AI Authorizations Software - Risk vs Reward Analysis

Summary:  Artificial intelligence (AI) promises to simplify the frustrating world of prior authorization, but it also introduces new risks. This live blog explores how authorizations became a fixture of American healthcare, why the volume keeps climbing, and how AI fits into the picture. It examines both the rewards—streamlined workflows, faster approvals and cost savings—and the risks, including data breaches, algorithmic bias and wrongful denials. Throughout the article you’ll find specific examples, timelines and regulatory context so providers and payers can better navigate the evolving landscape.

Why does Prior Authorization Exist?

Prior authorization (PA) is often blamed for creating unnecessary barriers to care, yet it was introduced with noble intentions. Utilization reviews emerged in the early 1960s, when Blue Cross plans began reviewing medical necessity before reimbursing hospitals. By the 1970s, the HMO Act encouraged gatekeeping and required pre‑admission certification for hospital stays. In the 1980s and 1990s, managed‐care organizations expanded PAs to cover imaging, elective surgeries, and brand‑name drugs. The goal was to curb spiraling costs and encourage appropriate use of high‑cost services.

Payers still argue that PAs reduce unnecessary care and keep premiums affordable. Studies estimate that Medicare Advantage (MA) insurers made almost 50 million PA determinations in 2023—about 1.8 per enrollee—and that 6.4 percent were denied. In theory, such gatekeeping helps control costs, but the low appeal rate (only 11.7 percent of denials were appealed) and high overturn rate (81.7 percent of appeals were overturned) suggest that many denials are questionable. Critics argue that a system designed to curb overuse can easily morph into a tool for rationing and profit.

Sources: Medical Economics, Evidence Care

Prior Authorization Growth - Impact on Providers

The relentless growth of authorizations

Several forces have pushed PA from a niche review process to a central administrative burden:

• Rising utilization and complexity. As medical technology advances, expensive therapies and imaging proliferate. By 2023, MA insurers processed nearly 50 million PAs, up from 37 million in 2021. Behavioral health services, maternal health, and complex imaging volumes grew sharply in 2025, squeezing payer review capacity.
• Administrative pressure and oversight. Providers completed an average of 43 prior authorizations per physician per week in 2024 and spent 12 hours on the paperwork. Ninety‑five percent of physicians said PAs increase burnout. Oversight bodies now scrutinize not just turnaround times but the rationale and consistency of decisions.
• Policy and regulatory shifts. CMS finalized rules requiring urgent PA decisions within 72 hours and standard approvals within seven days beginning in 2026. Most Medicaid and MA plans must implement automated electronic PA systems by 2027. Several insurers are voluntarily cutting the number of services needing authorization and implementing “gold card” programs to exempt providers with high approval rates.
• Emerging AI tools. Starting in 2024, AI engines entered PA workflows faster than organizations could develop governance. In 2025, about 50 insurers pledged to adopt electronic submission standards and reduce PAs, and states such as California began prohibiting AI‑only coverage decisions.

The human toll: impact on providers and patients

The numbers only hint at the frustration. In the AMA’s 2024 survey, physicians reported completing 43 PAs per week and spending 12 hours on them. This administrative drag leads to more than stress:

Providers describe the process as demoralizing. Many peer‑to‑peer consultations involve reviewers with little clinical similarity; only 15 % of physicians reported speaking with an appropriate peer. The result is wasted time, duplicate paperwork and treatment delays that erode patient trust.

AI Prior Authorization Software is Exploding

The AI PA Options for Providers

AI authorization software is often marketed as a magic bullet that will “eradicate denials” and “eliminate manual work.” Marketing materials highlight the benefits but rarely discuss the limitations. For example:

• Real‑time decisions as a differentiator. Abridge and Availity promote their integration as enabling real‑time approvals by exchanging clinical data via FHIR. What’s less clear is how often the AI recommends denial or requests more documentation, and whether clinicians can appeal the recommendation.
• Cost savings and efficiency claims. Simbie AI advertises 24/7 voice agents that can reduce PA costs by up to 60 %. While these savings may be possible, they assume providers have clean data and integrated systems; small practices may not see the same results.
• Top‑vendor rankings. Blog posts and webinars list the “top five AI vendors” without disclosing commercial relationships. Such content can blur the line between independent analysis and marketing.

To be fair, AI tools do offer real rewards. Automating data gathering, verifying benefits, and routing requests to the right payer could free clinicians from hours of phone calls. But the promise of frictionless automation depends on high‑quality data, transparent algorithms, and robust governance.

The AI PA Options for Payers

AI authorization software is often marketed as a magic bullet that will “eradicate denials” and “eliminate manual work.” Marketing materials highlight the benefits but rarely discuss the limitations. For example:

• Real‑time decisions as a differentiator. Abridge and Availity promote their integration as enabling real‑time approvals by exchanging clinical data via FHIR. What’s less clear is how often the AI recommends denial or requests more documentation, and whether clinicians can appeal the recommendation.
• Cost savings and efficiency claims. Simbie AI advertises 24/7 voice agents that can reduce PA costs by up to 60 %. While these savings may be possible, they assume providers have clean data and integrated systems; small practices may not see the same results.
• Top‑vendor rankings. Blog posts and webinars list the “top five AI vendors” without disclosing commercial relationships. Such content can blur the line between independent analysis and marketing.

To be fair, AI tools do offer real rewards. Automating data gathering, verifying benefits, and routing requests to the right payer could free clinicians from hours of phone calls. But the promise of frictionless automation depends on high‑quality data, transparent algorithms, and robust governance.

Where Automation Actually Becomes Safe: Rules, Governance, and Human Accountability

One of the most misunderstood aspects of automated prior authorization is where the “intelligence” truly lives. Platforms such as VEWS from PCG Software, when implemented in combination with HCIM, Key Software, or similar utilization and care management systems, can absolutely support automated authorization workflows on either the payer side or the provider side. However, the automation itself is not the decision-maker—the entity deploying it is.

In practice, VEWS acts as an execution and validation layer. It can evaluate authorizations programmatically, but only after the payer or provider defines the rules. Those rules are not generic. They must be explicitly designed to reflect contracts, coverage policies, medical-necessity criteria, utilization thresholds, benefit designs, clinical guidelines, and regulatory constraints. In simple terms, the logic must be deterministic: if A + B + C are present, and the contract allows X under Y conditions, then Z may be approved or denied. The software enforces what the organization defines—it does not invent policy.

This is where the real work happens, and where many AI authorization initiatives fail. Building compliant automation requires significant upfront effort from the deploying entity: cross-functional alignment between medical policy, compliance, legal, operations, and IT; rigorous testing against historical claims and authorization outcomes; and continuous monitoring as contracts, CMS rules, and payer policies evolve. Without that governance layer, automation simply accelerates risk.

When implemented correctly, however, this model flips the risk-reward equation. Instead of opaque, probabilistic AI making assumptions, organizations gain transparent, auditable, rules-based automation that can be defended during audits and regulatory reviews. The accountability remains with the payer or provider—exactly where regulators expect it to be—while the technology handles scale, consistency, and speed.



The takeaway is critical: compliant automation is not something you buy; it is something you design. VEWS and complementary platforms can enable automated authorizations, but only disciplined rule-setting, documentation, and oversight prevent automation from becoming a compliance liability rather than an operational advantage.

Risks Involved with AI Authorizations Software

1. Wrongful denials and patient harm



The highest‑profile warning comes from a class‑action lawsuit against UnitedHealth Group. A subsidiary used an AI tool (nH Predict) to decide whether Medicare Advantage members needed post‑acute care. According to the complaint, the system overruled treating physicians, had a 90 % error rate, and prematurely discharged patients. Many patients suffered harm after being sent home too early. Regulators reminded insurers that coverage decisions must not conflict with clinically accepted standards. The case illustrates how AI can magnify errors when human oversight is absent. Sources: Legathia

2. Algorithmic bias and discrimination



AI models learn from historical data, which may embed disparities. The Legal HIE article notes that biased models could flag older or disabled patients as “high‑cost” and deny them care. They might make biased predictions based on race, gender or socioeconomic factors if trained on non‑representative data. Regulators such as the FTC, DOJ and HHS OCR have signaled they will crack down on algorithmic discrimination.

3. Privacy and data‑security risks



AI systems need vast amounts of patient data. Misconfigured models can re‑identify patients, expose protected health information (PHI) or violate HIPAA. The Censinet report warns that AI errors can lead to data breaches and HIPAA penalties ranging from $141 to $2.1 million per violation. Vendors must implement encryption, anonymization and regular audits to mitigate these risks. Source: Censinet

4. Lack of transparency and “black‑box” decisions

Many AI tools operate as black boxes, making it hard to explain why a request was approved or denied. Under the 21st Century Cures Act, there is increasing pressure for explainable AI. If a hospital or insurer cannot justify an AI‑driven decision, it could face lawsuits and regulatory scrutiny.

5. Governance gaps

Adopting AI without robust oversight can backfire. BHM Healthcare Solutions noted that AI‑enabled tools entered PA workflows faster than organizations could standardize clinical rationale and governance. As a result, AI outputs began influencing decisions before policies were in place to ensure consistency and accountability. This governance gap can lead to inconsistent determinations and reputational damage. Source: BHMPC

6. Legislative uncertainty

Federal and state regulations are evolving. CMS allowed MA plans to use AI for PA but stressed that tools must comply with anti‑discrimination guidelines. In 2025 the agency declined to issue specific AI rules, while states such as California banned AI‑only decisions. Future legislation could either encourage responsible AI adoption or impose strict limits, creating uncertainty for vendors and payers. Source:  Beckers Payer

Navigating the Risk-Reward AI Auth Debacle

The AI PA Options for Providers

AI authorization software sits at the intersection of efficiency and ethics. To realize the benefits without repeating past mistakes, stakeholders should:

1. Adopt transparency as a core principle. Algorithms should produce explainable recommendations. Providers deserve to understand why an authorization is denied and what evidence supports the decision.
2. Prioritize diverse, high‑quality data. AI models must be trained on data that reflect diverse patient populations to avoid embedding bias.
3. Maintain human oversight. AI should assist, not replace, clinicians. Requiring human review for denials can prevent erroneous decisions.
4. Invest in governance and compliance. Organizations should create multidisciplinary oversight committees, include physicians in algorithm design, and audit AI outputs regularly. Compliance with HIPAA and anti‑discrimination laws is non‑negotiable.
5. Prepare for evolving regulations. Keep abreast of CMS rules, state laws and federal proposals like the Improving Seniors’ Timely Access to Care Act. Flexible systems will adapt more easily to new requirements.
6. Evaluate vendor claims critically. Look beyond marketing. Ask vendors to provide evidence of accuracy, error rates, audit trails and bias mitigation. Recognize that cost‑saving estimates may not materialize without clean data and process alignment.

Current Thoughts on AI for PAs

AI authorization software holds an undeniable promise: it can streamline administrative work, speed decisions, and allow providers to refocus on patient care. At the same time, high‑profile lawsuits and regulatory actions remind us that poorly governed AI can cause harm, embed bias, and erode trust. The challenge is not to embrace or reject AI outright but to harness it responsibly. With transparent algorithms, human oversight, strong governance, and a commitment to equity, the industry can realize the rewards of AI without repeating the mistakes of the past.