A Real-Time Look at How America's Healthcare Rules Are Made
Imagine a massive, continuous experiment that determines how over 300 million Americans access healthcare. This isn't confined to a laboratory; it unfolds daily in congressional hearings, state agencies, and clinical settings across the nation. Health policymaking represents one of America's most complex and consequential scientific processes—a living system where theories of economics, sociology, and political science are tested in real-time, with human well-being as the ultimate metric.
The year 2025 has become a particularly rich case study in this ongoing experiment. With the passage of the One Big Beautiful Bill Act (officially the 2025 Budget Reconciliation Act) and numerous other regulatory changes, we're witnessing a dramatic reshaping of America's healthcare landscape 1 6 . These changes offer a rare opportunity to observe health policymaking in action—testing established theories about how policies influence behavior and ultimately determine who receives care and under what circumstances.
This article examines health policymaking through the lens of these recent developments, treating policy shifts as natural experiments that reveal the intricate mechanisms connecting legislation to human outcomes. By applying scientific scrutiny to the policy process, we can better understand this powerful force that touches every American's life.
At its core, health policymaking operates on theoretical frameworks that predict how various interventions will change behavior within the healthcare system. These frameworks represent educated hypotheses about the cause-and-effect relationships between policy levers and health outcomes.
This approach assumes that individuals and institutions make calculated decisions to maximize benefits and minimize costs.
This perspective focuses on how rules and organizational structures shape behavior.
Rather than sweeping overhaul, this theory suggests policy evolves through small, gradual adjustments.
Increasingly, policymakers point to data and research to justify decisions.
| Theory | Core Principle | Policy Example | Predicted Outcome |
|---|---|---|---|
| Rational Choice | Actors maximize benefits while minimizing costs | Increased cost-sharing for Medicaid expansion adults 1 | Reduced "unnecessary" healthcare utilization |
| Institutional | Rules and structures dictate behavior | Medicaid work requirements 1 6 | Increased workforce participation; reduced enrollment |
| Incremental | Change occurs through small adjustments | Temporary telehealth extensions 9 | Flexible policy adaptation with ongoing evaluation |
| Evidence-Based | Data should drive policy decisions | WHO's theories of change guide 5 | More effective, accountable interventions |
The passage of the 2025 Budget Reconciliation Act represents one of the most significant healthcare policy experiments in recent years 1 6 . This legislation implements multiple simultaneous interventions across Medicaid, Medicare, and Affordable Care Act marketplaces, creating a natural laboratory for observing policy effects.
Projected reduction in federal healthcare spending by 2034
Projected increase in uninsured Americans by 2034
The law's centerpiece involves fundamental changes to Medicaid, the nation's largest health insurance program. By introducing federal work requirements, restricting eligibility for immigrants, and increasing the frequency of eligibility redeterminations, the legislation tests several theoretical propositions about how to structure public benefits 1 .
The Congressional Budget Office, which serves as something of an institutional research lab for Congress, has projected the experiment's outcomes: reductions in federal healthcare spending by over $1 trillion alongside an increase of 10 million uninsured Americans by 2034 1 . These projections represent the official hypothesis about the law's effects—predictions that will now be tested against real-world outcomes.
Parallel to this massive experiment, another policy intervention—the telehealth "policy cliff"—creates a contrasting natural experiment 9 . Here, the research question revolves around what happens when temporary pandemic-era flexibilities expire. The experimental design is straightforward: observe differences in healthcare access and utilization before and after the policy reversal, particularly for vulnerable populations who have come to rely on remote care.
If the 2025 Budget Reconciliation Act is the experimental intervention, then its implementation constitutes the methodological protocol. This process transforms legislative language into operational reality through precisely defined steps:
States must verify compliance through monthly documentation—a data collection method that itself may influence outcomes.
The legislation allows states to request compliance exemptions until December 2028 if demonstrating "good faith effort" 1 .
The policy includes a notable methodological control: individuals denied Medicaid coverage due to work requirements become ineligible for subsidized Marketplace coverage 1 .
DACA recipients become ineligible for ACA Marketplace coverage 6
Medicaid work requirements and immigrant eligibility restrictions begin 1
Retroactive Medicaid coverage limited; address verification requirements begin 1
Cost-sharing requirements for some Medicaid beneficiaries 1
While the 2025 policy experiments are still unfolding, preliminary results and projections offer insight into their potential effects. The Congressional Budget Office's estimates provide the baseline against which actual outcomes will be measured:
| Policy Provision | 10-Year Federal Savings | Projected Coverage Loss (by 2034) |
|---|---|---|
| Medicaid Work Requirements | $326 billion | 5.3 million people |
| Biannual Eligibility Checks | $63 billion | 700,000 people |
| Delayed Implementation of Streamlined Enrollment | $56 billion | 400,000 people |
| Restricted Immigrant Eligibility | $6 billion | 100,000 people |
| Limited Retroactive Coverage | $4 billion | 100,000 people |
Early analyses indicate that documentation requirements may function as a more significant barrier to coverage than the work requirements themselves 6 .
As the CBO projects coverage losses, healthcare providers—particularly rural hospitals that derive 20-50% of revenue from Medicaid—face financial strain 6 .
The simultaneous implementation of multiple Medicaid restrictions appears to create synergistic effects on coverage loss that may exceed the sum of individual provisions 6 .
The parallel telehealth experiment presents contrasting preliminary findings. Research on telehealth utilization during the pandemic flexibility period suggests particularly significant benefits for behavioral health access and chronic disease management 4 9 . The impending expiration of these policies thus sets up a natural experiment in treatment withdrawal, allowing researchers to observe what happens when an established benefit is removed.
Just as laboratory science relies on specific tools and reagents, evidence-based health policymaking employs its own set of analytical instruments:
The World Health Organization now promotes structured "theories of change" as essential policy tools 5 . These documents map the logical pathway from intervention to outcome.
The Congressional Budget Office uses sophisticated statistical models to project policy effects—akin to running in silico experiments before implementing real-world interventions 1 .
Researchers employ quasi-experimental methods like difference-in-differences analysis to study policy changes 6 .
The Commonwealth Fund emphasizes centering community as a critical value in health system redesign 7 .
This growing discipline systematically studies how policies translate into practice—examining the crucial difference between policy as designed and policy as implemented 6 .
Health policymaking in the United States remains an imperfect science—a series of iterative experiments where hypotheses are tested not in controlled laboratories but in the complex ecosystem of American healthcare. The 2025 policy changes represent the latest intervention in this ongoing investigation, with findings that will shape subsequent rounds of policy development.
What makes this scientific process uniquely challenging is its high stakes and limited controls. Unlike bench science, there are no true control groups in policymaking—only comparison populations who themselves experience some mix of policies. The "experimental subjects" are real people whose health and financial security hang in the balance.
Yet the scientific framework remains invaluable for understanding this process. By examining policies as testable interventions, measuring their effects against predictions, and transparently reporting results, we can gradually refine our approach to healthcare governance. The ultimate goal remains the same as in any scientific endeavor: to incrementally expand our knowledge and improve outcomes through evidence, observation, and rigorous analysis.
Its results will shape not only future policy decisions but also our fundamental understanding of how healthcare financing and delivery systems influence human health and wellbeing. In the demanding laboratory of American democracy, we are all both researchers and research subjects in this ongoing investigation.