category-articles"],"_links":{"self":[{"href":"https:\/\/medical-article.com\/index.php?rest_route=\/wp\/v2\/posts\/12841"}],"collection":[{"href":"https:\/\/medical-article.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/medical-article.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/medical-article.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=12841"}],"version-history":[{"count":0,"href":"https:\/\/medical-article.com\/index.php?rest_route=\/wp\/v2\/posts\/12841\/revisions"}],"wp:attachment":[{"href":"https:\/\/medical-article.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12841"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/medical-article.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12841"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/medical-article.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12841"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}