As healthcare becomes increasingly shaped by AI-driven discovery, online reputation has evolved from a marketing consideration into a core component of patient acquisition, retention, and trust. Today’s patients are not only evaluating providers based on clinical quality, but also on the digital signals that shape first impressions — from online reviews and search visibility to the consistency and authenticity of patient feedback across platforms.
At the same time, healthcare organizations are navigating a delicate balance: embracing AI to improve efficiency and patient insights while preserving the human connection that defines quality care. The practices succeeding in this environment are those using technology to remove friction, uncover actionable feedback, and strengthen patient relationships — not replace them.
In this Q&A, Evan Steele and Austin Colvard of rater8 discuss how healthcare organizations can prioritize patient experience, maintain Net Promoter Scores above 90, improve online discoverability in the AI era, and build reputations that accurately reflect the quality of care they deliver.
Evan Steele
Questions for Evan Steele, Founder + CEO, rater8:
How do you help practices ensure that their online reputation matches their quality of care?
The gap between care delivery and online perception is still one of the biggest inefficiencies in healthcare. Most practices are delivering excellent care, but only a fraction of that experience makes it online. We focus on systematically capturing patient feedback at scale and structuring it in a way that search engines and AI systems can actually understand. When you consistently collect verified reviews and publish them in the right places, your online reputation starts becoming a true reflection of reality.
Is the Net Promoter Score (NPS) the most universally trusted measure of practice quality?
NPS is a useful directional metric, but it’s incomplete on its own. It tells you how people feel, not necessarily why they feel that way. In healthcare, context matters. A 90+ NPS is meaningful, but only if you can tie it back to specific patient experiences and operational drivers. The practices that perform best don’t just track NPS; they pair it with qualitative feedback and real-time insights to understand what’s actually driving loyalty.
What challenges does the “AI era” pose for practices?
AI is fundamentally changing how patients are finding and choosing providers. We’re moving from a world of “ten blue links” to one where AI-generated results are giving a single answer. If your practice isn’t represented accurately in the data those systems rely on, you effectively disappear from consideration.
How can practices use AI to help improve their online reputation?
The biggest opportunity with AI is accessibility. Historically, extracting insights from patient feedback required time, tools, and expertise. AI removes that barrier. You can ask simple questions like “What are patients frustrated about in the last 30 days?” and get immediate answers. That allows practices to move faster, close gaps sooner, and continuously improve the experience they’re delivering, which ultimately drives stronger reviews and better online visibility.
How can practices maintain a human connection with their patients as AI increasingly helps connect patients and providers?
AI should handle the operational friction, not replace the human moments that matter. Patients don’t remember how efficiently they booked an appointment, they remember whether they felt heard, respected, and cared for. The goal is to use AI to create more space for those interactions, not less. The practices that get this right use technology to streamline the background work so their teams can be more present where it matters most.
Austin Colvard
Questions for Austin Colvard, VP of Professional Services, rater8:
How do online reviews impact practice success and discoverability?
Online reviews play a direct role in both patient decision-making and visibility. Patients are using them to validate whether a practice is worth their time, and search platforms are using them to decide which practices to show. If reviews aren’t coming in consistently, you lose ground in both areas. Recency and volume show that the experience is still consistent today, and the testimonials have the added benefit of boosting the confidence a patient has in booking an appointment. The combination of all three maximize discoverability.
What data are patients and prospective patients most concerned with when it comes to selecting or staying with a practice?
Patients are typically drawn to listings with a strong volume of reviews, while also paying close attention to any negative feedback. The listings with the most reviews help build a foundation of trust in a provider or practice by creating a baseline assessment of the quality of care. From there, seeing some negative reviews acts like a sanity check that often reassures the potential patient that they are making the right decision. For more specific care, such as procedures, having reviews that reference those procedures is especially important as well.
Most patients start with the star rating, but they don’t stop there. They’ll usually read through a handful of reviews to get a better sense of what the experience is actually like.
What tends to stand out are patterns. Things like whether the provider takes time to answer questions, how the staff interacts with patients, and how smooth the overall visit feels. Wait times and communication come up pretty frequently as well. It’s less about clinical detail and more about whether the experience feels consistent and trustworthy.
Should practices put more emphasis on Answer Engine Optimization (AEO) and GEO (Generative Engine Optimization) than Search Engine Optimization (SEO) in today’s AI landscape?
It’s less about shifting away from SEO and more about building on it. The same foundational pieces still matter: accurate information, strong review presence, and consistent content. What’s changing is how that information gets used. AI tools are pulling from those same sources, just presenting them differently. So the focus should really be on making sure your data is complete, consistent, and easy to interpret in every place patients and AI tools are searching, especially review sites that were historically considered secondary to Google, such as Healthgrades, WebMD, and Vitals.
What are the first steps practices can take to improve their online reputation?
The first step is making sure you’re asking every patient for feedback in a consistent way. A lot of practices are only capturing feedback from a small portion of their patient base, which can skew things over time and tremendously hinder building AEO/GEO/SEO authority at a competitive rate.
From there, double-check that all listings are accurate and up to date across platforms. After that, I’d focus on how reviews are being handled. Responding consistently, especially to negative feedback, goes a long way in showing that the practice is paying attention, willing to improve, and open to taking service recovery measures.
What’s the most common misconception you hear from practices about managing their online reputation?
The most common misconception is that online reputation is something that can be fixed quickly or treated as a one-time effort. In reality, it reflects what’s happening day to day in the practice and compounds over time. The teams that see the most improvement are the ones that build it into their routine. They’re consistently collecting feedback, reviewing it, and making small adjustments over time. In turn, they have the most visibility and success attracting and retaining patients.
By Dr. Wael Khouli, MD, MBA | Co-Founder & Chief Medical Officer, Authsnap.
I want to be direct about something: the denial problem in U.S. healthcare is not complicated to understand. It is complicated to fix, but the basic dynamic is straightforward. Hospitals deliver medically appropriate care, submit claims, and then watch a meaningful portion of those claims get denied. Most of those denials are never challenged. Revenue disappears. And somewhere along the way, we collectively decided this was just how things work.
It isn’t. And we need to stop treating it like it is.
I spent years as a Chief Medical Officer and Medical Director of Case Management before co-founding Authsnap. In those roles, I sat in the middle of this problem every single day, watching clinicians re-justify care they had already delivered, watching revenue cycle teams triage which denials they had bandwidth to fight, and watching recoverable revenue get written off because the math on appealing a $400 claim didn’t make sense when it took three hours of staff time to pursue. I understood why those decisions were made. I also understood what they were costing us.
The Numbers Are Worth Sitting With
About 15% of all hospital claims are denied. Across the industry, that adds up to roughly $262 billion in unpaid claims annually. Hospitals then spend approximately $19.7 billion a year just on the administrative work of contesting those denials, chart review, documentation assembly, appeal drafting, and payer follow-up. And after all of that, a significant portion of denied revenue is still never recovered.
Here’s the part that gets me: around 70% of appealed claims can be successfully overturned. The denials aren’t mostly legitimate. Many of them are wrong. The care was appropriate. The documentation was there. But the appeal never got filed, because the team didn’t have time, or the filing window closed, or someone made a judgment call that the claim wasn’t worth the effort.
That gap between what could be recovered and what actually gets recovered is not a clinical failure. It is a workflow failure. A capacity failure. And, increasingly, it is a technology failure.
Why This Has Gotten So Much Harder
When I started in hospital administration, denials were manageable as exceptions. You had a team, you had a process, and while it wasn’t elegant, you could keep up. That is genuinely no longer true for most health systems, and I think it’s worth being honest about why.
Payer policies are not just more complex; they change more frequently, with less notice, and with greater specificity than they did even five years ago. Medicare Advantage, which now covers more than half of all Medicare beneficiaries, has brought a new layer of utilization management scrutiny that many organizations are still trying to get their arms around. Prior authorization requirements have expanded into services that were once routinely approved. And the documentation bar for medical necessity has been raised in ways that put real pressure on already-stretched clinical teams.
At the same time, the workforce capable of managing this complexity is shrinking. It takes years to develop a skilled appeals specialist. Turnover in revenue cycle roles is high. And the cognitive demand of the work is not trivial — effective appeals require reading clinical documentation carefully, interpreting payer-specific criteria, and building a structured, evidence-based argument. That is genuinely hard. The quality of an appeal written at the end of a long Friday looks nothing like one written fresh on Monday morning.
Meanwhile, most hospitals are navigating all of this with fragmented technology, manually toggling between EHR systems, PDFs, and payer portals that were not designed to talk to each other. The tools haven’t kept up with the complexity.
What’s Actually at Stake
I want to make the downstream consequences concrete, because I think they get abstracted away in conversations about revenue cycle.
When a hospital consistently loses revenue it legitimately earned, the effects are real and visible. Capital investment gets deferred. Services get reduced or eliminated. Staffing decisions get made under financial duress rather than based on patient need. The American Hospital Association reports that 149 hospitals have closed in the past decade due to financial pressures. Those closures have consequences for communities, particularly in rural and underserved areas where access is already limited.
There is also a direct toll on the clinical workforce. Clinicians who spend hours re-documenting care they already delivered, re-justifying decisions they already made, dealing with prior auth delays for treatments their patients need now, that friction accumulates. It is a real contributor to burnout, and it pulls physicians and nurses away from what they actually came to do.
And patients bear costs too. Delayed authorizations mean delayed care. Denied claims generate confusing bills and unexpected financial exposure. For patients managing serious illness, administrative uncertainty on top of clinical uncertainty is its own burden.
The Shift That’s Starting to Happen
The health systems making real progress on this problem have made one fundamental reframe: they stopped treating denial management as a staffing problem and started treating it as a data and workflow problem. That reframe matters because it changes what solutions are even on the table.
If you think the problem is that you don’t have enough appeals specialists, your solution is to hire more appeals specialists. And you will perpetually lose that race, because the volume and complexity of denials is growing faster than any organization can staff against it.
If you think the problem is that the work is too manual, too inconsistent, and too dependent on individual capacity at any given moment, you start looking at how to systematize it. You look at where AI can take on the extractive, repetitive labor, ingesting clinical records, mapping documentation against payer criteria, generating structured appeal arguments, so that the clinical expertise in your organization can be applied to judgment, oversight, and the cases that genuinely require human reasoning.
The goal is not to remove clinicians from the process. Appropriate clinical judgment matters enormously in appeals, and bad claims shouldn’t be appealed just because automation makes it easy to do so. The goal is to stop asking skilled professionals to do work that does not require their skill, and to stop writing off legitimate revenue because the team didn’t have capacity on a given Tuesday.
What Forward-Looking Organizations Are Doing Differently
The health systems getting this right share a few things in common that go beyond technology adoption.
They treat denial data as operational intelligence, not just a report. They track denial patterns by payer, by service line, by physician, by reason code, and they use that data to drive prevention upstream, before claims are submitted. A denial that never happens is better than a denial that gets appealed and overturned.
They’ve built feedback loops between revenue cycle and clinical operations. When documentation gaps are causing denials, the people generating the documentation need to know — specifically, consistently, and without it feeling like a compliance audit. That connection between clinical and administrative functions is often missing, and it is expensive when it is.
And they have stopped accepting inconsistency as inevitable. The quality of denial management should not vary based on who is available, how tired they are, or whether it’s the end of the month. Consistency requires process, and increasingly, it requires tools that enforce that process at scale.
A Final Word
None of this is any easy. Changing how denial management works inside a health system requires organizational will, cross-functional coordination, and a willingness to challenge assumptions that have been baked in for a long time. I have been in those rooms. I know how hard those conversations are.
But I also know what it costs to leave this problem unaddressed. The revenue is real. The operational burden is real. The impact on staff and patients is real. And the tools to do better exist right now.
We owe it to our institutions, our clinicians, and our patients to stop treating $262 billion in denied claims as an unavoidable cost of doing business, and start treating it as a solvable problem.
By April Miller, Senior Technology Writer, ReHack.
Care delivery has become less about providing clinical attention and more about coordination through healthcare automation. Hospitals need to move patients through intake, diagnosis, treatment, discharge and follow-up. Additionally, they must manage pressures on staffing, documentation, payer requirements and growing data volumes.
When viewed as infrastructure for workflow, AI can reformat otherwise disjointed operational data into actionable signals for executives.
Why AI Belongs in the Workflow Conversation
Healthcare workflow automation refers to eliminating time-consuming steps in processing. This could include multiple entries of the same data, several steps in which patient information is passed from one recipient to another, and delays in providing reports to others. In January 2025, more states had implemented automated feeds of near-real-time hospital bed capacity. The CDC noted that automated data exchange is saving time and costs.
Such automation is important because clinicians and administrators can only optimize what they can see. AI-enabled tools can analyze historical scheduling data, bed availability, staffing, patient acuity and discharge impediments to identify pinch points. With a click, people can access indicators such as days when patient flow slows. Leaders can act sooner to allocate resources, coordinate transportation or influence discharges.
The strongest use cases remove friction from clinical judgment. AI can identify work queues to prioritize, flag missing documentation, surface relevant patient history and recommend next best actions. Health IT teams can deploy these systems broadly within the existing EHR and communication workflows.
Where AI Improves Efficiency and Safety
During intake and triage, generative and ambient models can help route patients based on symptoms, risk factors and service availability. They can also draft notes for the clinician to review, preventing after-hours charting.
AI may also ease workflows for diagnosis or care management, such as identifying abnormalities on imaging, predicting readmission risk or identifying shortcomings upstream of preventive care. These systems perform better when they deliver explainable recommendations and involve clinicians at key points. Compared to a model that inundates a nurse with alerts, one that brings the patients most likely to need intervention to the forefront is helpful.
Central to this work is the exchange of data. Healthcare represents roughly 30% of the global volume of information, and hospitals will only see reliable workflow efficiency gains from AI once they have secure and scalable infrastructure. A high-capacity EHR exchange enables organizations to receive clinical summaries, referrals, event notifications and PHI from partners without manual processing.
How AI Can Create Savings Without Sacrificing Care
AI can save the most money safely by reducing waste. For example, predictive staffing models can make detailed predictions about shifts. Revenue cycle tools could flag coding, prior authorization or claim problems earlier in the process. Also, supply chain models could prevent costly stockouts and anticipate use.
If AI can help automatically identify patients who have discharge planning needs, such as transport, home health coordination or medication reconciliation, the hospital can address them sooner. Shorter delays may assist patient flow, reduce avoidable length of stay and create capacity to receive new patients.
Cost savings require successful implementation, and health systems should measure whether AI tools save work or shift the review workload. A tool is not optimized if it saves 10 minutes for one department but adds 20 minutes to another.
Governance Keeps Healthcare Automation Useful
The use of AI in care delivery would be improved by privacy, bias, security and accountability guardrails. Hospitals could evaluate model performance across populations and also set escalation paths and ownership for recommendations. EHR systems are responsible for 61.3% of diagnostic mistakes stemming from electronic health record systems, which can occur when teams become complacent.
Strong governance includes training staff. Clinicians need to know what an AI tool can do, what it cannot do and when to override it. IT teams should monitor drift, downtime and integration failures, and leaders should start with workflows that have obvious pain points, clear outcomes and limited risk.
Building a Smarter Care Delivery Model
AI works best to improve efficiency, safety and affordability of healthcare processes. It’s particularly helpful when it connects a physical flow of patients, data and decision-making in the real world and is combined with interoperability and governance and frontline input. After identifying bottlenecks, teams can measure burden before and after implementing solutions, testing iterations until they produce a workflow manageable for providers and patients.
There is a major transformation happening inside healthcare organizations across the country, and it is being led not just by technology, but by experienced operational leaders. Healthcare administrators are helping turn the promise of AI and integrated systems into measurable, patient-centered results by combining operational expertise with strategic execution.
The widespread adoption of electronic health records over the past decade was more than a technology milestone. It was a leadership challenge. Healthcare administrators managed complex implementations, aligned stakeholders across multi-site organizations, redesigned workflows, and helped modernize how patient information is captured and shared.
That experience created a generation of leaders who understand both the clinical environment and the systems supporting it. Leaders who know how to manage budgets, improve workflows, support adoption, and guide organizations through operational change without disrupting patient care. That foundation is critical as healthcare enters a new era of AI integration and advanced analytics.
Today’s healthcare leaders are addressing long-standing operational challenges such as fragmented systems, inefficient referral processes, and inconsistent communication across care teams. Strong administrators recognize these are not permanent barriers. They are operational problems that require operational solutions.
Across hospitals, clinics, and physician networks, healthcare administrators are overseeing system integrations, implementing intelligent workflow tools, and improving access to real-time operational data. These efforts help streamline documentation, improve reporting accuracy, support care coordination, and reduce administrative burdens on clinical staff.
The difference between a successful technology implementation and a failed one is rarely the software itself. More often, success depends on leadership, governance, and execution. Effective healthcare administrators establish clear implementation frameworks, define accountability early, standardize workflows, and invest in training that supports long-term adoption.
Equally important, they communicate effectively throughout the process. Strong leaders bring clinical teams, operational departments, and executive stakeholders together early to ensure alignment and long-term success. In complex healthcare environments, that level of coordination is essential.
The strongest healthcare administrators also understand that technology initiatives are ultimately about improving the patient experience. Reduced delays, stronger care coordination, better visibility into outcomes, and improved communication between providers all directly impact the quality of care patients receive.
Healthcare is entering one of its most operationally complex periods, but it is also one of its most promising. AI, integrated platforms, and advanced analytics are creating new opportunities to improve efficiency and strengthen care delivery. The organizations that succeed will be those led by administrators who understand how to implement these tools responsibly, strategically, and with patients at the center of every decision.
Melissa A. Corneal, MBA, is an Operational Program Manager specializing in healthcare operations, EHR implementation, and technology integration. She is a member of the American College of Healthcare Executives and the Project Management Institute.
Healthcare is built on patient engagement. But when that engagement breaks down, patients don’t get the care they need, and regional practices are hit with the dual loss of time and money.
Patient no-shows and late cancellations remain one of the biggest and most persistent revenue leaks for regional practices. Each year, no-shows cost U.S. healthcare providers roughly $150 billion. Regional practices that lack the scale of larger health systems are hit especially hard by this loss of money and efficiency.
But no-shows don’t have to be a consistent revenue drain. Regional practices are redefining the patient experience by adopting automated, two-way communication systems. These systems can boost patient engagement, save practices time and money, and ensure patients get the care they need.
Old Systems, New Expectations
No-shows often come down to unmet patient expectations. So much of our world has become seamless. Think about the last time you booked a flight or shopped for a product online. The process is easy and intuitive. Healthcare systems, however, have not kept pace with changing expectations.
We see this in a national no-show rate of 18%. Patients expect personalized, streamlined experiences, but healthcare communication systems don’t meet these expectations.
According to research, traditional healthcare communications fail patients in three important ways. First, patients feel overwhelmed by communications, yet they still often don’t get the specific information they need. Next, patients feel frustrated when they have to call in response to an appointment reminder. And finally, patients feel messages are not meant for them personally. The same research has shown long waiting times, seeing different doctors for appointments, transportation issues, and fear of hospitals are among the most common reasons for no-shows. Other reasons include scheduling conflicts and miscommunication with medical staff.
The bottom line is that healthcare is struggling to keep up with modern patient expectations. If your cellphone carrier can send personalized communications when you need them the most, why can’t your doctor?
This is why many regional practices are turning to automated communication systems to close the gap between patient expectations and experience.
Automate Engagement, Boost Appointment Adherence
We’ve seen the results when healthcare practices shift from one-way reminder calls and manual follow-ups to two-way, automated, multi-channel communications. When regional practices adopt an automated, proactive approach to patient communication, appointment adherence improves and no-show numbers drop, strengthening patient-provider connections. These communication systems tailor reminders to individual patients using their preferred language and communication channel.
The results are clear. Studies show that telephone reminders can reduce no-shows from 21% to 7%. Reminders are even more effective when they are personalized and delivered when and how individual patients need them the most.
Research has also shown how practices can enhance patient reminders by mixing up content and format, simplifying information, and including specifics such as clinic locations and contact information. Most importantly, the study recommends personalizing reminders to individuals and letting patients control the reminders.
AI-powered engagement systems can help regional practices reach patients in their preferred language using their preferred channel, whether that is a text, phone call, or email. Automated systems also let patients cancel, ask questions, or reschedule at their convenience, reducing the communication barriers between patient and practice.
Automated patient management systems are an efficient way for regional practices to give patients exactly what they need to keep their appointments. Reminders that include specific information about tests or procedures can help alleviate anxiety that cause no-shows. Data analysis can also help practices tailor messages with specialized information for patients who might need a greater level of engagement to keep appointments.
Patients aren’t the only ones benefiting from automated engagement. Staff no longer have to spend valuable time making reminder calls, leaving voicemails, or contacting patients after a no-show. They are free to focus on the more complex parts of their jobs best suited to their training and expertise.
Stronger Patient Engagement, Better Care for Everyone
Reducing no-shows creates a positive ripple effect across regional practices. When more patients keep their appointments, they get the care they need. When practices’ schedules are more stable, they can see more patients, increase efficiency, and save money too often lost to no-shows. Boosting patient engagement with timely, personalized reminders breaks down barriers between patients and practices, benefiting both.
Simply put, if a patient gets the information they need when and how they need it, if they can seamlessly confirm and reschedule, they are more likely to keep that appointment. And in healthcare, that is a win-win.
The Centers for Medicare & Medicaid Services (CMS) is taking action, it announced, to protect Medicare beneficiaries and taxpayer dollars through implementation of a six-month, nationwide data-driven moratoria on new Medicare enrollment for hospices and home health agencies (HHAs).
The moratoria will allow CMS to temporarily halt the influx of new providers into these high-risk categories—a key source of fraudulent activity. Today’s move continues the Trump Administration’s crackdown on fraud, waste, and abuse in the Medicare program by stopping improper billing and preventing bad actors from entering the system.
“We’ve seen systemic and deeply troubling fraud in the hospice and home health space, with bad actors exploiting some of our most vulnerable Medicare patients and stealing money from the American taxpayer,” said CMS Administrator Dr. Mehmet Oz. “Today we’re shutting the door on fraud—preventing new bad actors from entering Medicare while we aggressively identify, investigate, and remove those already exploiting them. This is about protecting patients, restoring integrity, and safeguarding taxpayer dollars.”
During the six-month moratoria, CMS will intensify targeted investigations, deploy advanced data analytics, and accelerate the removal of hospice and HHA providers from the Medicare program that are suspected of committing fraud. This nationwide approach will also eliminate the ability of bad actor operators to evade detection by simply shifting across state lines. In addition, the moratoria will apply to all applications for initial Medicare enrollment and certain changes in majority ownership, which are frequently used to obscure control by bad actors. The moratoria will not impact current enrollments, and existing providers can continue to deliver services to Medicare beneficiaries.
CMS’ announcement follows its earlier notice of a similar moratorium to prevent fraudulent Medicare billing by certain durable medical equipment, prosthetics, orthotics, and supplies (DMEPOS) companies. With three separate moratoria now in place, CMS has taken some of the most significant fraud prevention actions in the agency’s history.
The moratoria are part of CMS’ ongoing efforts to stop fraud before it starts, using data-driven prevention and real-time enforcement as part of a coordinated federal approach. Recent CMS action has included the suspension of payments to 773 hospices and 23 HHAs suspected of fraud in Los Angeles alone, representing $70 million in suspended funds thus far.
Additional CMS work to crush fraud in the hospice and HHA areas has included:
Revoking or deactivating hundreds of hospices and HHAs engaged in improper or fraudulent activity;
Conducting nationwide hospice site visits to verify operations and identify suspicious activity;
Heightened oversight of newly enrolled Medicare hospice providers in states with elevated fraud risk, including Arizona, California, Georgia, Ohio, Nevada, and Texas;
Launching a new, publicly available hospice scoring system to increase transparency and identify providers with troubling patterns of utilization, quality, or compliance;
Implementing enhanced enrollment screening measures for high-risk HHAs, including site verification of reported practice locations and fingerprinting-based background checks; and
Expanding a demonstration project that allows pre- and post-claim review of HHA claims in Florida, Illinois, Oklahoma, Ohio, North Carolina, and Texas to stop improper payments before they occur.
Additional information on the Hospice and Home Health Agency moratoria can be found via the Federal Register at: https://www.federalregister.gov/
New survey data from the American Medical Association (AMA) show physicians are pervasively skeptical that meaningful change will occur—reflecting years of similar commitments that have failed to produce lasting improvements.
In June 2025, after successful engagement from the Trump administration to address widespread concerns from patients and physicians, roughly 60 health insurers pledged to streamline, simplify, and reduce prior authorization requirements, with implementation deadlines spanning 2025 through 2027. Ahead of the first major deadline, the AMA surveyed 1,000 practicing physicians to assess whether these commitments are likely to deliver meaningful improvements for patients and physicians.
Physician skepticism is grounded in experience. As part of the 2025 pledge, insurers committed to ensuring that all medical necessity denials would be reviewed by a licensed and qualified clinician. Yet only one in four physicians (24%) report that such reviews are consistently conducted by appropriately qualified clinicians. In addition, just 16% of physicians who participate in peer-to-peer reviews say the health plan representative often or always has the appropriate qualifications.
“Physician trust in voluntary insurer pledges is deeply eroded after years of unfulfilled promises,” said AMA President Bobby Mukkamala, M.D. “Physicians are especially frustrated when so-called peer-to-peer reviews are conducted by individuals who lack the appropriate clinical expertise to evaluate a patient’s care. When only a third of physicians expect meaningful impact—and so few report that health plan reviewers are appropriately qualified—it highlights a credibility gap that won’t be closed with vague or partial measures. Rebuilding trust will require sustained, transparent, and measurable action to streamline prior authorization and keep it clinically focused and patient-centered. Anything less risks reinforcing the skepticism these pledges were meant to address.”
The AMA survey shows how much work insurers still must do and highlights ongoing concerns that prior authorization delays care, disrupts treatment, and harms patient outcomes.
Patient Harm — More than one in four physicians (26%) report that prior authorization has led to a serious adverse event, including hospitalization, permanent impairment, or death.
Delayed Care — More than nine in 10 physicians (95%) say prior authorization delays access to necessary care.
Disrupted Care — Nearly four in five physicians (79%) report that patients abandon treatment due to authorization challenges.
Poor Outcomes — More than nine in 10 physicians (92%) say prior authorization negatively affects clinical outcomes.
Prior authorization also continues to place significant strain on physician practices, driving high volumes of requests and denials, consuming clinical and administrative time, and contributing to widespread burnout. As administrative demands intensify, resources are increasingly diverted from patient care to manage an inefficient process.
Added Burden — Physicians complete an average of 40 prior authorizations per week, and nearly one in three (32%) report that requests are often or always denied.
Physician Burnout — More than nine in 10 physicians (94%) say prior authorization contributes to burnout.
Denial Trend — Three-quarters of physicians (74%) report that denials have increased over the past five years, and six in 10 express concern that augmented intelligence (AI) may further increase denial rates.
Diverted Time and Resources — Prior authorization consumes an average of 13 hours of physician and staff time each week, and two in five physicians (40%) employ staff dedicated exclusively to prior authorization tasks.
Beyond its impact on patients and physician practices, prior authorization also drives inefficiencies and unnecessary costs across the health system.
Wasted Health Resources — More than four in five physicians (88%) report that prior authorization increases overall health care utilization, contributing to waste rather than savings. Physicians cite ineffective initial treatments (75%), additional office visits (73%), urgent or emergency care (47%), and hospitalizations (32%) as consequences of prior authorization requirements.
Physicians also report consistently high administrative burden with prior authorization across all major health insurers. UnitedHealthcare (75%) tops the ranks for “high” or “extremely high” burden, followed by Humana (65%), Anthem/Elevance (61%), Aetna (61%), Cigna (59%), and Blue Cross Blue Shield (56%).
The AMA continues to work on every front in support of prior authorization reforms that prioritize patients’ access to necessary care and reduce administrative burdens for physicians. The AMA looks forward to continuing to work with the Trump administration, Congress and health insurers on this critical issue. To learn more about prior authorization challenges experienced by patients, physicians, and employers, go to FixPriorAuth.org.
Healthcare organizations have rapidly adopted artificial intelligence across clinical decision support, diagnostics, revenue cycle management, and operational systems.
AI tools are now embedded across many hospital environments, promising better clinical outcomes, decreased administrative burden, and smarter use of healthcare data.
But as adoption accelerates, oversight continues advancing rapidly.
Regulators are increasingly scrutinizing how AI is developed, validated, and deployed in healthcare, making AI governance a new compliance focus for health system leaders. Healthcare executives and boards must urgently manage the operational, legal, and regulatory obligations that accompany AI adoption.
AI Is No Longer Solely an IT Decision
Historically, new technologies in healthcare have often been treated primarily as IT decisions. Artificial intelligence changes that dynamic. AI systems influence clinical decision making, patient risk scoring, workflow prioritization, and reimbursement. Their effect goes beyond technology deployment to clinical accountability along with regulatory oversight.
This shift demands comprehensive oversight.
Effective AI oversight now demands coordination across compliance, legal, clinical leadership, risk management, and IT teams. Health systems must begin asking foundational questions about the algorithms they deploy:
How was the model trained and validated?
What data sources were used, and are they representative of the patient population?
How frequently should models be monitored or recalibrated?
Who is accountable if AI recommendations influence clinical outcomes?
Without formal governance structures in place, health systems risk deploying tools they cannot fully explain or defend during regulatory review.
Regulators Are Catching Up
Oversight advances alongside AI adoption. In the United States, the FDA has already begun developing guidance frameworks for AI-enabled medical software and adaptive algorithms, signaling greater regulatory attention to the lifecycle management of AI systems.
This signals accountability for algorithm development, testing, monitoring, and documentation. This means AI systems may require similar documentation, validation, and performance monitoring as medical devices. Many hospitals lack readiness for this operational rigor.
The Hidden Operational Workload
One of the most common mistakes health systems make is underestimating the operational effort required to govern AI effectively. This includes committing time to oversight, establishing new processes, and allocating resources to promote ongoing compliance and risk mitigation.
Deploying an algorithm is only the starting point. Responsible AI programs require regular oversight, including:
Algorithm validation and revalidation
Bias monitoring and performance tracking
Documentation of model training data and updates
Clinical review and oversight structures
Audit trails that support regulatory inspection
Each item needs dedicated governance and clear accountability. Without them, AI meant to improve efficiency can add complexity and risk.
AI Is Becoming Part of Clinical Infrastructure
Many healthcare leaders still view AI as a pilot initiative or innovation program. Increasingly, however, AI tools are becoming embedded within everyday clinical processes. If algorithms help determine triage priorities, diagnostic interpretation, or patient risk stratification, they effectively become part of the organization’s clinical infrastructure.
This reality heightens the stakes.
Boards and executives are realizing AI oversight is fundamental. As systems affect care and decisions, governance becomes a strategic and safety-critical responsibility.
Preparing for the Next Phase of AI Adoption
The next phase of AI adoption in healthcare may be defined less by technological capability and more by governance maturity.
Health systems that establish structured oversight programs early will be better able to scale innovation while continuing regulatory readiness.
Essential steps include:
Setting up formal AI governance committees that include clinical, compliance, legal, and IT leaders
Creating model validation and lifecycle management processes
Deploying monitoring tools to evaluate accuracy and bias
Developing documentation standards that support regulatory review
Ensuring executive leadership and boards understand their oversight responsibilities
Organizations that move from reactive compliance to forward-looking governance will be better prepared for the emerging regulatory landscape in healthcare AI. AI is growing essential to healthcare delivery. Governance must evolve accordingly. Treating AI oversight as core compliance, not solely a technical matter, is vital to health innovation.
The Centers for Medicare & Medicaid Services (CMS) is taking action, it announced, to protect Medicare beneficiaries and taxpayer dollars through implementation of a six-month, nationwide data-driven moratoria on new Medicare enrollment for hospices and home health agencies (HHAs).
New survey data from the American Medical Association (AMA) show physicians are pervasively skeptical that meaningful change will occur—reflecting 
By Gilda D’Incerti, Founder and CEO,