Latest news with #priorauthorization
Forbes
27-07-2025
- Health
- Forbes
Changes In Prior Approval Coming To Traditional Medicare, Medicare Advantage
There were two major announcements recently regarding prior approval of treatments and services for Medicare beneficiaries. In most medical insurance, many treatments won't be covered unless it is approved first by the insurer. It's been a source of controversy for some time. Original Medicare hasn't required prior authorization of treatments and services, with a few exceptions. For most care, providers and the patient agree on a treatment. After the treatment, paperwork for approval and payment is submitted to Medicare. Medicare recently announced a new model program that will test pre-approval. The voluntary model program will test pre-approval for some services and treatments, according to a recent announcement from the Center for Innovation of the Centers for Medicare and Medicaid Services. The model program is seeking medical providers to volunteer for the program from Jan. 1, 2026 through Dec. 31, 2031. The model will be restricted to New Jersey, Ohio, Oklahoma, Texas, Arizona, and Washington. Providers who volunteer and are accepted will agree to seek prior authorization for 17 items and services, including skin substitutes, deep brain stimulation for Parkinson's Disease, impotence treatment, and arthroscopy for knee osteoarthritis. A provider who volunteers for the program can choose not to seek prior approval for a case. There will be a post-treatment review of the case, and the provider will risk not being paid by Medicare for the treatment. CMS initiated the program and selected the services to be covered because of a series of reports showing waste, fraud or abuse in certain areas. For example, Medicare spent up to $5.8 billion in 2022 on unnecessary or inappropriate services that had no clinical benefit, according to the Medicare Payment Advisory Commission. Under the model, providers will submit the same information they currently submit for payment approval after a service is provided to a beneficiary. The difference is that under the model, the information will be submitted earlier and the provider will wait for approval before performing the services. CMS will select companies to receive and review the prior authorizations. It expects that they will use artificial intelligence and other tools in addition to medical professionals to review the submissions. The companies will be paid based on the extent to which they saved the government money by stopping unnecessary services. CMS said it will manage the program to avoid adverse impact on beneficiaries and providers. There was other news about pre-approval, this time involving Medicare Advantage plans. Pre-approval in Medicare Advantage plans has been controversial recently. There have been a number of recent reports and studies that found the authorization process was delaying treatment or causing patients to abandon treatment plans. Other reports indicated that a high percentage of treatments that initially were denied coverage eventually were approved if the patients or their providers appealed the than 50 major insurers who sponsor many types of insurance plans announced that they will voluntarily streamline prior authorization of treatments and services in all insurance markets, including Medicare Advantage plans. The insurers say they plan to have the new process in place by Jan. 1, 2027.
Associated Press
27-07-2025
- Business
- Associated Press
Major Health Insurers Slash Prior Authorization Requirements, Transforming the PA Technology Landscape
Black Book Research identifies Cohere Health, Innovaccer, and Waystar among leading vendors rapidly adapting to new industry rules. NEW YORK CITY, NY / ACCESS Newswire / July 26, 2025 / U.S. healthcare is undergoing a pivotal shift as major insurers-led by UnitedHealthcare and Humana-begin to significantly reduce or eliminate prior authorization (PA) requirements for hundreds of routine procedures. Accelerated by federal policy, provider frustration, and consumer demands for timely access to care, these sweeping changes signal a new era in PA technology and operations, according to a July 2025 flash survey conducted by Black Book Research. Industry Drivers: Regulatory Action Meets Provider and Consumer Pressure Insurers covering over 250 million Americans have committed to streamlining or removing PA burdens by the end of 2026. This is partly driven by the Centers for Medicare & Medicaid Services (CMS), which is launching a pilot program in six states in January 2026 requiring faster, more transparent prior authorizations for select Medicare services. CMS has also announced national response time standards for Medicare Advantage plans, further intensifying the need for automation and interoperability in PA processes. Key Survey Insights from the Field Black Book Research's flash survey compiled viewpoints from: 24 IT leaders representing the top 10 PA vendors; 108 managed care and health plan IT and operational decision-makers; 142 healthcare providers and administrative leaders; and 100 healthcare consumers with recent PA experiences. Notable Findings: 84% of managed care executives support reducing PA requirements 96% of healthcare providers report improved workflows and lower administrative burdens 99% of consumers favor eliminating PA for routine care; 83% say they've experienced harmful care delays 67% of health plans expect to reevaluate or end contracts with existing PA vendors by 2026 Additional Observations: 90% of providers foresee broad adoption of interoperable PA tools by 2027 94% of payers plan substantial investment in AI-based PA platforms 100% of consumers prefer providers with automated and transparent PA processes 96% of PA vendor executives acknowledge their current solutions require modernization within two years __________ Vendors Rapidly Adapting and Leading the Innovation Curve: Client Top KPI Scores Black Book highlights the top-performing vendors already making critical advancements to align with industry shifts: Cohere Health - Excels in AI-based automation, payer-provider integration, and CMS-aligned interoperability Innovaccer - Offers strong EHR integration and regulatory compliance dashboards for PA workflows Waystar - Enhancing its Auth Accelerate platform for real-time eligibility checks and exception handling ScribeRunner - Developing dynamic auto-approval rulesets and real-time tracking modules CoverMyMeds - Expanding AI-powered real-time authorizations for both pharmacy and medical benefits Change Healthcare - Transitioning legacy infrastructure with modular FHIR APIs for automated decision-making Availity - Driving advanced API adoption and digital submission channels PriorAuthNow (Rhyme) - Connecting providers and payers through real-time electronic submission with limited manual effort Black Book's Q1-Q2 client satisfaction rankings show these vendors excelled across 18 qualitative KPIs for PA technology. Cohere Health earned the highest overall honors, with MCG Health, eviCore Healthcare, Agadia, Infinx, and Availity also receiving good marks. Onyx led in FHIR-based PA platform innovation. Detailed competitive intelligence reports are available in the Black Book research store. __________ Vendors Facing Existential Threats in the New Era Not all companies are poised for success. Several previously top-rated PA vendors now face considerable risk due to outdated systems and slow adaptability: eviCore Healthcare - Still dependent on manual review processes, with limited AI capabilities HealthHelp (WNS) - Lagging behind in interoperability and modern payer integration PriorAuthNow (Rhyme) - Despite innovation efforts, struggles with scalable real-time API integration threaten its long-term viability _________ Looking Ahead: A Positive Outlook for Adaptive Vendors While legacy vendors must evolve rapidly or risk market exit, the broader outlook for PA tech is optimistic. Companies investing in automated, intelligent, and interoperable systems are well-positioned to thrive. 'The future of prior authorization is transparent, automated, and fully integrated into clinical workflows,' said Doug Brown, Founder of Black Book Research. 'Vendors delivering real-time, AI-powered solutions will define the next generation of care access efficiency for providers, payers, and patients alike.' About Black Book Research Black Book Research is a leading healthcare IT research firm known for its independent, vendor-agnostic approach. Over the past 15 years, Black Book has collected over 3 million survey responses from nearly 500,000 healthcare professionals. The firm's flash surveys and long-form evaluations provide real-time, unbiased insights that support strategic decision-making across the healthcare ecosystem. Visit or contact [email protected] for full survey results and vendor-specific performance details. Contact InformationPress Office 8008637590 SOURCE: Black Book Research press release
Yahoo
26-07-2025
- Business
- Yahoo
Major Health Insurers Slash Prior Authorization Requirements, Transforming the PA Technology Landscape
Black Book Research identifies Cohere Health, Innovaccer, and Waystar among leading vendors rapidly adapting to new industry rules. NEW YORK CITY, NY / / July 26, 2025 / U.S. healthcare is undergoing a pivotal shift as major insurers-led by UnitedHealthcare and Humana-begin to significantly reduce or eliminate prior authorization (PA) requirements for hundreds of routine procedures. Accelerated by federal policy, provider frustration, and consumer demands for timely access to care, these sweeping changes signal a new era in PA technology and operations, according to a July 2025 flash survey conducted by Black Book Research. Industry Drivers: Regulatory Action Meets Provider and Consumer Pressure Insurers covering over 250 million Americans have committed to streamlining or removing PA burdens by the end of 2026. This is partly driven by the Centers for Medicare & Medicaid Services (CMS), which is launching a pilot program in six states in January 2026 requiring faster, more transparent prior authorizations for select Medicare services. CMS has also announced national response time standards for Medicare Advantage plans, further intensifying the need for automation and interoperability in PA processes. Key Survey Insights from the Field Black Book Research's flash survey compiled viewpoints from: 24 IT leaders representing the top 10 PA vendors; 108 managed care and health plan IT and operational decision-makers; 142 healthcare providers and administrative leaders; and 100 healthcare consumers with recent PA experiences. Notable Findings: 84% of managed care executives support reducing PA requirements 96% of healthcare providers report improved workflows and lower administrative burdens 99% of consumers favor eliminating PA for routine care; 83% say they've experienced harmful care delays 67% of health plans expect to reevaluate or end contracts with existing PA vendors by 2026 Additional Observations: 90% of providers foresee broad adoption of interoperable PA tools by 2027 94% of payers plan substantial investment in AI-based PA platforms 100% of consumers prefer providers with automated and transparent PA processes 96% of PA vendor executives acknowledge their current solutions require modernization within two years __________ Vendors Rapidly Adapting and Leading the Innovation Curve: Client Top KPI Scores Black Book highlights the top-performing vendors already making critical advancements to align with industry shifts: Cohere Health - Excels in AI-based automation, payer-provider integration, and CMS-aligned interoperability Innovaccer - Offers strong EHR integration and regulatory compliance dashboards for PA workflows Waystar - Enhancing its Auth Accelerate platform for real-time eligibility checks and exception handling ScribeRunner - Developing dynamic auto-approval rulesets and real-time tracking modules CoverMyMeds - Expanding AI-powered real-time authorizations for both pharmacy and medical benefits Change Healthcare - Transitioning legacy infrastructure with modular FHIR APIs for automated decision-making Availity - Driving advanced API adoption and digital submission channels PriorAuthNow (Rhyme) - Connecting providers and payers through real-time electronic submission with limited manual effort Black Book's Q1-Q2 client satisfaction rankings show these vendors excelled across 18 qualitative KPIs for PA technology. Cohere Health earned the highest overall honors, with MCG Health, eviCore Healthcare, Agadia, Infinx, and Availity also receiving good marks. Onyx led in FHIR-based PA platform innovation. Detailed competitive intelligence reports are available in the Black Book research store. __________ Vendors Facing Existential Threats in the New Era Not all companies are poised for success. Several previously top-rated PA vendors now face considerable risk due to outdated systems and slow adaptability: eviCore Healthcare - Still dependent on manual review processes, with limited AI capabilities HealthHelp (WNS) - Lagging behind in interoperability and modern payer integration PriorAuthNow (Rhyme) - Despite innovation efforts, struggles with scalable real-time API integration threaten its long-term viability _________ Looking Ahead: A Positive Outlook for Adaptive Vendors While legacy vendors must evolve rapidly or risk market exit, the broader outlook for PA tech is optimistic. Companies investing in automated, intelligent, and interoperable systems are well-positioned to thrive. "The future of prior authorization is transparent, automated, and fully integrated into clinical workflows," said Doug Brown, Founder of Black Book Research. "Vendors delivering real-time, AI-powered solutions will define the next generation of care access efficiency for providers, payers, and patients alike." About Black Book Research Black Book Research is a leading healthcare IT research firm known for its independent, vendor-agnostic approach. Over the past 15 years, Black Book has collected over 3 million survey responses from nearly 500,000 healthcare professionals. The firm's flash surveys and long-form evaluations provide real-time, unbiased insights that support strategic decision-making across the healthcare ecosystem. Visit or contact research@ for full survey results and vendor-specific performance details. Contact Information Press Office research@ SOURCE: Black Book Research View the original press release on ACCESS Newswire Error while retrieving data Sign in to access your portfolio Error while retrieving data Error while retrieving data Error while retrieving data Error while retrieving data
Harvard Business Review
23-07-2025
- Business
- Harvard Business Review
Should Your Business Use a Generalist or Specialized AI Model?
The prevailing wisdom in artificial intelligence suggests that bigger models yield better results. In our work for health insurance companies and third-party administrators of health plans, we've discovered that this assumption holds in many cases but breaks down when AI moves from general tasks to specialized professional domains. We have built generative AI systems for prior authorization: the process that health insurers employ to determine whether a doctor's recommended treatments are covered by the patient's policy. In this article we share what we have learned about how executives evaluating investments in AI for professional problem-solving applications should decide between generalist AI models and specialized ones. When Scale Delivers the Greatest Value Well-known generative AI offerings like ChatGPT, Claude, and Gemini are built on large language models (LLMs) and other AI technologies that are trained on text and images from countless domains and therefore seem capable of answering almost any question imaginable. As tempting as it might be for business leaders to actually rely on these generalist models to do just that, however, it's critical to recognize where their broad capabilities create the greatest competitive and operational advantages. Simply put, these systems excel at many enterprise applications precisely because they don't specialize. Their value derives from their ability to synthesize information broadly across domains, make unexpected connections, and handle the full spectrum of business communications. They can simultaneously draw on legal precedents, technical specifications, and customer psychology. For organizations seeking to both deepen and accelerate the creativity and productivity of content teams, generalist models offer unmatched versatility. Leaders are well advised to think of them as sophisticated generalists—AI versions of the most valuable utility players on the team—and employ them as such. In contrast, specialized generative AI models understand not just what information to retrieve but also how that information operates within a specific domain's decision-making framework. These generative AI technologies add highly contextualized training data and methodologies to the core capabilities of generalized AI technologies. The result is that they can generate far more intelligent and accurate outputs in specific domains like healthcare and finance than would be possible for generalist AI models. For example, a specialized model designed to assist a physician making a treatment decision must not only know what aspects of a patient's current clinical status and medical history are relevant but also be able to identify appropriate treatment protocols and the strength of the evidence supporting that protocol. When AI Must Think Like Experts First things first: No one in their right mind would question whether generalist models are valuable; they clearly are. When leaders rely on specialized solutions for tasks better suited to generalist models, they waste resources and set the stage for weak performance across important domains. Meanwhile, misapplying generalist AI to specialized professional domains isn't just inefficient; it can create liability, regulatory violations, and an erosion of trust among stakeholders and the public more broadly. We should know, because we nearly fell into this very trap. When we first applied generalist models to prior authorization, we were confident that their capabilities would translate well. But we quickly encountered a fundamental mismatch between how these systems process information and how healthcare professionals actually make decisions. This challenge reflects what Martin Reeves and Mihnea Moldoveanu recently described as 'dataism': the false belief that gathering ever more data and feeding it to ever more powerful algorithms alone can help businesses make optimal decisions. Our experience building healthcare AI taught us that this approach breaks down precisely where it matters most: in the nuanced, contextual reasoning that defines professional expertise in medicine and across industries. Prior authorization requires mapping complex clinical presentations to equally complex insurance policies—a process that involves understanding not just what information is present but also how to interpret that information within specific medical and regulatory frameworks. It's not enough, for example, to understand that a patient with a Stage 2 lung cancer diagnosis would qualify for a specific chemotherapy under an insurer's policy; the system would also need to account for other medical conditions—say, end-stage renal disease accompanied by a recent hospice referral—that would impact a treatment or coverage decision. This mirrors challenges across multiple professional domains: Legal teams must map case facts to relevant precedents, financial advisors must align client circumstances with regulatory requirements, and engineers must connect design specifications to safety standards. To solve a prior authorization problem, a generalist AI model would seek to find statistical patterns between symptoms and approval decisions, but this pattern-matching approach misses the underlying clinical and policy logic that drives these decisions—especially with more complex cases like the one mentioned above—just as it would miss similar considerations in legal, financial, and engineering realms. The breakthrough idea, for us, came in the form of a question: 'Why would we try to make the AI think like a computer when it needs to think like a doctor?' Put another way, we observed that effective professional AI requires understanding not just what information to retrieve but also how that information operates within a specific clinical and insurance coverage framework. This insight led us to replace a pattern-matching approach with one that starts by understanding the prior authorization policy criteria, then searches clinical documents for the specific evidence a clinician would use. We trained our generative AI agents to follow how clinicians read—understanding the structure of charts, moving from sections to subsections, and identifying the right findings in context. To maintain clinical precision, we built specialized agents for distinct tasks, avoiding the cross-specialty overlap that general models often introduce. What Enterprise AI Solutions Must Deliver This shift represents more than a technical refinement; it's a fundamentally different philosophy. Rather than retrieving text and hoping the model parses it correctly, effective specialized solutions extract structured professional facts first, then apply domain-specific reasoning to those facts. Consider how experienced physicians approach a complex case. They don't simply pattern-match symptoms to diagnoses; they systematically evaluate specific clinical criteria, understand how different findings interact, and apply established medical practices to reach conclusions. Clinicians are also able to intuitively and effectively grasp context and, perhaps more importantly, discern irrelevant or out-of-context information. The most effective specialized AI solutions mirror this structured approach rather than relying on the more impressionistic reasoning of generalist large language models. The broader principle here extends well beyond healthcare. Enterprise decision-making across the other domains we've referenced—and many others—involves precise interpretation of domain-specific data points, not just general language understanding. The thing about generalist models, however is that they're democratic in the worst possible way. They treat all information equally, potentially weighing irrelevant details as heavily as crucial professional findings. Why Transparency Beats Performance in Enterprise Contexts Perhaps our most important observation was that in many enterprise contexts, AI applications must offer transparent reasoning for their outputs—something large general models struggle to provide. In healthcare, physicians, patients, and insurers need to understand not just what decision was made but also why specific evidence was prioritized and how different clinical factors were weighted. The same is true in a host of other domains such as structural engineering, financial advising, and legal decision-making. This transparency requirement also reflects a deeper truth about professional domains: It's not nearly enough to be right most of the time. Enterprises need to trace the logical chain from evidence to conclusion, identify potential weaknesses in reasoning, and understand how new information might affect decisions. As Joe McKendrick and Andy Thurai have noted, AI systems notoriously fail in capturing the intangible human factors—ethical, moral, and contextual considerations—that guide real-life professional decision-making. Generalist models, despite their impressive capabilities, remain largely opaque in their decision-making processes. We essentially had no choice, then, but to ensure our system generated explicit rationales for every decision, showing which clinical criteria were evaluated and how they mapped to specific policy requirements. This wasn't just about regulatory compliance; it became essential for earning physicians' trust. Transparent reasoning proved vital for enabling effective human-AI collaboration. Evaluating Adaptability to Enterprise Evolution Great leaders achieve that status largely because they're able to evolve strategies in lockstep with competitive and regulatory changes in their domain. Reliable AI solutions must be able to do the same; they must be able to perpetually recalibrate how new information affects existing frameworks. This is precisely the kind of contextual reasoning that generalist models find challenging, however. Specialized models, by contrast, are tuned to recognize domain-specific signals and understand when seemingly small shifts might have major implications for industry professionals. When evaluating AI vendors, leaders should prioritize those that understand the underlying structure of professional decision-making in their domain and can incorporate new information more effectively than approaches requiring complete model retraining. The same principle applies whether you're assessing solutions that understand how new legal precedents apply to particular factual circumstances or those that can quickly contextualize new financial regulatory changes rather than simply optimizing for returns. Hybrid Architecture as the Path Forward Our experience suggests that the future of enterprise generative AI implementation lies not only in choosing between generalist and specialized models but also in thoughtful hybrid implementation strategies. The most effective approaches leverage generalist models for tasks they excel at—e.g., customer service chatbots, content creation, document summarization, exploratory data analysis, and internal knowledge management—while relying on specialized vendors for domain-specific reasoning and decision-making in areas like regulatory compliance, health insurance, legal precedent analysis, and financial risk assessment. This hybrid approach allows enterprises to benefit from the broad capabilities of large models for routine business functions while accessing the specific professional logic that drives expert decision-making in high-stakes domains. Rather than replacing professional judgment, these solutions can enhance it by handling routine applications of established frameworks while flagging cases that require human expertise. How to Choose an AI Vendor and Avoid Common Pitfalls Given how well today's biggest LLMs perform across less-mission-critical tasks, it's hard for business leaders to make grave errors when choosing among them for such purposes. That's not the case when evaluating more specialized AI models, however. Through our experience, we've developed several key questions business leaders can ask during specialized AI vendor evaluations to avoid the most common pitfalls: Can the vendor clearly demonstrate the logic behind its AI and make its reasoning transparent? The critical question isn't whether AI can process information faster than humans but whether it can reason—i.e., use that information—in ways that your key domain experts can trust, understand, and build upon. This question helps avoid the trap of 'shallow transparency illusions,' which happens when vendors show which criteria were evaluated, for instance, but fail to capture deeper professional reasoning. Look for solutions that embed professional judgment frameworks, not just decision trees, and can explain why they prioritize specific evidence. Does the vendor maintain ongoing collaboration with domain experts in your field? How does it handle evolving practices? This requires deep collaboration between AI developers and domain experts—not just to gather training data but also to understand the underlying frameworks that guide professional decision-making. Success comes from vendors that embed this professional logic into their architecture rather than hoping their models will somehow discover it through pattern matching. This question ensures your vendor can adapt as professional standards change, while maintaining access to the domain expertise that drives continuous improvement. Can the vendor's proposed solution address c ross-domain integration and s cope expansion, or will its offering only function as a point solution? As researchers from McKinsey highlighted in HBR, the key to capturing AI's full potential lies in understanding and addressing the organizational and cultural barriers that AI initiatives face, rather than simply deploying more powerful general-purpose models. This question helps leaders choose vendors that anticipate how professional decisions interconnect across domains and ones that expand their capabilities as an organization's needs evolve. The competitive landscape for industry-specific AI may look quite different from the current focus on ever-larger generalist models. Companies that understand the unique reasoning patterns of specific professional domains and can partner with AI vendors that participate effectively in that reasoning will create more sustainable advantages than those competing purely on a model's scale. In high-stakes enterprise environments, specialized expertise trumps general intelligence—for humans and AI systems alike. Our journey building healthcare-specific AI taught us that the path to effective professional AI lies not in simply choosing bigger models but also in selecting smarter solutions that understand how professionals actually think, decide, and act. The lessons we've learned in healthcare authorization apply broadly: When AI moves from general capabilities to specific professional applications, architecture matters more than scale, transparency matters more than performance, and domain expertise matters more than computational power.
Yahoo
22-07-2025
- Business
- Yahoo
Humana to reduce about one-third of prior authorization requirements
(Reuters) -Humana said on Tuesday it would eliminate about one-third of prior authorizations for outpatient services by next year, the latest insurer to address the tedious paperwork process that has been a pain point for patients and providers. The company will remove the authorization requirement for diagnostic services across colonoscopies and transthoracic echocardiograms and select CT scans and MRIs by January 1, 2026. Insurers have been facing increased scrutiny and backlash over the lengthy paperwork required to carry out specific services or treatment. While the companies have said it ensures people receive required care and helps to keep track of costs, patients have argued that the long process leads to care delays or denials. "Today's healthcare system is too complex, frustrating, and difficult to navigate, and we must do better," said Humana CEO Jim Rechtin. The killing of the head of UnitedHealth's insurance unit last year had ignited significant social media backlash from Americans struggling to receive and pay for medical care. Several health insurers have since taken additional measures to simplify their requirements for prior approval on medicines and medical services. America's Health Insurance Plans, the industry trade group, said last month health insurers would work to develop standardized data and submission requirements for electronic prior authorization by January 1, 2027. UnitedHealth said in March it would ease requirements to get insurance authorization when renewing prescriptions on about 80 drugs, aiming to eliminate up to 25% of reauthorization requirements. Humana said it would report publicly its prior authorization metrics, including requests approved, denied, and approved after appeal and average time between submission and decision, in 2026. The company will also provide a decision within one business day on at least 95% of all complete electronic prior authorization requests, it said. Currently, it provides a decision within one business day on more than 85% of outpatient procedures, in which patients do not require an overnight stay in the hospital. Solve the daily Crossword
