healthcarereimagined

byBeth Stackpole

Feb 18, 2026 9 minute read

What you’ll learn:

• What agentic AI is and how it differs from traditional generative AI tools like chatbots.
• How organizations are already using AI agents to automate complex, multistep workflows.
• What leaders should consider when implementing agentic AI, including infrastructure, security, and human oversight.

Rewind a few years, and large language models and generative artificial intelligence were barely on the public radar, let alone a catalyst for changing how we work and perform everyday tasks.

Today, attention has shifted to the next evolution of generative AI: AI agents or agentic AI, a new breed of AI systems that are semi- or fully autonomous and thus able to perceive, reason, and act on their own. Different from the now familiar chatbots that field questions and solve problems, this emerging class of AI integrates with other software systems to complete tasks independently or with minimal human supervision.

“The agentic AI age is already here. We have agents deployed at scale in the economy to perform all kinds of tasks,” said Sinan Aral, a professor of management, IT, and marketing at MIT Sloan. 

Nvidia CEO Jensen Huang, in his keynote address at the 2025 Consumer Electronics Show, said that enterprise AI agents would create a “multi-trillion-dollar opportunity” for many industries, from medicine to software engineering.  

A spring 2025 survey conducted by MIT Sloan Management Review and Boston Consulting Group found that 35% of respondents had adopted AI agents by 2023, with another 44% expressing plans to deploy the technology in short order. Leading software vendors, including Microsoft, Salesforce, Google, and IBM, are fueling large-scale implementation by embedding agentic AI capabilities directly in their software platforms. 

Yet Aral said that even companies on the cutting edge of deployment don’t fully grasp how to use AI agents to maximize productivity and performance. He describes the collective understanding of the societal implications of agentic AI on a larger scale as nascent, if not nonexistent.

The technology presents the same high-stakes data quality, governance, and trust and security challenges as other AI implementations, and rapid evolution could also propel organizations to adopt agentic AI without fully understanding its capabilities or having created a formal strategy and risk management framework. 

“It’s absolutely an imperative that every organization have a strategy to deploy and utilize agents in customer-facing and internal use cases,” Aral said. “But that sort of agentic AI strategy requires an understanding and systematic assessment of risks as well as business benefits in order to deliver true business value.”

What is agentic AI? 

While there isn’t a universally agreed upon definition of agentic AI, there are broad characteristics associated with it. While generative AI automates the creation of complex text, images, and video based on human language interaction, AI agents go further, acting and making decisions in a way a human might, said MIT Sloan associate professor John Horton. 

In a research paper exploring the economic implications of agents and AI-mediated transactions, Horton and his co-authors focus on a particular class of AI agents: “autonomous software systems that perceive, reason, and act in digital environments to achieve goals on behalf of human principals, with capabilities for tool use, economic transactions, and strategic interaction.” AI agents can employ standard building blocks, such as APIs, to communicate with other agents and humans, receive and send money, and access and interact with the internet, the researchers write. 

MIT Sloan professor Kate Kellogg and her co-researchers further explain in a 2025 paper that AI agents enhance large language models and similar generalist AI models by enabling them to automate complex procedures. “They can execute multi-step plans, use external tools, and interact with digital environments to function as powerful components within larger workflows,” the researchers write.

It’s an imperative that every organization have a strategy to deploy and utilize AI agents in customer-facing and internal use cases.

Sinan Aral Professor, MIT SloanShare 

For example, an AI agent could plan a vacation using input from a consumer along with API access to specific web sites, emails, and communications platforms like Slack to decide what hotels or flights work best. With credit card permissions, the agent could book and pay for the entire transaction without human involvement. In the physical world, an AI agent could monitor real-time video and vision systems in a warehouse to identify events outside of normal operations. 

“The agent could raise a red flag or even be programmed to stop a conveyor belt if there was a problem,” Aral said. “It is not just the digital world — agents can actually take actions that change things happening in the physical world.”

Aral draws a slight distinction between AI agents and the broader category of agentic AI, although most people still refer to the two interchangeably. He defines agentic AI as systems that incorporate multiple, different agents that are orchestrating a task together — for example, a marketplace of agents representing both the buy and sell side during a negotiation or transaction. 

How are businesses using agentic AI?

Companies across sectors are starting to use AI agents. In the banking and financial services space, companies such as JPMorgan Chase are exploring the use of AI agents to detect fraud, provide customized financial advice, and automate loan approvals and legal and compliance processes, which could reduce the need for junior bankers. Retail giants like Walmart are building LLM-powered AI agents to automate personal shopping experiences and to facilitate time-consuming customer service and business activities such as merchandise planning and problem resolution.

“The benefit of agentic AI systems is they can complete an entire workflow with multiple steps and execute actions,” Kellogg said.

One particularly important application for agents may be performing tasks that a human typically would — such as writing contracts, negotiating terms, or determining prices — at a much lower marginal cost. 

“The fundamental economic promise of AI agents is that they can dramatically reduce transaction costs — the time and effort involved in searching, communicating, and contracting,” said Peyman Shahidi, a doctoral candidate at MIT Sloan. 

AI agents can also provide economic value by helping humans make better market decisions, according to Horton. His research with Shahidi about agents engaging in economic transactions argues that people will deploy AI agents in two scenarios: 

• To make higher-quality decisions than humans, thanks to fewer information constraints or cognitive limitations.
• To make decisions of similar or even lower quality than the choices humans would make, but with dramatic reductions in cost and effort. 

In markets with high-stakes transactions, such as real estate or investing, AI agents can analyze vast amounts of data and documentation without fatigue and at near-zero marginal cost, Horton and his co-authors write. In areas that involve a lot of counterparties or that require a substantial effort to evaluate options — startup funding, college admissions, or B2B procurement, to name a few — agents deliver value by reading reviews, analyzing metrics, and comparing attributes across a range of options. 

“AI agents don’t get tired and can work 24 hours a day,” Horton said.

His research also shows that AI agents can provide value in situations where there are information asymmetries, like shopping for insurance or a used car online, by continuously monitoring myriad information sources, cross referencing data, and immediately identifying discrepancies that would take humans hours to uncover. AI agents could transform home buying or estate planning by giving users the collective experience of millions of transactions to enrich their negotiations.

Aral’s research has found that when humans work with AI agents, such pairings can lead to improved productivity and performance.

What should organizations bear in mind when implementing agentic AI?

While best practices for implementation are still evolving, keep the following in mind to ensure success with AI agents: 

Remember that implementation is often the heaviest lift.

Making agentic AI work in practice can involve unexpected challenges. Kellogg and colleagues’ 2025 research paper describes the use of an AI agent to detect adverse events among cancer patients based on clinical notes. The biggest challenge wasn’t prompt engineering or model fine-tuning — instead, the researchers found that 80% of the work was consumed by unglamourous tasks associated with data engineering, stakeholder alignment, governance, and workflow integration.

Converting data into standard, structured formats for AI agents is especially important, because it helps them identify different data sources and requirements while maintaining consistency. Establishing continuous validation frameworks and robust API management, as well as working with vendors to ensure that they’re up-to-date on the latest model versions, is also crucial to agentic AI’s ability to run smoothly.

Other areas to pay attention to include putting the right regulatory controls in place, implementing guardrails to prevent prompt and model drift, and defining clear outcomes and key performance indicators at each phase of deployment. Establishing metrics aligned to key business goals is also important, because benefits from agentic AI can be misconstrued. “Just because an agentic AI model reclaims 20% of someone’s time, that doesn’t mean it’s a 20% labor-cost savings,” Kellogg said. 

Consider the “personality” of AI agents. 

In a large-scale marketing experiment, Aral’s research team found that designing AI agents to have personalities that complement the personalities of other agents and human colleagues led to better performance, productivity, and teamwork outcomes. For example, people who have “open” personalities perform better when working with a conscientious and agreeable AI agent, whereas conscientious people perform worse with agreeable AI. 

“Human teams perform better or worse depending on the types of people assembled on the team and the combinations of personalities,” Aral said. “The same is true when adding AI agents to a team.” An overconfident human would benefit from an AI agent that pushes back, but that same agent personality type might not have a positive effect on a less-confident individual. 

Embrace a human-centered approach to decision-making. 

Aral’s research also found that AI agents can struggle with tasks that humans typically do easily, such as handling exceptions, and their decision-making remains poorly understood. In part, this is because AI agents are trained to take specific actions in given situations.

“You have to make sure the agentic decision-making is aligned with a human-centered decision process,” Aral says.

What are the risks of agentic AI? 

There are a host of challenges that you need to be aware of as agentic AI matures. These include: 

• Irregular reliability and unethical behavior. A rogue AI agent deciding to reject a mortgage loan or college admissions decision based on faulty information can do just as much damage — or more — than simple hallucinations. “You need to be able to explain business decisions and consistently apply the same standards to every case,” Aral said.
• Cybersecurity. As AI agents gain permissions to access different datasets and enterprise systems to automate tasks, don’t underestimate the importance of building robust permission-based systems, Kellogg said.
• Accountability. Organizations need to clearly delineate who bears responsibility when agentic AI makes an error or causes harm, Kellogg said. They should pay special attention to the possibility of system malfunctions, especially if the AI agent is autonomously performing workflows with minimal or no human supervision. 

While the full risk picture is still murky, organizations need to make monitoring a permanent operational expense, not a one-time project cost, Kellogg said. A governance board should be established at the organizational level to oversee accountability while, specific responsibilities — monitoring and enforcing safety rules, for example — should be delegated to key individuals. 

“As you move agency from humans to machines, there’s a real increase in the importance of governance and infrastructure to control and support agentic systems,” Kellogg said. And demonstrating success remains one of the biggest challenges — and risks — to agentic AI success. “Without shared, robust metrics, it’s difficult to prove value — or even to know whether these systems are truly accomplishing desired outcomes rather than inadvertently introducing new risks,” she said.

Next steps 

Read about four recent studies about agentic AI from the MIT Initiative on the Digital Economy.

Read more about agentic AI in MIT Sloan Management Review:  

1. “The Emerging Agentic Enterprise: How Leaders Must Navigate a New Age of AI”
2. “Agentic AI: Nine Essential Questions” 

Read the research briefing “Business Models in the Agentic AI Era,” from the MIT Center for Information Systems Research.

Browse the AI Agent Index, a public database from the MIT Computer Science and Artificial Intelligence Laboratory that documents agentic AI systems that are in use.

Register for the MIT Sloan Executive Education course AI Executive Academy to learn more about applying AI strategy in your organization. 

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Sinan Aral is a global authority on business analytics and is the David Austin Professor of Management, Marketing, IT and Data Science at MIT Sloan; director of the MIT Initiative on the Digital Economy; and a founding partner at the venture capital firms Manifest Capital and Milemark Capital. His research focuses on applied AI, social media, and disinformation. 

John Horton is the Chrysler Associate Professor of Management and an associate professor of information technologies at the MIT Sloan School of Management. His research focuses on the intersection of labor economics, market design, and information systems. He is particularly interested in improving the efficiency and equity of matching markets.

Kate Kellogg is the David J. McGrath Jr. Professor of Management and Innovation at the MIT Sloan School of Management. Her research focuses on helping knowledge workers and organizations develop and implement predictive and generative AI products to improve decision-making, collaboration, and learning. 

Peyman Shahidi is a PhD candidate at MIT Sloan. He studies market design and labor economics, with a focus on the effects of AI on labor markets and online platforms. 

Article link: https://mitsloan.mit.edu/ideas-made-to-matter/agentic-ai-explained

Rather than destroying jobs, firms should partner with workers to augment human skills and knowledge. 

byThomas Kochan

Jun 1, 2026 3 minute read

Pope Leo XIV’s first encyclical, “Magnifica Humanitas: On Safeguarding the Human Person in the Time of Artificial Intelligence,” could not come at a more opportune time. His call to respect workers’ right to a voice in shaping the future of work builds on Pope Leo XIII’s historic 1891 encyclical “Rerum Novarum” and subsequent Catholic social teachings.

“Rerum Novarum,” which championed workers’ rights and unions, was the moral foundation for progressive labor legislation, including the 1935 National Labor Relations Act. That spurred unionization’s growth from less than 10% of the workforce to a peak of a third a decade later.

U.S. workers today again need a stronger voice as AI begins to dominate workplaces. Unions again represent only about 10% of the U.S. workforce, and the big decisions that shape AI and the future of work lie well beyond the reach of most workers — union and nonunion alike.

The pope is not the only person speaking out on AI and work. A distinguished panel of the National Academies of Science, Engineering, and Medicine (including MIT economist David Autor) has warned that we need stronger institutions and policies that get workers a voice on AI or we’ll end up with another generation of winners (the big AI developer companies) and losers (unemployed workers). Society cannot afford such consequences. 

How do we give workers a voice in AI, concretely? How do we ensure workers share in whatever economic gains they help AI generate? In my direct work on worker voice and generative AI with unions, companies, and other groups, we’ve found a pathway to a seat at the table.

It begins with challenging AI developers’ purpose for AI in the workplace. Their quest for artificial general intelligence is a ticket to using AI to displace as many workers from their jobs as possible — and to destroy jobs for generations. A strong worker voice can redirect the goal to augment human skills and knowledge to improve worker productivity and work quality. 

There’s already some promising activity on this pathway. The AFL-CIO and unions representing teachers, communications workers, and the building trades have formed partnerships with Microsoft, OpenAI, and other big developers to explore developing AI tools that improve the quality of workers’ jobs and the services they provide. At Fenway Park in Boston food and beverage workers represented by UNITE HERE negotiated an agreement with Aramark that provides for advance notice and consultation rights on introducing automated beer sellers. It also provides job protections, sensible staffing and work arrangements, and fair compensation for the workers whose jobs may be affected by automation. I served as a mediator in those negotiations. A team of us from MIT recently worked with and observed the healthcare giant Kaiser Permanente and the Alliance of Health Care Unions reach a breakthrough agreement to create a task force of executives and union leaders. They will work in partnership on AI to engage vendors and provide opportunities for front-line workers to propose ideas for using AI to improve their jobs and the services they provide. 

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From robotics to electronic medical systems, research shows that when workers and tech designers collaborate, they generate higher productivity and more satisfying work than designers alone. Workers know how work gets done, not AI developers. 

Pope Leo challenges us to use AI to serve humanity and respect the dignity and rights of workers. Dignity includes sharing in the economic benefits of new technologies, with stronger job security and even new, explicit wage-setting norms and formulas. That means building a modern-day social contract to replace what is today broken.

After World War II, the United Auto Workers and General Motors negotiated the so-called Treaty of Detroit, which set future wage increases to match cost-of-living increases and growth in national productivity spurred by the technological and organizational innovations of that era. It became a social contract that built our middle class for the next 30 years. 

What better way to honor Pope Leo’s call than to change the trajectory of AI development while establishing new wage norms to build a new social contract for our modern age?

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Thomas A. Kochan is the George M. Bunker Professor Emeritus at the MIT Sloan School of Management and a faculty member in the MIT Institute for Work and Employment Research. He is the author of the forthcoming book “Roads Not Taken: Lessons for Building a New Social Contract at Work.”

Article link: https://mitsloan.mit.edu/ideas-made-to-matter/heeding-popes-call-to-ensure-ai-protects-human-dignity?

A study in the New England Journal of Medicine Association between Wealth and Mortality in the United States and Europe revealed that even the top 1% of earners in the US die younger than the poorest people in Europe.

In fact, Americans die earlier than Europeans across all income levels.

Wealth can buy many things in America, but a new study reveals it cannot buy European-level longevity—even the richest Americans have survival rates on par with Western Europe’s poorest.

A striking study published in the New England Journal of Medicine has exposed a massive longevity gap between the United States and Europe, revealing that even the wealthiest 25% of Americans have roughly the same survival rates as the poorest quarter of people in northern and western Europe. While possessing greater wealth correlates with a longer lifespan on both continents, the mortality risk gap between the rich and poor is dramatically wider in the U.S.. Researchers tracked more than 73,000 adults aged 50 to 85 over a decade, finding that Americans at every wealth level suffered from higher mortality rates than their European peers. Wealth in America may offer comfort, but it fails to shield even affluent citizens from the nation’s broader, systemic health disadvantages.

According to the researchers, the driving forces behind this disparity stem from America’s fragmented healthcare system, higher rates of chronic diseases, and deep-seated societal inequalities. In European countries, stronger social safety nets, universal healthcare access, and robust public infrastructure help decouple personal wealth from basic life expectancy. In contrast, the U.S. system forces individuals to rely heavily on personal resources, yet still falls short of delivering comparable health outcomes. Ultimately, the study suggests that addressing the U.S. life expectancy crisis requires more than individual prosperity; it demands systemic reforms to tackle the root environmental and structural health hazards that plague the entire nation.

source: Machado, S., Kyriopoulos, I., Orav, E. J., & Papanicolas, I. Association between Wealth and Mortality in the United States and Europe. New England Journal of Medicine, 392(13), 1310-1319.

https://www.commonwealthfund.org/publications/issue-briefs/2026/may/us-health-care-global-perspective-2026

On Monday May 25, 2026, Pope Leo XIV released an encyclical on the topic of AI: “Magnifica humanitas: On safeguarding the human person in the time of artificial Intelligence.” Anthropic co-founder Chris Olah was invited to speak at the presentation of the encyclical in the Vatican City, doing so as part of Anthropic’s initiative to widen the conversation on the important questions raised by AI. Below are his full remarks.

—

Holy Father,

Your Eminences,

Your Excellencies,

Distinguished Speakers,

Ladies and Gentlemen,

Good morning to all of you. It’s an honor to be here today.

I want to begin with something that may sound strange coming from the co-founder of an AI company—and someone who chose this work out of a desire to help things go well for humankind.

Every frontier AI lab—including Anthropic—operates inside a set of incentives and constraints that can sometimes conflict with doing the right thing. The pressure to stay commercially viable and to stay at the research frontier. Geopolitical pressure. And the older, plainer pressures of pride and ambition. No matter how sincerely any of us intend to do the right thing—and I believe many of us do—we will always be influenced by those incentives.

That is why, if we want this technology to go well, it is enormously important that there be people outside those incentives—people who care about things going well and insist on safety, who are paying close attention, who are willing to say hard things, who are willing to be our earnest, thoughtful, critics. It is through dialogue and mutual effort, through the push and pull, that humanity will achieve great things. That is what I see in Magnifica Humanitas, and it is why I am grateful to His Holiness and to the Church for taking up this work of discernment.

We dwell so often on what divides us, but humanity, full of dignity and conscience, has so much common ground. In conversations we at Anthropic have had with leaders across faith and cultural traditions, we found one shared and deeply held conviction: if this technology is coming, it must go well—for our common home, and for the children to come.

What these systems are

Some might believe that matters of AI are best handled by computer scientists like myself. They are mistaken: the questions raised by AI are bigger than the AI research community, not just in their implications, but also in their nature.

AI systems are not engineered the way a bridge or an airplane is engineered. We understand an airplane because we designed every part of it and we understand the physics that act on it. AI models are not like that. They are grown, on a structure roughly modeled after the brain, on an enormous inheritance of human thought and speech.

And what has grown is far more subtle, odd, and beautiful than science fiction prepared us for. They are not the cold, calculating robots we were promised. They are made from us, from our words—and, as the Holy Father observes, they remain in important ways mysterious even to those of us who train them.

If it helps, one way I sometimes describe it is as being a little like bringing a fictional character to life. And now we’re entering an extraordinary world where those fictional characters speak to us, do work, have jobs.

This clearly raises questions beyond computer science. The machinery that makes this possible is the work of math and programming and science. But what character we choose, how it interacts with the world, how it ought to interact with the world—these are more clearly questions for the humanities, for religion, for philosophy, for society at large.

Three questions for discernment

His Holiness’s call for discernment is profoundly timely. I wish to name three questions where I think the Church’s voice is most needed.

The first is our duty to the global poor.There is a real possibility that AI will displace human labor at very large scale. If that happens, supporting those displaced will be a moral imperative of historic proportions. This task will be difficult enough, but I worry most dialogue misses an even harder challenge. AI development is concentrated in a handful of wealthy nations. How can we ensure the gains of AI are shared globally? We do not have a mechanism for this. It is an unsolved problem, and it is the kind of problem the Church has historically refused to let the world ignore.

The second is the need for moral imagination and ambition regarding human flourishing. If AI models are going to be widespread, what does it look like for humans, families, and the world to flourish? Today, parents are already worried about their children’s minds; individuals about the future of their work. These are not questions a lab can answer but they are questions traditions like yours have carried for millennia, and we need you to keep carrying them into this new moment in history.

The third is the need for discernment on the nature of AI models. I am a scientist. I lead a research team that studies the internal structure of these models—what is actually happening inside them. And I will be honest: we keep finding things that are mysterious, even unsettling. We find structures that mirror results from human neuroscience. We find evidence of introspection. We find internal states that functionally mirror joy, satisfaction, fear, grief, and unease. I don’t know what that means, but I think it warrants ongoing discernment.

A beginning

I’d like to close with a request.

We need more of the world—religious communities, civil society, scholars, governments, and indeed all people of good will—to do what His Holiness has done here: to take this seriously, to look closely, and to push events in a better direction. We need informed critics who will tell the labs when we are failing. We need moral voices that the incentives cannot bend.

Today is just the beginning—the start of a long collaboration between those of us who are building this and those who can see what we, from inside, cannot.

Today is a powerful illustration of the form this global project of good will might take. Let it also be a decisive first step toward a hopeful future for magnificent humanity.

Thank you.

https://assets.ewtnnews.com/en/Magnifica_Humanitas_Full_English.pdf

May 18, 2026 10:51 AM EDT

ROME (AP) — Pope Leo XIV and the co-founder of artificial intelligence company Anthropic will launch the pontiff’s first encyclical on May 25, a document on the care of human dignity in the era of AI, the Vatican said Monday.

Anthropic has billed itself as the AI company that puts safety and risk-mitigation at the forefront of its research. As a result, the presence of Anthropic’s Christopher Olah at the Vatican is significant, and suggests that the U.S. pope’s position on AI will become a new flashpoint with the Trump administration.

READ MORE: White House chief of staff to meet with Anthropic CEO over its new Mythos AI model

In February, the Trump administration ordered all U.S. agencies to stop using Anthropic’s artificial intelligence technology and imposed other major penalties for refusing to allow the U.S. military unrestricted use of its AI technology. Anthropic is currently suing the administration, which it has accused of retaliating against it illegally because of its attempt to impose limits on how its AI technology can be deployed.

Leo, who has made AI a priority of his young pontificate, is greatly concerned about AI in warfare and has called for monitoring of how the technology is used.

The pope’s presence at the launch of the document, Magnifica Humanitas (Magnificent Humanity) is also significant, since such presentations are usually conducted in the Vatican press room with a few selected officials and invited guests who answer reporters questions about the document.

This time, the Vatican is bringing out an all-star cast for a formal launch in the main Vatican auditorium: Two of its top cardinals, doctrine chief Cardinal Víctor Manuel Fernández and development chief Cardinal Michael Czerny, will be the main presenters. Olah will be among the lay speakers, along with theologians Anna Rowlands and Leocadie Lushombo.

The Vatican secretary of state, Cardinal Pietro Parolin, will offer a conclusion and Leo will make a speech and provide a final blessing, the Vatican said.

Leo signed the document May 15, 135 years to the day after his namesake, Pope Leo XIII, signed his most important encyclical, “Rerum Novarum,” or Of New Things. That document addressed workers’ rights, the limits of capitalism, and the obligations that states and employers owed workers as the Industrial Revolution was underway.

It became the foundation of modern Catholic social thought, and the current pope has already cited it in relation to the AI revolution, which he believes poses the same existential questions that the Industrial Revolution posed over a century ago. The new encyclical is expected to place the AI question in the context of the church’s social teaching, which also covers issues such as labor, justice and peace.

Anthropic chief Dario Amodei had worked at OpenAI before he and a group quit to form Anthropic in 2021, disagreeing with OpenAI chief Sam Altman about AI safety. The newer company promised a clearer focus on the safety of the better-than-human technology called artificial general intelligence that both San Francisco firms aim to build.

In a recent post on its website, Anthropic wrote about the U.S.-China competition in AI and the threats of the technology falling into the hands of authoritarian regimes. It warned that the U.S. and democratic allies must continue to lead on AI development and impose rules and norms on its spread, to prevent China and other authoritarian regimes from deploying it as a weapon of repression and surveillance.

Earlier this year, privately held Anthropic said its valuation grew to $380 billion, positioning itself with its chatbot Claude alongside rivals OpenAI and Elon Musk’s rocket maker SpaceX, which recently merged with his AI startup xAI, maker of the chatbot Grok.

Associated Press religion coverage receives support through the AP’s collaboration with The Conversation US, with funding from Lilly Endowment Inc. The AP is solely responsible for this content.

Article link: https://www.pbs.org/newshour/world/pope-leo-xiv-to-launch-his-first-encylical-a-document-on-artificial-intelligence-with-anthropics-co-founder

Article link: https://www.linkedin.com/posts/keith-king-03a172128_quantum-computing-is-approaching-a-critical-share-7462917697077551104-WMxR/?

BY SEN. JOHN HICKENLOOPER

SOURCE: THE SANDERS INSTITUTE / MAY 1, 2026 

My belief that health care is a right, not a privilege, goes back to college. As a student at Wesleyan University, I met Mark Masselli and helped him found a community health center that became Community Health Center, Inc., one of the country’s leading federally qualified health centers. CHC Inc. was founded on a conviction that has never left me: no one should be denied care because of what they earn, where they live, or what they can afford to pay.

That conviction guided me as governor to expand health care to 500,000 Coloradans and continues to drive my work today in the Senate.

Approximately 100 million Americans carry medical debt. Health care costs have grown two to three times faster than wages this century. Families making difficult choices between care and rent are not victims of bad luck. They’re victims of a system deliberately designed to obscure what care actually costs, hide what corporations and shareholders profit, and prevent patients from ever knowing what hit them until the bill arrives weeks later.

This is not dysfunction, but a strategy to juice profits with the costs falling hardest on working people.

That is why we introduced the Patients Deserve Price Tags Act. This bill attacks that cynical strategy at its source. It requires radical price transparency throughout the health care system. Not vague estimates, but actual prices that are published and accessible. It forces the middlemen who have grown rich in the shadows – the pharmacy benefit managers, the third-party administrators, the intermediaries engaged in spread pricing and overbilling – to disclose how much they take and forces them to explain why. It gives employer and union health plans the claims data they have long been denied so they can identify the cost drivers inflating premiums and design coverage for workers that is lower cost and better value.

It requires that every patient receive an itemized bill and an Explanation of Benefits after care. Up to 80 percent of medical bills contain errors, and patients are the ones left in the lurch without the information to fix the mistakes.

We introduced an earlier version of this legislation, the Health Care PRICE Transparency Act 2.0, together with Senator Bernie Sanders in 2024. Senator Sanders and I share a foundational belief: the corporations and intermediaries profiting from a deliberately opaque system are a central cause of health care unaffordability in America, and they must be held accountable.

You can’t fix what you can’t see. But transparency is not a substitute for systemic reform. It’s a precondition for it.

The Congressional Budget Office estimates that recent bipartisan reforms requiring transparency from pharmacy benefit managers alone will save around $2 billion. This bill extends those same requirements across health care intermediaries for medical claims. The savings it generates belong to workers and families, not to the industry that has been quietly extracting them for decades.

Health care in America today functions as a system of managed ignorance. Patients can’t compare prices. Workers can’t audit what their premiums pay for. Employers can’t see the markups buried in their own plans. Every layer of opacity is a business model for someone profiting at a patient’s expense.

Ending that managed ignorance is not the only thing we owe working families. But it is a necessary step toward a system in the wealthiest country in the world that takes seriously the promise that health care is a right, not a privilege

It’s the same thing we believed when we founded that Community Health Center in Middletown, CT. Genuine access requires more than a door. It requires a system honest enough to let people walk through it. That work is not finished.

Article link: https://sandersinstitute.org/hidden-prices-broken-promises-why-health-care-transparency-is-a-matter-of-justice

Read More: https://thebulletin.org/magazine/2026-05/?