U.S. Medicine Use Trends 2026

 

Summary

The U.S. healthcare system is undergoing a significant transformation, shaped by shifting patterns in medicine use, evolving patient cost burdens, changing benefit designs, and rising spending driven by innovation. At the same time, structural barriers in access and affordability continue to present persistent challenges for patients, often leading to patients who may need medicines the most not receiving them. Together, these dynamics define a market that is expanding in complexity while also signaling important opportunities for system-level improvements. As discussions continue around access and affordability, policymakers, payers, and manufacturers have an opportunity to implement meaningful change to ensure the sustainability of the U.S. healthcare system and that patients are able to benefit from the full potential of medical advances.

Areas of focus in this year’s report range from looking at how medicine usage patterns have shifted, to the impact of out-of-pocket costs and benefit designs on patients, to the complex nature of drug pricing. Evolving trends in 2025 and recent policies have driven significant revisions to the outlook, and in this report, the drivers of change in medicine spending over the next five years are deconstructed to enable a better understanding. This examination includes the impact of novel obesity and diabetes medicines and the uptake of other innovative brands that are driving medicine spending.

Health Train Express reports on the IQVIA Institute symposia (online) on July 15,2026.

The complete report can be ACCESSED Here.  (full disclosure, )

Key Findings

Medicine use has increased:

Total prescription medicine use increased 1.5%, reaching 210 billion days of therapy in 2025

Vaccinations have had mixed trends, with seasonal vaccines seeing significant declines in the latest season, while many routine vaccines increased in 2025

Some patients see out-of-pocket cost reductions:

Patient out-of-pocket costs reached a record $110Bn in 2025, increasing by $6Bn

Implementation of the Medicare Part D out-of-pocket cap reduced overall spending by Medicare beneficiaries, offset by increases in other pay types driven by GIP/GLP-1s

Patients continue to see barriers to medicine access:

Nearly two-thirds of prescriptions for newly launched drugs go unfilled in the first year on the market, and limited coverage persists for several years

Spending on medicines has accelerated:

The U.S. market at net prices grew 10.6% in 2025 and an average of 9.3% annually over the last five years

GIP/GLP-1 agonists and COVID-19 medicines have had significant impacts on spending growth since 2020

Growth will slow through 2030:

U.S. medicine spending at net prices is forecast to grow 4.5 to 7.5% through 2030, while 6 to 9% at list prices

Pricing pressures and patent expiries will slow growth through 2030 offset by continued uptake of innovative therapies

Other Findings

The use of prescription medicines in the U.S. — based on defined daily doses — has grown 13% in the last five years to 210 billion days of therapy across both retail and non‑retail settings, although growth slowed beginning in 2024.

Retail drugs currently represent 84% of medicine use in the U.S., with only 16% in non‑retail settings, and non‑retail growth exceeded retail growth in 2025.

The use of prescription drugs dispensed from retail pharmacies has continued to grow at an average annual rate of 2.4% over the last five years, with much slower growth in 2024 and 2025, reducing total market growth.

Out‑of‑pocket costs rose in aggregate for commercially insured patients, Medicaid beneficiaries, and those who paid cash, while Medicare out‑of‑pocket costs declined, largely driven by the Medicare Part D out‑of‑pocket cap implemented in 2025.

Commercial insurance out‑of‑pocket costs, which account for 52% of total patient out‑of‑pocket costs, rose 5% in aggregate in 2025 and 37% over five years due to increased volume and a shift towards higher‑cost prescriptions.

Medicare out‑of‑pocket costs declined by $638 million (2.2%) in aggregate in 2025 following the implementation of the Medicare Part D out‑of‑pocket cap; however, costs remain more than $5.3 billion (23%) higher than in 2020, driven by increased volume and shifts in prescription mix.

Between 2020 and 2024, 99 novel medicines were launched in retail and mail channels in the U.S., often providing benefits over standard of care or addressing unmet needs. For these medicines, 7 million new prescriptions were written in the first year of availability, with 64% related to RSV vaccines alone.

On average, 35% of these prescriptions were filled, while 65% went unfilled, including an average of 49% rejected by payers and 17% abandoned by patients after payer approval, likely due to high out‑of‑pocket costs.

During the first four years a new medicine is on the market, fill rates improve; however, by year four, more than half of new prescriptions still go unfilled, significantly higher than the 29% unfilled rate across all brands and branded generics.

Net medicine spending increased by $58 billion (10.6%) in aggregate, rising from $548 billion in 2024 to $606 billion in 2025, with most growth driven by protected brands outside GIP/GLP‑1 agonists and COVID‑19 vaccines and therapeutics.

GIP/GLP‑1 agonists across diabetes and obesity contributed $14 billion in growth, with $9.6 billion concentrated in products approved for obesity and related comorbidities.

COVID‑19 vaccines and therapeutics, which contributed to spending growth in 2024, declined by $4 billion in 2025.

Other evolving issues include artificial intelligence, pharmacy benefit managers, and authorization procedures.

Total net spending on medicines in 2030 is expected to increase by $200 billion compared with 2025, as volume growth and innovation adoption are partly offset by lower‑price drivers such as patent expiries and policy effects.

Over the next five years, medicine spending is projected to grow between 6–9% on a list‑price basis and 4.5–7.5% after discounts, rebates, and other price concessions.

Growth will be driven by the adoption of newly launched innovative products, with an average of 50–55 new medicines expected to launch annually over the next five years, including therapies in oncology, immunology, and other specialty areas, as well as more traditional treatments in diabetes, obesity, and neurology.

Research Brief | U.S. Medicine Use Trends 2026

A concise overview of the latest trends in U.S. medicine use, spending, and patient access in 2025. This video highlights continued growth in prescription use, the impact of innovative therapies such as GIP/GLP-1 agonists, and shifting dynamics across therapy areas and care settings. It also examines persistent affordability and access challenges, including rising out-of-pocket costs, payer restrictions, and evolving insurance design.

Author's Addendum:

Artificial intelligence (LLM) has been touted for the past several years. Although great things are predicted for its use in healthcare, the ultimate outcome is cloudy.

There are thousands of models. Many of the current LLM generation are expensive, and each use requires tokens.

AI model costs vary widely depending on whether you are paying for an end-user subscription or API usage (paying per million tokens). 

1. Consumer Subscriptions

Most flagship platforms (e.g., ChatGPT Plus, Claude Pro, Perplexity Pro) share a standard rate of $20/month. Premium models and creative platforms (e.g., Midjourney, Google AI Ultra) range from $30 to $250/month, depending on image limits and advanced reasoning access. 

2. API Usage (Pay-per-Token)

For developers and businesses, costs scale with token usage (text chunks). Output tokens typically cost 3 to 8 times more than input tokens: 

Budget/Mini Models: (e.g., GPT-4o Mini, DeepSeek V4 Flash, Gemini 2.5 Flash). Rates average $0.15 to $1.00 per million tokens. Ideal for high-volume, simple tasks. 

Standard Tier: (e.g., Claude 3.5 Sonnet, GPT-5 series). Rates range from $3 to $15 per million tokens. These offer the best balance of reasoning and cost. 

Premium/Reasoning Tier: (e.g., Claude Opus, o1 Pro, Gemini 2.5 Pro). Rates can exceed $20 to $600+ per million tokens. Reserved for highly complex coding or research. 

3. Open-Source vs. Proprietary

For large-scale enterprise needs, open-weight models (e.g., Llama 3, Muse Spark) are free to use if you host them yourself, though they require paying for cloud infrastructure like AWS or specialized GPU servers. 

How to get rid of deepfake doctors | And Deepfake Imaging (X-rays)

By Henry Bair

July 14, 2026

Bair is a resident physician at Wills Eye Hospital in Philadelphia and a physician-writer.

Imagine a patient who arrives at her doctor’s clinic furious. She shows her doctor a video of him — white coat, plausible exam room, familiar cadence — endorsing an over-the-counter hormone supplement for menopausal symptoms, dismissing standard therapies as “pharma scams,” and offering a discount code.

But the physician never recorded that message. Someone built a deepfake from online recordings, including interviews, webinars, and patient-facing videos, and used the synthetic likeness to sell an unregulated product. This scenario is no longer hypothetical. Investigations have documented AI-generated videos impersonating specific clinicians whom they name to promote supplements and other dubious treatments on major platforms. 

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Medical misinformation is often treated as a content problem: debunk falsehoods, reduce amplification, pressure platforms to remove harmful posts. Deepfakes push this problem into new terrain. They undermine the credibility that makes digital care, ranging from telehealth visits to patient portals and even social media, possible. And they raise a basic question: What, and whom, can patients trust?

Call this underlying requirement ”epistemic security”: the degree to which clinicians and patients can believe that what they see, hear, and document is authentic enough to act on safely. In digital medicine, that trust rests on three layers: identity (who is speaking), record (what belongs in the chart), and evidence (what we accept as real findings). Deepfakes threaten all three.

First, identity. Deepfake “doctors” exploit the cues patients are taught to trust: a familiar face, a clinic logo, the tone of professional certainty. For patients, it becomes harder to know whether a video featuring their doctor is genuine advice or a persuasive imitation. For clinicians, a second hazard emerges: the “liar’s dividend.” When forgery is plausible, people who did say something reckless can deny authentic recordings as synthetic. The result is not only deception, but degraded accountability. 

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 STAT Plus: Can you spot a deepfake X-ray? Neither can your radiologist

Second, the clinical record and workflow. Consider a voice-cloned “attending” calling overnight to change an opioid dose; a resident, hearing a trusted voice amid urgency and fatigue, complies. Or consider a telehealth encounter for a controlled substance: the patient presents with a synthetic face and a borrowed identity, and the documentation looks routine until a discrepancy triggers review. Deepfake methods also threaten diagnostic media. Researchers have demonstrated that deep learning can tamper with medical images while preserving a clinically plausible appearance. If images, recordings, or screenshots can be manipulated without leaving a trace, the electronic health record risks becoming less a faithful chronicle and more another contested digital artifact, undermining continuity, collaboration, and trust in documentation.

Third, the evidence base. Generative models can create synthetic datasets that, when responsibly governed, may protect privacy or support method development. But the same tools reduce the cost of fabrication. Paper mills already manufacture manuscripts and results at scale, and an investigation published in Nature has described how industrialized fraud and untrustworthy clinical trials already exist in the literature. A future in which bad actors generate a “trial,” complete with convincing tables, figures, and patient trajectories, should not be dismissed as fanciful. Even if fully synthetic randomized trials are not yet documented as being passed off as real-patient evidence, the incentives are obvious: publication, promotion, and profit.

The scale of this problem in routine clinical practice is not yet well measured. But in safety-critical systems, uncertain prevalence is not a reason for complacency. These attacks are cheap, scalable, and asymmetric: They can be created in minutes and may take hours to unwind, with consequences that spill across patients and institutions.

The response should not be framed solely as an arms race to detect every forgery. Detection matters, but adversarial systems evolve. What health care needs is a practical trust infrastructure, or norms and controls that make verification routine. The National Institute of Standards and Technology has emphasized layered approaches — provenance, watermarking, detection, and auditing — because no single method is sufficient.

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Health systems can take near-term steps without waiting for regulation: 

• Establish trusted channels. Clinical instructions and results must be delivered through the patient portal or a verified phone tree, not screenshots, forwarded videos, or social-media clips.
• Require verification for high-risk requests. Medication changes, controlled substances, urgent orders, and major care-plan changes should trigger callback verification to a known number or a two-factor check, and the chart should record that it occurred.
• Treat provenance as patient safety. For high-stakes clinical media such as key diagnostic photographs, select imaging exports, clinician-recorded audio/video, retain a tamper-evident chain of custody showing when the file was created and how it was edited. Emerging standards such as the Coalition for Content Provenance and Authenticity (C2PA) “Content Credentials” point toward how provenance can travel with media.
• Create a clear escalation pathway. “Suspected synthetic media” should be a one-click report to information security and risk management, so clinicians are not forced to improvise in real time.
• Train for the conversation. Clinicians need scripts for patients who bring fabricated “doctor videos” into the exam room. Patients need to know where a practice does (and does not) publish advice, and they should be explicitly invited to ask, “Did you actually say this?”

None of this is frictionless. Callback verification, provenance-aware media handling, and new escalation pathways add steps to already burdened clinical workflows, and many health systems will not have the IT infrastructure to implement them seamlessly. But the alternative is to leave frontline clinicians improvising when authenticity is in doubt. The goal should not be perfect verification of every artifact on day one; it should be a pragmatic, risk-based standard for the highest-stakes communications and media.

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Medical misinformation wins when patients can’t see their doctors

Regulators and platforms have parallel responsibilities. Using a clinician’s name, image, or credentials in a synthetic endorsement without consent should be treated as a deceptive practice, with liability directed toward those who commission or distribute the content. The Federal Trade Commission’s authority over deceptive advertising offers a ready lever when deepfakes are used as marketing. Journals and regulators should also tighten disclosure expectations when data are synthetic or heavily augmented, and require clear descriptions of how generated data were validated against observed data before clinical claims are made.

Deepfakes shift the burden of trust from recognition to verification. Digital medicine runs on an infrastructure of authenticity that is easy to take for granted — until it fails. We should rebuild that infrastructure now, before patients arrive not only uncertain about what is true, but uncertain about whether their clinician is real.

DEEPFAKE RADIOLOGY

Deepfake images can mislead viewers, upend elections, and instigate violence. They can also, researchers say, disrupt medical care.

In a study published Tuesday in Radiology, an international team of researchers tested whether 17 radiologists could tell the difference between real X-rays and those generated by ChatGPT. Only 41% noticed that anything was awry when they were initially asked to diagnose patients based on the synthetic images. Even once they knew to look out for deepfake X-rays, they only differentiated them accurately 75% of the time. 

 

How to get rid of deepfake doctors | STAT

Health and Human Services (HHS) Secretary Robert F. Kennedy Jr. has his sights set on remaking another influential health panel, one that determines what preventive medical services insurers must cover for free. 

After Kennedy blocked the panel from meeting on multiple occasions, declined to replace members whose terms had expired, and fired its leaders in May, the U.S. Preventive Services Task Force is finally set to convene in August, potentially with as many as eight new members.    Neither Kennedy nor HHS has publicly offered an explanation of why they are changing the panel or what they think its role should be, raising concerns of political interference into an independent panel which historically has been apolitical. 

The reason(s) are not transparent. Is it due to the economics of providing preventive care, which elevates healthcare costs for Medicare, Medicaid, and private payers?

Political issues such as Red vs. Blue factions also obfuscate the reasons, whether scientific (data) based or economic (budgetary) issues.

HHS did not respond to a list of questions about the secretary’s plans for the task force or why the administration has blocked the release of four recommendations.  

Senior HHS press secretary Emily Hilliard said only that because of an “unprecedented number of nominations received” for new members, the July meeting was postponed until late August “to allow additional time for selection and onboarding” of new members. 

Kennedy has repeatedly criticized the task force, accusing it of poor performance.  

RFK Jr.’s focus on preventive health panel provokes new fears

RFK Jr.’s focus on preventive health panel provokes new fears

Alzheimer's Disease New Therapies under Investigation;'

 

Treatment Advances for Alzheimer’s Disease: 

New medications for Alzheimer’s disease can help manage symptoms or slow the progression of the disease. Learn how these breakthrough treatment options can help improve the quality of life for people with Alzheimer’s disease and their caregivers. An adult woman helps an older adult woman put on a sock. New treatments for Alzheimer’s disease can help reduce symptoms like agitation and slow progression of the disease. Alzheimer’s disease is the most common form of dementia, affecting approximately 7.2 million older adults in the U.S. Until recently, treatment options for Alzheimer’s disease were focused on managing symptoms, rather than treating the disease itself. However, new therapies are helping to target the underlying disease, slowing progression for people living with Alzheimer’s disease. Understanding Alzheimer’s disease Alzheimer’s disease is a progressive brain disorder that slowly affects skills like memory and thinking. In the early stages of the disease, symptoms can include: asking the same questions repeatedly, losing items, showing a lack of judgment difficulty making decisions forgetting about recent events or conversations issues thinking of the right word problems remembering the names of places or objects being hesitant to try new things A person with late-stage Alzheimer’s disease can find it difficult to perform everyday tasks like holding a conversation. As the disease becomes more severe, a person may not be able to communicate and may spend all their time in bed. While Alzheimer’s disease starts with mild memory loss and confusion, a person with a severe form of the disease requires full-time caregiving. People with Alzheimer’s disease generally live an average of 4 to 8 years after diagnosis. However, depending on specific factors, a person can live up to 20 years. On average, Alzheimer’s disease affects people ages 65 or older, but it’s not a typical part of aging. The condition is caused by complex brain changes, primarily the buildup of abnormal amyloid plaques and tau tangles, which lead to cognitive decline.

Neutrofibullary Tangle of. Amyloid Plaque, surrounding a Neural Cell in Brain

The U.S. federal government is currently investing approximately $3.9 billion annually into Alzheimer’s and dementia research. This massive funding is primarily distributed through the National Institutes of Health (NIH), following a finalized $100 million increase signed into law in early 2026. Additional non-profit and private financial commitments augment these federal figures: Alzheimer's Association: Made a record-breaking single-year investment of $112.2 million into scientific investigations in 2025, bringing its active portfolio to over $450 million across 56 countries. Private Philanthropy: High-profile private donors also contribute heavily to the space; for example, Bill Gates has personally invested over $300 million in Alzheimer's research and prevention. 

Track how these funds are distributed by exploring the NIH Professional Judgment Budget or reviewing active projects via the Alzheimer's Association Research Progress tracker. Would you like to know more about: Where the NIH money is specifically allocated (e.g., drug trials, AI research, or early detection). 

None of these proposed therapies reverses the disease.  However, they do slow the progress. 

How Is Alzheimer's Disease Treated?

10 states where people spend the most, least on health insurance

Americans spend the most on health insurance in West Virginia and the least in Maryland, according to a WalletHub analysis published July 9.

The personal finance company compared the average premiums for a silver health insurance plan with each state’s median household income using June 2026 data from the U.S. Census Bureau and KFF. It calculated insurance costs as a percentage of median monthly household income to rank all 50 states.

The 10 states where people spend the most on health insurance, based on insurance costs as a percentage of median monthly household income:

1. West Virginia — 20.86%
2. Vermont — 19.05%
3. Wyoming — 17.16%
4. Arkansas — 14.87%
5. Mississippi — 14.05%
6. Alaska — 13.18%
7. Louisiana — 12.58%
8. Tennessee — 12.19%
9. Alabama — 11.85%
10. Montana — 11.27%

The 10 states where people spend the least on health insurance:

1. Maryland — 4.66%
2. New Hampshire — 4.77%
3. Massachusetts — 5.49%
4. Virginia — 5.86%
5. Minnesota — 5.89%
6. New Jersey — 6.23%
7. California — 6.32%
8. Hawaii — 6.37%
9. Rhode Island — 6.67%
10. Colorado — 6.72%

10 states where people spend the most, least on health insurance

Why U.S. measles outbreaks have grown harder to extinguish - The Washington Post

Americans spend the most on health insurance in West Virginia and the least in Maryland, according to a WalletHub analysis published July 9.

The personal finance company compared the average premiums for a silver health insurance plan with each state’s median household income using June 2026 data from the U.S. Census Bureau and KFF. It calculated insurance costs as a percentage of median monthly household income to rank all 50 states.

The 10 states where people spend the most on health insurance, based on insurance costs as a percentage of median monthly household income:

1. West Virginia — 20.86%
2. Vermont — 19.05%
3. Wyoming — 17.16%
4. Arkansas — 14.87%
5. Mississippi — 14.05%
6. Alaska — 13.18%
7. Louisiana — 12.58%
8. Tennessee — 12.19%
9. Alabama — 11.85%
10. Montana — 11.27%

The 10 states where people spend the least on health insurance:

1. Maryland — 4.66%
2. New Hampshire — 4.77%
3. Massachusetts — 5.49%
4. Virginia — 5.86%
5. Minnesota — 5.89%
6. New Jersey — 6.23%
7. California — 6.32%
8. Hawaii — 6.37%
9. Rhode Island — 6.67%
10. Colorado — 6.72%

Why U.S. measles outbreaks have grown harder to extinguish - The Washington Post

How your baby communicates

 

Every baby speaks a language before they learn words. The amazing part is that it is the same language worldwide. Before they can talk, babies use five sounds to express what they need. This discovery, made by Priscilla Dunstan, shows that newborns share a universal way of communication through sound reflexes. Here are the five baby sounds and what they mean: 

 1. Neh means “I am hungry.” This sound comes from the natural sucking reflex when babies want to feed.

 2. Owh means “I am sleepy.” It is formed when the mouth opens in a yawn, signaling tiredness and the need for rest. 

 3. Heh means “I am uncomfortable.” Babies use this when something feels wrong, such as a wet diaper, tight clothes, or a temperature change. 

 4. Eairh means “I have gas.” It is a deeper sound that comes from discomfort in the tummy. You might see the baby squirm or pull their knees up. 

 5. Eh means “I need to burp.” A short, gentle sound that comes before small bursts of air leave the chest. When parents learn to recognize these sounds, they begin to understand what their baby is really saying. What once felt like crying now feels like conversation. Listening becomes the first form of love.