Republican cuts to federal health programs, AI automation and rising costs are making health systems and other employers level off hiring — including for jobs requiring a professional license like nurses or physical therapists.
The results could widen gaps in care and exacerbate health disparities.
By the numbers:
Health care employment drove most of the month-by-month job growth last year, increasing by an average of 34,000 jobs per month, according to the Bureau of Labor Statistics.
But that’s less than health care’s monthly average increase of 56,000 roles in 2024.
Health care hiring hasessentially returned to a pre-pandemic pattern of slower growth after a post-COVID surge driven by returning patients and hospitals replacing burned-out workers, said Neale Mahoney, an economics professor at Stanford.
“It was only a question of when … and we’re starting to see it now,” Mahoney said.
Federal policychanges could further chill hiring.
Hospitals face financial pressure from the nearly $1 trillion cut to federal Medicaid spending in the GOP budget law. That’s combined with rising costs from treating more uninsured patients and other factors.
New caps on federal student loan borrowing could also push students away from clinical careers, many of which require pricey advanced degrees.
It wouldn’t be a surprise if a rise in deportations — combined with fewer foreign-born health workers opting to come to the U.S. on visas — dried up the pipeline of available help, especially in segments like home care.
Case in point:
Alameda Health System, a safety-net provider based in Oakland, California, announced last month that it’s laying off 247 employees, including clinicians.
Administrators cited the system’s precarious finances: It expects to lose $100 million annually by 2030 as a result of the Medicaid cuts, per CBS San Fransisco.
AI automation is also pushing some providers to cut administrative staff.
Revere Health, the largest physician-owned health system in Utah, announced in September that it will lay off 177 employees, citing a partnership with a company that automates claims processing.
Clinical jobs in health care are more insulated from automation, and AI may actually help extend the clinical workforce where shortages exist.
Still, some clinicians are concerned. Almost 15,000 nurses in New York City went on strike this month, demanding new safeguards around AI use in hospitals, among other things.
What they’re saying:
This past year represented “a repositioning of the labor market,” said Rick Gundling, chief mission impact officer at the Healthcare Financial Management Association.
Health systems are doing more targeted hiring, he said. They might look to downsize in revenue management but increase their clinical staff.
The intrigue:
Demand for care is still high. The “silver tsunami” of aging Baby Boomers may keep jobs plentiful, said Laura Ullrich, director of economic research in North America at the Indeed Hiring Lab.
Last week, J.P. Morgan hosted its 44th annual healthcare conference, with over 500 companies and 8,000 people in attendance to discuss health system performance, pharmaceutical trends, and new AI offerings.
Health systems outline current performance, future plans
According to STAT, many health systems took a measured tone at this year’s conference, focusing largely on stability and consistency, especially in the face of significant Medicaid funding cuts from the One Big Beautiful Bill Act.
“For me, it’s been about stabilizing,” said Kevin Smith, CFO of SSM Health. “Taking a look at the operations, doing a lot of blocking and tackling. Getting back to the basics.”
Separately, Paul Rathbun, outgoing CFO at AdventHealth, said that “[i]t all begins and ends with consistent financial performance.” He also emphasized sustainability and the importance of a solid foundation to handle future uncertainty.
As hospitals and health systems face financial challenges, many are focusing on boosting efficiency and reducing expenses. For example, Intermountain Health is focusing on optimizing its supply chain and simplifying healthcare with value-based arrangements.
Some health systems, like CommonSpirit Health, are planning to sell some facilities to help boost finances and expand ambulatory networks. Providence is also considering selling some of its assets, potentially including some hospitals.
“We have 51 hospitals, most of which have No. 1 market share in their communities, but we do have a handful that we may have to find a different purpose or different sponsors for,” said Providence CFO Greg Hoffman.
Other health systems, like Mass General Brigham and Hackensack Meridian, are aiming to expand their partnerships with other providers or outside organizations. Currently, Mass General Brigham has a new ambulatory care venture with Tampa General Hospital, and Hackensack Meridian is planning to add 20 new primary care clinics in New Jersey with Amazon One Medical.
Top CMS officials meet with hospital, insurance leaders
At the conference, CMS Administrator Mehmet Oz, along with four top members of his staff, hosted an event with hundreds of top hospital and health insurance leaders to discuss the Trump administration’s healthcare policies.
During the event, Oz downplayed the potential impact of upcoming Medicaid cuts, saying that they won’t be felt for at least a year and a half. “The catastrophizing over the idea that it’s going to rip the guts out of the system, I don’t think that’s fair,” he said.
CMS leaders also discussed efforts to combat fraud, the Trump administration’s focus on deregulation, recent vaccine changes, and more.
“The catastrophizing over the idea that it’s going to rip the guts out of the system, I don’t think that’s fair.”
According to David Joyner, CEO of Hill Physicians Medical Group, the general mood among attendees was skeptical and pessimistic, particularly about cuts to Medicaid and the impact of declining Affordable Care Act membership. At the same time, people were curious and optimistic about CMS’ potential to promote new innovation and technology that could address some of the healthcare industry’s most persistent challenges.
AI offerings continue to expand
AI was a large focus of the conference, with many organizations announcing new tools or collaborations.
At the conference, Anthropic announced Claude for Healthcare, a new AI model that includes HIPAA-ready infrastructure for enterprise customers, native integration to commonly used medical and scientific databases, and a model specifically trained for healthcare and life sciences tasks.
The new model follows Claude for Life Sciences, which was launched last October. Anthropic also announced new capabilities for life sciences, which ranged from preclinical research and development and regulatory affairs.
According to Eric Kauderer-Abrams, head of biology and life sciences at Anthropic, healthcare and life sciences are one of the company’s largest bets.
“Anthropic is a very natural fit for the healthcare and life sciences world because our identity as an AI company is built around safety and responsibility and rigor and reproducibility, and these are all the central tenants of the healthcare and life sciences industries,” Kauderer-Abrams said.
Currently, several healthcare organizations already use Anthropic’s Claude model, including Banner Health, Novo Nordisk, and AbbVie. At the conference, Elation Health announced that it integrated Claude into its EHR to create chart summaries and clinical insights.
Other AI companies at the conference also announced new expansion efforts. For example, Hippocratic AI, which develops patient-facing generative AI healthcare agent, said it acquired Grove AI, a startup that provides agentic AI for pharma research and development and clinical trial operations. According to Hippocratic, the acquisition will help it build its life sciences division and accelerate the use of generative and agentic AI in the biopharma and med tech sectors.
Separately, Open Evidence said it plans to move toward “medical super-intelligence” by building its AI platform on top of a group of specialist medical AI models (oncology, neurology, radiology, etc.) instead of a single centralized model.
Pharma companies discuss new products, drug pricing deals
According to Johnson & Johnson (J&J) CEO Joaquin Duato, 2026 should be a better financial year for the company as it focuses on three high-growth areas for its med tech business: cardiovascular, surgery, and vision. Currently, J&J has around a dozen upcoming product launches across its med tech and innovative medicine businesses, including its new Ottava Robotic Surgical System.
Separately, BioNTech, which is most known for developing a COVID-19 vaccine with Pfizer, is turning its attention to cancer treatment. The company has an $11 billion partnership with Bristol Myers Squibb for its bispecific PDL1-VEGF antibody.
Pharmaceutical leaders also discussed the “most favored nation” (MFN) deals they signed with the Trump administration to reduce the prices of certain prescription drugs.
Paul Hudson, CEO of Sanofi, said that while it may sound like everyone won with these deals, there were compromises from both sides.
“I would say the government got what it needed, and we worked very hard to make sure that we could still deliver what we think is an attractive investment thesis for the company without breaking stride,” Hudson said. “So it was a very difficult needle to thread. I don’t want to give the impression that there’s no impact from MFN, because the question for us is: Can we manage that and deliver an attractive long-range plan?”
Christopher Boerner, CEO of Bristol Myers Squibb, had a similar view. “I think what we did with the agreement we signed at the end of last year is find a way to balance the interest of the administration in ‘most-favored nation’ with, obviously, what’s best for the company, but importantly, how can we find ways to provide real value to patients?”
The digital health market ramps up
In 2025, digital health investments reached $14.2 billion in funding, a 35% increase from 2024 and the highest total since 2022. Last year, there were 26 megadeals, or those that raised over $100 million, and 15 new “unicorn” companies, or those valued at over $1 billion, up from just six in 2024.
Although the digital health market is still below its pandemic-era peak, there was significant growth in the market in 2025, largely driven by excitement around AI, according to analysts from Rock Health. The analysts also noted that there is a growing concentration of power in the digital health space, with certain companies having outsized influence.
“On one side, AI-native upstarts attracted huge rounds at unprecedented speed, a handful of companies broke the IPO drought, and private equity made major moves, signaling real bets on an emerging ‘winner’ class,” wrote Rock Health analysts Megan Zweig, Jacqueline Kimmell, and Maddie Knowles. However, on the other side, “… many companies are still grappling with valuation overhangs from prior cycles while operating in a more competitive market.”
The economic fortunes of mom-and-pop businesses are diverging from those of their larger counterparts — a pre-existing gap that now appears to be getting bigger, faster.
Why it matters:
The evidence is in the private-sector labor market, that in recent months, has been propped up by large companies as smaller firms — typically responsible for 40% of U.S. employment — shed workers.
The big picture:
Larger businesses have been able to adapt to a tough economic backdrop — historic tariffs, high interest rates and a more cautious consumer — in ways far more challenging for small companies with fewer resources.
“It’s evident that medium and large firms are better positioned to weather what’s going on,” said ADP chief economist Nela Richardson.
“They can set prices, they can change suppliers. They can hire contractors instead of permanent employees in a more sophisticated way. They can hire globally, not just in their local region. They have more tools in the toolbox,” Richardson said.
By the numbers:
The hiring gap between small and big businesses is getting worse, a fresh sign that small business firings are holding down jobs growth across the economy.
As we mentioned yesterday, the private sector shed 32,000 jobs in November, according to payroll processor ADP. Small firms — those with fewer than 50 employees — accounted for all of the losses.
Those businesses reported a net loss of 120,000 jobs, the most small businesses have cut since the pandemic’s onset. Larger businesses grew, but not enough to offset the cuts elsewhere.
“Small business hiring reallystarted to slow in April and I attribute some of this to tariffs and the higher cost of doing business that small companies are much less able to absorb,” Peter Boockvar, chief investment officer at One Point BFG Wealth Partners, wrote in a note.
“The natural reaction is to cut costs elsewhere and we know that labor is their biggest cost,” Boockvar added.
The intrigue:
Bloomberg recently reported that there are more small businesses filing for bankruptcy under a special federal program this year than at any point in the program’s six-year history.
Subchapter V filings, which allow firms to shed debt faster and cheaper, are up 8% from last year, according to data from Epiq Bankruptcy Analytics.
Chapter 11 filings — a process used by larger businesses — are up roughly 1% over the same time frame.
Threat level:
Main Street is bearing the brunt of an economic slowdown in ways that might make it even harder for small shops to compete with larger companies.
One bright spot: Despite that pain, applications to start new businesses — ones likely to employ other people — remain notably higher than in pre-pandemic times, according to the latest data available from the Census Bureau.
What to watch:
The Trump administration shrugged off the ADP data that indicated a hiring bust. Commerce Secretary Howard Lutnick told CNBC that the cuts were due to factors unrelated to tariffs, like immigration crackdowns.
That hints at a debate among monetary policymakers, who are trying to gauge how much weak jobs growth is a byproduct of fewer available workers.
But ADP had earlier told reporters that small businesses generally had less demand for workers — not that staff weren’t available for hire.
The software monopoly that powers American hospitals wasn’t built for the data, speed, or intelligence the future of medicine demands.
Epic Systems is an American privately held healthcare software company, founded by Judy Faulkner in 1979, and has grown into the largest electronic health record (EHR) vendor by market share, covering over half of all hospital patients in the U.S.
Epic dominates American healthcare today. But so did Kodak in photography and GE in industry. Its software runs the country’s hospitals, determines the workflows clinicians, nurses and clinical support staff use, and shapes what data gets captured (or more often, what gets lost). It also serves as the front door for healthcare data for the patients it serves. Dominance has never guaranteed a future. Epic’s position reflects the architecture of the past, not the one emerging now.
More importantly, the sheer volume of activity occurring in these hospitals means they are collectively running thousands of experiments, mini clinical trials, and critical observations daily. The stakes are enormous: billions of dollars in drug discovery, the efficiency of clinical trials (currently plagued by poor recruitment and high costs), and the potential for better, personalized care. The data generated in these environments is the single most valuable, untapped resource in all of medicine.
However, this monumental source of value is being throttled by outdated infrastructure, and it shows. It’s hard to imagine a world where AI is used to its full potential in healthcare while Epic is still running the show. The ideas are oppositional at their core.
The Massive Data Problem
Technology is accelerating faster than any legacy system can keep up with. AI is reshaping every major industry, and healthcare will be forced to catch up. However, this essential transformation is structurally incompatible with the dominant system of record.
To put it bluntly: Epic has a data problem. A massive data problem. Not just imperfect data — structurally flawed data. What Epic captures is fragmented, delayed, and riddled with inconsistencies. Diagnoses become billing codes that distort reality. Interventions like intubations, pressor starts, and ventilator changes appear hours late, if at all. Outcomes are incomplete or missing. What remains isn’t a clinical record in any meaningful sense but a billing ledger dressed up as documentation. No model can learn reliably from that.
But the deeper problem is the data Epic never sees. Some of the most valuable information in modern medicine: continuous monitoring streams, ventilator logs, infusion pump data… never enters the EHR in a structured or analyzable form. In many cases, it isn’t captured at all.
I recently brought Roon (a well-known engineer at OpenAI) and Richard Hanania(a public intellectual/cultural critic)—both advisors in my new venture, in full disclosure—to one of the largest academic medical centers in the country. Both watched torrents of millisecond-scale data spill off monitors. Streams that could reveal what’s happening in the brain, heart, and vasculature. Valuable data… all vanished instantly. None of it logged. None of it stored. None of it correlated with outcomes. Roon captured this shock in a viral post on X/Twitter, essentially describing how hospitals are filled with catastrophic events like sudden cardiac death, yet we save none of the time-series data that could teach us how to prevent the next one. His shock distilled what people in technology grasp immediately and what healthcare has normalized: industries where human life isn’t exactly top of mind record everything; hospitals, where the stakes are life and death, learn almost nothing from themselves.
In Silicon Valley, losing data like this is unthinkable. In healthcare, it barely registers.
Epic was never built to ingest or learn from this scale of data. It was built to satisfy billing requirements, regulatory checklists, and documentation workflows. That is the beginning and end of its architecture. It is not a learning system, much less an AI system. It is not even a modern data system. And that is the root of Epic’s downfall.
The Cultural and Financial Moat
Epic is famous for its internal commandments — principles Judy Faulkner wrote decades ago:
Do not acquire.
Do not be acquired.
Do not raise outside capital.
(If you haven’t heard it, the latest Acquired podcast episode on Epic is essential listening)
But the same rules that built its empire now limit what it can become. What was once a strategic strength is now its ceiling.
The next era of healthcare software demands investments that were unnecessary when the EHR was the center of gravity. Building AI-native infrastructure: real-time data pipelines, device integrations, large-scale compute, continuous model training, semantic normalization — requires not millions but tens of billions of dollars. Most companies facing that kind of leap can raise capital, acquire talent, or merge with partners. Epic has ruled all of those options out.
Epic’s formidable market share is anchored by a massive customer sunk cost. With implementation fees often exceeding a billion dollars for large systems, the financial and political inertia makes replacing the EHR functionally unthinkable. However, this commitment only forces customers to defend an obsolete data architecture. By preventing them from adopting novel solutions, this inertia doesn’t protect Epic’s long-term viability, it simply guarantees a widening technical gap between the EHR and the transformative potential of AI.
A company optimized for slow, controlled expansion cannot transform itself into an AI-scale enterprise without violating the principles that define it. The culture that kept Epic dominant is the culture that prevents it from catching the next wave. Epic will continue to excel at documentation, billing, and compliance — but those strengths are anchored in the past. The future belongs to systems that learn, and Epic was never designed to learn.
The Shift to Middleware
Meanwhile, the broader economy is being held up by AI. The world’s largest tech companies are pouring staggering sums into compute, data centers, and model training. And all that compute needs rich, complex, high-value data to train on.
Healthcare is the only remaining frontier of that scale.
No other industry generates so much information while analyzing so little of it. No other sector represents nearly 20% of U.S. GDP yet still runs on fragmented workflows and manual processes. And the incentives here are unmatched: improving patient outcomes, reducing costs, eliminating inefficiency, accelerating drug development, modeling disease trajectories, and eventually automating the more repetitive layers of care. There’s even an irony: the very infrastructure needed to enable learning health systems would also finally make billing more accurate.
I’m not writing this to showcase some utopian vision of AI curing all disease. It’s the practical use of technology we already possess. Our limitation isn’t the models; it’s the missing data.
A handful of companies have bet their trillion-dollar valuations on this: OpenAI, Google, Amazon, Nvidia, Apple, Oracle. They are spending hundreds of billions a year on AI infrastructure and need high-volume, high-quality datasets to justify that investment. Healthcare produces oceans of exactly that kind of data, and most of it evaporates. The companies that learn to capture and structure it will define the next layer of healthcare infrastructure. Whether they integrate with Epic, build around it, or replace it is almost secondary.
What matters is that none of them are waiting for Epic.
Clinicians won’t either. Once tools exist that unify the data hospitals already generate, reduce workload, eliminate administrative drag, and answer the questions clinicians actually ask — What happened? Why did it happen? What should we do now? — the center of gravity will shift. Clinicians will live inside those tools, not inside an interface built for billing.
Epic can still exist, but it doesn’t need to function as healthcare’s operating system. There’s precedent for this in every major industry: the core orchestration/data layer eventually recedes into the background while workflow and data intelligence move up the stack. At that point, the EHR becomes background infrastructure or middleware. The intelligence/workflow layer becomes the real operating system. Epic will undoubtedly resist this shift, yet its attempts to maintain total control of the clinician interface will ultimately collide with the utility and data gravity of AI-native systems.
Epic becomes the backend: essential, invisible, and no longer the place where the practice of medicine occurs.
Regulatory modernization around HIPAA, interoperability, and data liquidity will be essential, but that is a conversation for another essay.
Epic isn’t vanishing tomorrow. Large institutions rarely do. But its relevance is eroding in the only domain that will matter over the next decade: the ability to harness data at a scale and fidelity that makes AI transformative. It can keep its commandments, preserve its culture, and reject outside capital — it just can’t do all that and remain the central platform of hospital data in an AI-native future.
For an economy that’s rapidly expanding, the usual drivers of job creation sure aren’t carrying their weight.
Why it matters:
Anemic job growth in key sectors is a sign that there is more underlying weakness in worker demand than the low unemployment rate might suggest.
It makes for a weaker starting point, as companies see new opportunities around the corner to use AI to automate their work.
It’s not a new trend: These sectors showed weak job creation or outright job losses for the last couple of years of the Biden administration.
But it is striking that a GDP surge fueled by data center and AI investment hasn’t been enough to generate more robust hiring.
By the numbers:
Overall employment is up 0.8% over the 12 months ended in September, but the hiring has been driven in significant part by health care, state and local government, and other less cyclical sectors.
Manufacturing employment is down 0.7% over the last 12 months. Tariffs are weighing on the sector, but its job losses long predate the Trump trade wars, with year-over-year job losses for more than two years.
Temporary help employment, which tends to be a volatile indicator underlying growth trends, is down 3%. It has been losing jobs for three consecutive years.
Two other sectors that tend to correlate with overall economic momentum, transportation and warehousing and wholesale trade, are also adding jobs at rates below that of overall job growth (0.6% and 0.2%, respectively).
Stunning stat:
As Bloomberg flagged, two sectors — health care and social assistance, and leisure and hospitality — accounted for more than 100% of net job gains so far in 2025.
Excluding those sectors, employment dropped by 6,000 jobs in the first nine months of the year.
Zoom out:
There’s not much reason to think these numbers are driven by AI-related opportunities for companies to increase productivity and rely on fewer human workers, particularly given that the phenomenon isn’t new.
But it is more plausible that seeing such opportunities on the horizon has made companies more reluctant to hire in the absence of overwhelming need.
BlackRock chief investment officer for global fixed income Rick Rieder wrote in a note after last week’s jobs report that “what we think we are seeing now is … essentially a hiring pause in anticipation of AI.”
Of note:
A report out this morning from the McKinsey Global Institute finds that AI and robotics technologies could, in theory, automate 57% of U.S. work hours.
“AI will not make most human skills obsolete, but it will change how they are used,” the authors find. “As AI takes on common tasks, people will apply their skills in new contexts,” they write, such as less time researching and preparing documents and more time framing questions and interpreting results.
The bottom line:
Beneath the headline numbers, there is some good reason that attitudes toward the job market are glum.
Layoff trends in 2025 indicate an increase in job cuts compared to 2024, with US employers announcing nearly 950,000 cuts through September, the highest number since 2020. Key drivers include cost-cutting measures, the strategic implementation of artificial intelligence (AI), and a cooling labor market.
Key Trends
Elevated Numbers: Total US job cuts through October 2025 were over one million, a 65% increase from the same period in 2024. October 2025 had the highest number of layoffs for that month in 22 years.
AI as a Primary Driver: AI adoption is a leading cause for job cuts as companies restructure for efficiency and reallocate resources. Companies like Amazon and Intel have cited AI as a reason for significant workforce reductions.
“Forever Layoffs”: A new trend involves smaller, more regular rounds of layoffs (fewer than 50 people) that create ongoing worker anxiety and impact company culture. These rolling cuts often stay out of headlines but contribute significantly to the overall job cuts.
Method of Notification: The process is becoming more impersonal, with many employees being notified of their termination via email or phone call rather than in-person meetings.
Hiring Slowdown: Alongside the layoffs, there has been a sharp drop in hiring plans, with planned hires for the year at their lowest level since 2011.
Affected Industries
While tech has been significantly impacted since late 2022, other industries are also facing substantial cuts in 2025:
Technology: Remains a leading sector for cuts as companies continue to restructure after pandemic-era overhiring and focus on AI.
Retail and Warehousing: Companies like Target and UPS are cutting thousands of jobs due to changing consumer demands, automation, and a push for efficiency.
Energy and Manufacturing: Oil giants such as Chevron and BP are making cuts as part of cost-reduction strategies and market consolidation.
Finance and Consulting: Firms like PwC and Morgan Stanley are trimming staff, citing factors like low attrition rates and the need to realign resources.
Media and Communications: Companies like CNN and the Washington Post have made cuts to pivot toward digital services and reduce costs.
Economic Context
The overall U.S. labor market remains relatively healthy despite the uptick in layoffs, though it is showing signs of cooling. The unemployment rate has inched up, and consumer sentiment has declined. The Federal Reserve is monitoring the situation and has implemented interest rate cuts to help stabilize the job market.
President Donald Trump has made big promises about fixing American healthcare. Now comes the moment that separates talk from action.
With the 2026 midterms fast approaching and congressional attention soon shifting to electoral strategy, the window for legislative results is closing quickly. This summer will determine whether the administration turns promises into policy or lets the opportunity slip away.
Trump and his handpicked healthcare leaders — HHS Secretary Robert F. Kennedy Jr. and FDA Commissioner Dr. Marty Makary — have identified three major priorities: lowering drug prices, reversing chronic disease and unleashing generative AI. Each one, if achieved, would save tens of thousands of lives and reduce costs.
But promises are easy. Real change requires political will and congressional action. Here are three tests that Americans can use to gauge whether the Trump administration succeeds or fails in delivering on its healthcare agenda.
Test No. 1: Have Drug Prices Come Down?
Americans pay two to four times more for prescription drugs than citizens in other wealthy nations. This price gap has persisted for more than 20 years and continues to widen as pharmaceutical companies launch new medications with average list prices exceeding $370,000 per year.
One key reason for the disparity is a 2003 law that prohibits Medicare from negotiating prices directly with drug manufacturers. Although the Inflation Reduction Act of 2022 granted limited negotiation rights, the initial round of price reductions did little to close the gap with other high-income nations.
President Trump has repeatedly promised to change that. In his first term, and again in May 2025, he condemned foreign “free riders,” promising, “The United States will no longer subsidize the healthcare of foreign countries and will no longer tolerate profiteering and price gouging.”
To support these commitments, the president signed an executive order titled “Delivering Most-Favored-Nation (MFN) Prescription Drug Pricing to American Patients.” The order directs HHS to develop and communicate MFN price targets to pharmaceutical manufacturers, with the hope that they will voluntarily align U.S. drug prices with those in other developed nations. Should manufacturers fail to make significant progress toward these targets, the administration said it plans to pursue additional measures, such as facilitating drug importation and imposing tariffs. However, implementing these measures will most likely require congressional legislation and will encounter substantial legal and political challenges.
The pharmaceutical industry knows that without congressional action, there is no way for the president to force them to lower prices. And they are likely to continue to appeal to Americans by arguing that lower prices will restrict innovation and lifesaving drug development.
But the truth about drug “innovation” is in the numbers: According to a study by America’s Health Insurance Plans, seven out of 10 of the largest pharmaceutical companies spend more on sales and marketing than on research and development. And if drugmakers want to invest more in R&D, they can start by requiring peer nations to pay their fair share — rather than depending so heavily on U.S. patients to foot the bill.
If Congress fails to act, the president has other tools at his disposal. One effective step would be for the FDA to redefine “drug shortages” to include medications priced beyond the reach of most Americans. That change would enable compounding pharmacies to produce lower-cost alternatives just as they did recently with GLP-1 weight-loss injections.
If no action is taken, however, and Americans continue paying more than twice as much as citizens in other wealthy nations, the administration will fail this crucial test.
Test No. 2: Did Food Health, Quality Improve?
Obesity has become a leading health threat in the United States, surpassing smoking and opioid addiction as a cause of death.
Since 1980, adult obesity rates have surged from 15% to over 40%, contributing significantly to chronic diseases, including type 2 diabetes, heart disease and multiple types of cancers.
A major driver of this epidemic is the widespread consumption of ultra-processed foods: products high in added sugar, unhealthy fats and artificial additives. These foods are engineered to be hyper-palatable and calorie-dense, promoting overconsumption and, in some cases, addictive eating behaviors.
RFK Jr. has publicly condemned artificial additives as “poison” and spotlighted their impact on children’s health. In May 2025, he led the release of the White House’s Make America Healthy Again (MAHA) report, which identifies ultra-processed foods, chemical exposures, lack of exercise and excessive prescription drug use as primary contributors to America’s chronic disease epidemic.
But while the report raises valid concerns, it has yet to produce concrete reforms.
To move from rhetoric to results, the administration will need to implement tangible policies.
Here are three approaches (from least difficult to most) that, if enacted, would signify meaningful progress:
Front-of-package labeling. Implement clear and aggressive labeling to inform consumers about the nutritional content of food products, using symbols to indicate healthy versus unhealthy options.
Taxation and subsidization. Impose taxes on unhealthy food items and use the revenue to subsidize healthier food options, especially for socio-economically disadvantaged populations.
Regulation of food composition. Restrict the use of harmful additives and limit the total amount of fat and sugar included, particularly for foods aimed at kids.
These measures will doubtlessly face fierce opposition from the food and agriculture industries. But if the Trump administration and Congress manage to enact even one of these options — or an equivalent reform — they can claim success.
If, instead, they preserve the status quo, leaving Americans to decipher nutritional fine print on the back of the box, obesity will continue to rise, and the administration will have failed.
Test No. 3: Are Patients Using Generative AI To Improve Health?
The Trump administration has signaled a strong commitment to using generative AI across various industries, including healthcare. At the AI Action Summit in Paris, Vice President JD Vance made the administration’s agenda clear: “I’m not here this morning to talk about AI safety … I’m here to talk about AI opportunity.”
FDA Commissioner Dr. Marty Makary has echoed that message with internal action. After an AI-assisted scientific review pilot program, he announced plans to integrate generative AI across all FDA centers by June 30.
But internal efficiency alone won’t improve the nation’s health. The real test is whether the administration will help develop and approve GenAI tools that expand clinical access, improve outcomes and reduce costs.
To these ends, generative AI holds enormous promise:
Managing chronic disease: By analyzing real-time data from wearables, GenAI can empower patients to better control their blood pressure, blood sugar and heart failure. Instead of waiting months between doctor visits for a checkup, patients could receive personalized analyzes of their data, recommendations for medication adjustments and warnings about potential risk in real time.
Improving diagnoses: AI can identify clinical patterns missed by humans, reducing the 400,000 deaths each year caused by misdiagnoses.
Personalizing treatment: Using patient history and genetics, GenAI can help physicians tailor care to individual needs, improving outcomes and reducing side effects.
These breakthroughs aren’t theoretical. They’re achievable. But they won’t happen unless federal leaders facilitate broad adoption.
That will require investing in innovation. The NIH must provide funding for next-generation GenAI tools designed for patient empowerment, and the FDA will need to facilitate approval for broad implementation. That will require modernizing current regulations. The FDA’s approval process wasn’t built for probabilistic AI models that rely on continuous application training and include patient-provided prompts. Americans need a new, fit-for-purpose framework that protects patients without paralyzing progress.
Most important, federal leaders must abandon the illusion of zero risk. If American healthcare were delivering superior clinical outcomes, managing chronic disease effectively and keeping patients safe, that would be one thing. But medical care in the United States is far from that reality. Hundreds of thousands of Americans die annually from poorly controlled chronic diseases, medical errors and misdiagnoses.
If generative AI technology remains confined to billing support and back-office automation, the opportunity to transform American healthcare will be lost. And the administration will have failed to deliver on this promise.
When I teach strategy at Stanford’s Graduate School of Business, I tell students that the best leaders focus on a few high-priority goals with clear definitions of success — and a refusal to accept failure. Based on the administration’s own words, grading the administration on these three healthcare tests will fulfill those criteria.
However, with Labor Day just months away, the window for action will soon close. The time for presidential action is now.
Artificial general intelligence (AGI) refers to AI systems that can match or exceed human cognitive abilities across a wide range of tasks, including complex medical decision-making.
With tech leaders predicting AGI-level capabilities within just a few years, clinicians and patients alike may soon face a historic inflection point: How should these tools be used in healthcare, and what benefits or risks might they bring? Last month’s survey asked your thoughts on these pressing questions. Here are the results:
My thoughts:
I continue to be impressed by the expertise of readers. Your views on artificial general intelligence (AGI) closely align with those of leading technology experts. A clear majority believes that AGI will reach clinical parity within five years. A sizable minority expect it will take longer, and only a small number doubt it will ever happen.
Your answers also highlight where GenAI could have the greatest impact. Most respondents pointed to diagnosis (helping clinicians solve complex or uncertain medical problems) as the No. 1 opportunity. But many also recognized the potential to empower patients: from improving chronic disease management to personalizing care. And unlike the electronic health record, which adds to clinicians’ workloads (and contributes to burnout), GenAI is widely seen by readers as a tool that could relieve some of that burden.
Ultimately, the biggest concern may lie not with the technology, itself, but in who controls it. Like many of you, I worry that if clinicians don’t lead the way, private equity and for-profit companies will. And if they do, they will put revenue above the interests of patients and providers.
Thanks to those who voted. To participate in future surveys, and for access to timely news and opinion on American healthcare, sign up for my free (and ad-free) newsletter Monthly Musings on American Healthcare.
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Dr. Robert Pearl is the former CEO of The Permanente Medical Group, the nation’s largest physician group. He’s a Forbes contributor, bestselling author, Stanford University professor, and host of two healthcare podcasts. Check out Pearl’s newest book, ChatGPT, MD: How AI-Empowered Patients & Doctors Can Take Back Control of American Medicine with all profits going to Doctors Without Borders.
The growth in denials can be partially explained by the increasing popularity of managed Medicare and Medicaid plans, but evolving payer practices, including the adoption of AI for algorithmic denials, have also contributed. Claims denials have emerged as one of the key points of payer-provider tension, and an effective claims denials management and prevention program is a powerful way for health systems to rebalance their payer relationships.
Denied claims result in reduced reimbursement, added administrative burdens, and patient and provider frustrations. Even when denials are successfully appealed and reversed—the KFF report found that in 2023, 82% of Medicare Advantage denials were partially or fully overturned—the time and resources devoted to the appeals process add to the costs of providing healthcare services. Optimizing pre-billing activities to reduce avoidable denials and improve and streamline the patient experience of care is as essential for health systems as a robust appeals strategy. This article addresses critical success factors for both preventing and appealing denials.
Preventing Claims Denials During Pre-Bill Period
Successfully preventing denials requires a centralized program across the workforce, from frontline providers to clinical and revenue cycle staff, to manage pre-bill activities by focusing on identifying the correct patient insurance information, obtaining accurate authorizations, and preventing concurrent denials while the patient is still in the facility. Utilization review nurses, attending providers, and Physician Advisors should be attentive to documenting the full state of patient acuity, while collaborating with the revenue cycle team. This team should focus on the collection and reporting of medically necessary data and documentation, which serves as the evidence payers use to evaluate prior authorization requests. When information about a patient’s condition isn’t recorded, or acknowledged in an authorization request, unnecessary denials can result.
A successful denials prevention program expands beyond the utilization management (UM) team and includes revenue cycle, and provider collaboration. Revenue cycle pre-service procedures should focus on confirming insurance benefits and securing payer authorization for planned services while collaborating with UM and referral sources. A comprehensive and proactive denials prevention program helps conveys to payers the full extent of inpatient clinical work, thanks to a collaborative effort to improve documentation.
The following list can help organize denials prevention programs across all locations, clinics and practices:
Establish an enterprise-wide denials prevention strategy which includes a multi-disciplinary denials management committee focused on identifying denials trends, conducting root cause analyses, developing proactive denials mitigation plans, creating enhanced reporting, monitoring improvement, and communicating risk
Establish proactive revenue cycle, UM, pre-certification, and peer-to peer workflows procedures to confirm completion of payer requirements prior to scheduled services and discharge
Ensure patients are financially cleared through implementation of pre-service protocols, including enhanced medical necessity process for outpatient services, authorization defer and delay procedures to reduce rework and avoidable denials
Identify pre-bill edits to increase “clean claim” efficiency, reducing initial denials and expediting reimbursement
Deliver education to providers, care management, and nursing teams on key observation concepts, such as clinical documentation improvement, patient status documentation, medical necessity documentation and orders for the Two Midnights rule, and payer reimbursement methodologies
Pursuing Post-Bill Appeals, Reversals and Payer Escalation
A strong denials management and prevention program should include a robust post-bill appeals program with skilled coding, clinical and technical resources. A targeted and strategic appeal process can result in improved overturn rates and increased reimbursement. Appeal letters which are supported by clinical facts, payer policies, and a summary of key components relevant to each case and the associated denial increase the likelihood of success.
Components of the appeal program should include the following:
Guidelines for when to appeal based on potential success by payer and appeal level
Reviews of upheld appeals for second and third level appeals based on strategy by payer
Trends for all upheld appeals by reason and by payer
Dashboard for tracking denials activities
Appeal letter writing guidelines and tips to support
Evaluation process for existing payer escalation workflows, tools and payer communication strategies with consideration for payer
Process to measure and monitor overturn rates and improvement opportunities
The collaboration with managed care is vital to the success of the denials management/prevention program. A formal payer escalation process which facilitates transparency between the payer and provider can result in improved relations and a reduction in initial denials. Successful denials management/prevention payer escalation programs are strategic and focus on addressing unfair/incorrect denials and establishing clear bi-directional reporting and communications. These programs can result in improved contract negotiations and reduce incorrect denials.
Artificial Intelligence (AI) can support the post-bill appeals process and can be especially relevant when developing a strategy to combat denials. Not only are payers increasingly using AI to trigger denials, but health systems can also deploy AI to write appeal letters, analyze denial trends, and summarize medically necessary documentation. Although algorithmic denials have become a source of frustration for providers and patients, health systems can also deploy AI to their defense. While payers are often better positioned to devote AI resources to claims, a little bit of investment from health systems, deployed effectively, can go a long way toward evening the playing field.
Closing Thoughts and Seven Questions to Consider
A formal denials management and prevention program is essential to obtaining proper reimbursement for the care provided and reducing rework across the enterprise. A strong program should also improve the patient’s experience of care: ideally, a patient should not need to interact with or hear from their provider between scheduling an appointment and checking in.
Denials management and prevention programs should be led by multi-disciplinary committees and focus on reducing avoidable denials and rework. Reducing denials requires the implementation of a multi-disciplinary program and collaboration between UM, revenue cycle, clinical documentation improvement, managed care, clinical operation and providers.
Health systems reassessing their claims denials program should consider these questions:
Do you have a reactive or proactive denials management strategy in place?
Does your denials strategy include multi-disciplinary team representation?
What reporting/tools are currently being used to track and manage denials?
What are your top five denial categories and what is being done to address the root cause of these denials?
How are avoidable denial risks managed, communicated and monitored?
Have you implemented a comprehensive denials management strategy with a multi-disciplinary committee?
Are the system’s internal resources and expertise sufficient for addressing identified challenges, or should the system seek external partners to implement changes?
No occupation will be unaffected by the technology, but healthcare will be affected less than other industries, owing much to its inherent complexity
Across the country and across industries, workers are nervous that automation and artificial intelligence will eventually take over their jobs. For some, those fears may be grounded in reality.
Healthcare, however, looks like it will be largely safe from that trend, a new report from the Brookings Metropolitan Policy Program finds.
Examining a chunk of time from the 1980s to 2016, the piece tracks the historical evolution of the technology and uses those findings to project forward to 2030.
The verdict? AI will replace jobs in various industries, but not so much in healthcare.
IMPACT
AI is projected to be an increasingly common form of automation, and the report claims the effects should be manageable in the aggregate labor market. Uncertainty remains, of course, and the effects will vary greatly — across geography, demographics and occupations.
Overall, though, only about 25 percent of U.S. jobs are at a high risk of replacement by automation. That translates to about 36 million jobs, based on 2016 data.
A higher percentage, 36 percent, are at medium risk (52 million jobs) while the largest group is the low-risk group, at 39 percent (57 million jobs).
Most of healthcare belongs in the medium-to-low categories, largely driven by the complexity of healthcare jobs. Still, the risk varies wildly. Medical assistants have what the report calls “automation potential” of 54 percent, but home health aids have just an 8 percent automation potential. Registered nurses sit somewhere in between, at 54 percent.
For healthcare support occupations, the number is closer to 49 percent; healthcare practitioners and technical jobs have 33 percent automation potential.
TREND
The report emphasizes that while some occupations will be safer from automation than others, no industry will be unaffected totally. Mundane tasks will be the most vulnerable.
Fortunately for those in the industry, there’s little in healthcare that’s mundane. AI and machine learning algorithms tend to rely on large quantities of data to be effective, and that data needs human hands to collect it and human eyes to analyze it.
And since AI in healthcare is currently utilized mainly to aggregate and organize data — looking for trends and patterns and making recommendations — a human component is very much needed, an opinion shared by several experts, who point out that empathy are reasoning skills are required in the field.
HENRY KOTULA 