The U.S. once led the world in building railroads, highways, and the internet. Today, America lags in building the digital infrastructure foundation that underpins identity, payments, and data. Public Digital systems should be as essential to daily life as roads and bridges, yet America’s digital foundation is fractured and incomplete.

Digital public infrastructure (DPI) refers to a set of core and foundational digital systems like identity, payments, and data exchange that makes it easier for people, businesses, and governments to securely connect, transact, and access services. 

DPI consists of interoperable, open, and secure digital systems that enable identity verification, digital payments, and data exchange across sectors. Its foundational pillars are Digital Identity, Digital Payments, and Data Exchange, which together provide the building blocks for inclusive digital governance and service delivery. DPI acts as the digital backbone of an economy, allowing citizens, governments, and businesses to interact seamlessly and securely.

America’s current digital landscape is a patchwork of systems across states, agencies and private companies, and misses an interoperability layer. This means fragmented identity verification, uneven instant payment networks, and siloed data exchange rules and mechanisms. This fragmentation not only frustrates citizens but also costs taxpayers billions, leads to inefficiency and fraud. This memo makes the case that the United States needs sweeping legislation– a Digital Public Infrastructure Act— to ensure that the nation develops a coherent, secure, and interoperable foundation for digital governance. 

Challenges and Opportunities 

Around the world, governments are investing in digital public infrastructure to deliver trusted, inclusive, and efficient digital services. In contrast, the United States faces a fragmented ecosystem of systems and standards. This section examines each pillar of digital public infrastructure, digital identity, digital payments, and data exchange, highlighting leading international models and what institutional and policy challenges the U.S. must address to achieve a similarly integrated approach.

Fragmented and non-interoperable Digital Identities

Digital Identity. The U.S. has no universal digital identification system. Proving who you are online often relies on a jumble of methods like scanning driver’s licenses, giving your social security number, or one-off logins. Unlike many countries with national e-ID schemes, the U.S. relies on the REAL ID law which sets higher standards for physical driver’s licenses, but it provides no digital ID or consent mechanism for online use. Just under half of U.S. states have rolled out some form of mobile driver’s license (mDL) or digital ID, and each implementation is largely unique.

Federal agencies have tried to streamline login with services like Login.gov, yet many agencies still contract separate solutions (Experian, ID.me, LexisNexis, Okta, etc.), leading to duplication. The Government Accountability Office recently found that two dozen major agencies use a mix of at least five different identity-proofing providers. The result is an identity verification landscape that is inconsistent and costly, both for users and the government.

Fragmented Digital Payment Infrastructure

Digital Payments. The United States still lags in offering universal, real-time payments accessible to all. The payments landscape is highly fragmented, with multiple systems operated by both public and private entities, each governed by distinct rule sets. The Automated Clearing House (ACH) network is the batch-based system that processes routine bank-to-bank transfers such as salaries, bill payments, and account debits or credits. It is co-run by the Federal Reserve (FedACH) and The Clearing House (EPN) under Nacha rules and settles with delay. The Real-Time Payments (RTP) network is an instant 24/7 credit-push system that moves money within seconds through a prefunded joint account at the Federal Reserve Bank of New York. It was launched by The Clearing House in 2017 and is governed by its private bank owners.

In 2023, the Federal Reserve launched FedNow, the first publicly operated real-time payment rail in the United States, offering instant settlement through banks’ Federal Reserve master accounts. Card networks such as Visa, Mastercard, Amex, and Discover continue to operate proprietary systems, while peer-to-peer platforms like Zelle, Venmo, and CashApp run closed-loop schemes that often rely on RTP for back-end settlement. Because these systems differ in ownership, governance, settlement models, and liability frameworks, they remain largely non-interoperable. A payment sent through RTP cannot be received on FedNow, and card or wallet systems do not seamlessly connect to ACH or instant payment rails. 

FedNow operates as a real-time gross settlement (RTGS) infrastructure, enabling participating banks and credit unions to send and receive instant payments around the clock. Its design is infrastructure-centric: the Federal Reserve provides the back-end rail, while banks must opt in, build their own consumer interfaces, and set transaction fees and rules. The system does not define standardized public APIs, merchant QR systems, or interoperable consumer applications. These layers are left to the market. Its policy intent centers on efficiency and resilience in interbank payments rather than universal inclusion or open access.

Examples of Complete Public Payment Ecosystems

By contrast, India’s Unified Payments Interface (UPI) and Brazil’s Pix were designed as full digital public infrastructures that combine settlement, switching, and retail layers within a single public framework. Both are centrally governed, with UPI managed by the National Payments Corporation of India under Reserve Bank of India oversight and Pix managed by the Central Bank of Brazil. They enforce mandatory interoperability across all banks, wallets, and payment apps through open API standards. Their architecture integrates digital identity, authentication, and consent layers, allowing individuals and merchants to transact instantly at zero or near-zero cost.

While FedNow provides the plumbing for real-time settlement among banks, UPI and Pix function as complete public payment ecosystems built on open standards, public governance, and inclusion by design. Real-time payment systems in India (UPI), Brazil (Pix), and the United Kingdom (Faster Payments) now process far higher transaction volumes than their U.S. counterparts, reflecting how deeply these infrastructures have become embedded in daily economic activity.

Credit: fxcintel.com

This fragmented payment ecosystem became painfully apparent during COVID-19: some people waited weeks or months for stimulus and unemployment checks, while fraudsters exploited the delays. Only in 2025 did the Treasury Department finally announce it will stop issuing paper checks for most federal payments, to reduce delays, fraud, and theft.

Clearly, the U.S. needs a more cohesive approach to instant, secure payments, from Government-to-Person (G2P) benefits to Person-to-Government (P2G) tax payments and everyday Person-to-Person (P2P) transactions.

Data Exchange. Americans routinely encounter data silos and repetitive paperwork when interacting with different sectors and agencies. Each domain follows its own regulatory and technical standards. Health records are governed by the Health Insurance Portability and Accountability Act of 1996 (HIPAA) and the Trusted Exchange Framework and Common Agreement (TEFCA) established under the 21st Century Cures Act of 2016. Financial data are protected by the Gramm–Leach–Bliley Act of 1999 (GLBA) and will soon fall under the Consumer Financial Protection Bureau’s proposed Personal Financial Data Rights Rule (Section 1033, Dodd–Frank Act). Tax and education data are separately governed by the Internal Revenue Code and the Family Educational Rights and Privacy Act of 1974 (FERPA).

There is no unified, citizen-centric protocol for individuals to consentingly share their data across sectors. For example, verifying income for a mortgage, student loan, or benefits application might require three separate data pulls from the IRS or employer, each with its own process. In healthcare, TEFCA is creating a nationwide data-sharing framework but remains voluntary and limited to medical providers. In finance, Europe’s PSD2 Open Banking Directive (2018) forced banks to open consumer data via APIs, while the United States is only beginning similar steps through the CFPB’s data portability rulemaking. Overall, data-sharing rules remain sector-specific rather than citizen-centric, making it difficult to “connect the dots” across domains.

Data Protection. The United States follows a fragmented, sectoral approach to data protection rather than a single, unified framework. Health information is covered by HIPAA (1996), financial data by GLBA (1999), student records by FERPA (1974), and children’s online data by the Children’s Online Privacy Protection Act (COPPA, 1998).

States have layered on their own privacy laws, most notably the California Consumer Privacy Act (CCPA, 2018) and the California Privacy Rights Act (CPRA, 2020). At the federal level, the Federal Trade Commission (FTC) fills gaps using its authority to regulate “unfair or deceptive practices” under Section 5 of the FTC Act (15 U.S.C. §45). However, there remains no nationwide baseline for consent, portability, or deletion rights that applies uniformly across all sectors.

An illustration from 2021 by the New York Times shows the picture very well.

Credit: Dana Davis

Recent efforts in Congress, including the proposed American Data Privacy and Protection Act (ADPPA, 2022) and the American Privacy Rights Act (APRA, 2024), sought to create a comprehensive federal framework for data privacy and user rights. APRA built on ADPPA’s foundations by refining provisions related to state preemption, enforcement, and individual rights, proposing national standards for access, correction, deletion, and portability, and stronger obligations for large data holders and brokers. It also envisioned expanded enforcement powers for the FTC and state attorneys general, along with a limited private right of action.

Despite initial bipartisan attention, APRA has not secured sustained bipartisan support and remains stalled in Congress. The bill was jointly introduced in 2024 by the Republican Chair of the House Energy and Commerce Committee and the Democratic Chair of the Senate Commerce Committee, reflecting early cross-party interest. However, Democratic support weakened after language addressing civil-rights protections and algorithmic discrimination was removed, prompting several members to withdraw backing (Wired, 2024). As a result, the legislation has not advanced beyond committee referral, leaving the United States reliant on a patchwork of sector-specific and state-based privacy laws.

The outcome is a system where Americans face both fragmented data exchange and fragmented data protection, undermining trust in digital public services and complicating any transition toward a citizen-centric digital infrastructure.

The High Cost of Fragmentation

This patchwork system isn’t just inconvenient; it also bleeds billions of dollars. When agencies can’t reliably identify people, deliver payments quickly, or cross-check data, waste and fraud increase. Here are just a few examples:

Improper Payments. In FY2023 the federal government reported an estimated $236 billion in improper payments. That astronomical sum (almost a quarter-trillion dollars) stemmed from issues like payments to deceased or ineligible individuals and clerical errors. In fact, over 74% of the improper payments were overpayments The largest drivers included Medicare/Medicaid billing mistakes and identity-verification failures in pandemic relief programs. For example, the Pandemic Unemployment Assistance program alone saw an increase of $44 billion in erroneous payments, as identity thieves and imposter claims slipped through weak verification checks. While not all improper payments can be eliminated, a significant portion, GAO notes, can be eliminated. The biggest share of improper payments results from documentation and eligibility verification weaknesses, not intentional fraud. All errors could be reduced with better digital identity and data sharing systems.

Identity Theft and Fraud. American consumers are suffering a wave of identity-related fraud. In 2023, the Federal Trade Commission received over 1 million reports of identity theft such as credit cards opened in another person’s  name or fraudsters hijacking unemployment benefits. Identity theft now accounts for about 17% of all consumer fraud reports. The surge during the pandemic (when government aid became a target) showed how criminals exploit weak ID verification. State unemployment systems, for instance, paid out a significant sum to fraudsters who used stolen identities. Strengthening the digital ID infrastructure in U.S. could curb these losses by catching imposters before payments go out.

Administrative Overhead. Fragmentation forces each agency and company to reinvent the wheel, at great expense. Consider identity proofing: federal agencies spent over $240 million from 2020–2023 on contracts for login and ID verification solutions, much of it to third-party vendors, despite overlapping functionality. States and private institutions likewise pour resources into redundant systems for onboarding and verifying users. Processing paper documents and manual checks adds further costs and an indirect cost of time and frustration for citizens. A GAO report noted that agencies have widely varying systems and that a coordinated digital identity approach could improve security and save money. In short, the lack of shared public digital infrastructure means higher costs and slower service across the board.

Plan of Action 

What would a Digital Public Infrastructure Act do?

It’s clear that the status quo isn’t working. The U.S. needs a Digital Public Infrastructure (DPI) Act, a comprehensive federal law that would build the rails and rules for secure, efficient digital interactions nationwide. Just as past Congresses invested in highways and the internet itself, Congress today should invest in core digital systems to serve as public goods. A DPI Act could establish three pillars in particular:

Federated, Privacy-preserving Digital Identity

A secure digital ID that Americans can use (voluntarily) to prove who they are online, without creating a centralized “Big Brother” database. This would be a federated system, meaning you could choose from multiple trusted identity providers. For example, you could share your identification with your state DMV, the U.S. Postal Service, or a certified private entity, all adhering to common standards. The federated system must follow the latest NIST digital identity guidelines for security and privacy (e.g. NIST SP 800-63) to ensure high Identity Assurance Levels.

Crucially, it should be privacy-preserving by design: using techniques like encrypted credentials and pairwise pseudonymous identifiers so that each service you log into only sees a unique code, not your entire identity profile. A federated approach would leverage existing ID infrastructures (state IDs, passports, social security records) without replacing them. Instead, it links and elevates them to a digital plane.

Under a DPI Act, an American citizen might verify their identity once through a trusted provider and then use that digital credential to access any federal or state service, open a bank account, or consent to a background check, with one login. This approach can dramatically reduce fraud (no more 5 different logins for 5 agencies) while protecting civil liberties by avoiding any single centralized ID database. The Act could establish a national trust framework (operating under agreed standards and audits) so that a digital ID issued in, say, Colorado is trusted by a bank in New York or a federal portal, just as state driver’s licenses are mutually recognized today. Done right, a digital ID saves time and protects privacy: imagine applying for benefits or a loan online by simply confirming a verified ID attribute (e.g. “I am Alice, over 18 and a U.S. citizen”) rather than scanning and emailing your driver’s license to unknown clerks.

Universal, Real-time Payments (G2P, P2G, P2P)

The DPI Act should ensure that instant payment capability becomes as ubiquitous as email. This likely means leveraging FedNow, the Federal Reserve’s new instant payment rail, and expanding its use. For Government-to-Person (G2P) payments, Congress could mandate that federal disbursements (tax refunds, Social Security, veterans’ benefits, emergency relief, etc.) use a real-time option by default, with an ACH or card fallback only if a recipient opts out.

No citizen should wait days or weeks for funds that could be sent in seconds. The same goes for Person-to-Government (P2G) payments: taxes, fees, and fines should be payable instantly online, with immediate confirmation. This reduces float and uncertainty for both citizens and agencies. Finally, Person-to-Person (P2P): while the government doesn’t run private payment apps, a robust public instant payments infrastructure can connect banks of all sizes, enabling truly universal P2P transfers. This way, someone at Bank A can instantly pay someone at Credit Union B without needing both to join the same private app.

FedNow, as a public utility, is an important player, but the Act could incentivize or require banks to join so no institution is left behind. The result would be a seamless national payments system where money moves as fast as email, enabling things like on-demand wage payments, rapid disaster aid, and easier commerce.

Cross-sector, Consent-based Data Exchange

The third pillar is perhaps the most forward-looking: creating standard protocols for data sharing that put individuals in control. Imagine a secure digital pipeline that lets you, the citizen, pull or push your personal data from one place to another with a click – for instance, authorizing the IRS to share your income info directly with a state college financial aid office, or allowing your bank to verify your identity by querying a DMV record (with your consent) instead of asking you to upload photos or scans.

A DPI Act can establish an open-data exchange framework inspired by efforts like open banking and TEFCA, but broader. This framework would include technical standards (APIs, encryption, logging of data requests) and legal rules (what consents are needed, liability for misuse, etc.) to enable “tell us once” convenience for the public. 

Importantly, it must be consent-based: your data doesn’t move unless you approve and authorize it.It can let you carry digital attestations i.e. driver’s license, vaccination, veteran status, etc. on an e-wallet and share just the necessary bits with whoever needs to know. Some building blocks already exist: the federal Office of the National Coordinator for Health IT (ONC) is working on health data interoperability through TEFCA (so hospitals can query each other’s records), and the Consumer Financial Protection Bureau has begun rulemaking to give bank customers the right to share their financial data with third-party apps.

A DPI Act could unify these efforts under one umbrella, extend them to other domains, and fill in the gaps (for instance, enabling portable eligibility, if you qualify for one program, easily prove it for another). It could establish a governance entity or standards board to oversee the trust frameworks needed. Crucially, this must be accompanied by strong privacy and security measures like audit trails, encryption, and an emphasis that individuals can see and control who accesses their data. An example of this is how the EU wallet provides a dashboard for users to review and revoke data sharing.

The Digital Public Infrastructure Act would not necessarily build each piece from scratch but set national standards and provide funding to knit them together. It could, for example, direct NIST and a multi-agency task force to implement a federated ID by a certain date (building on Login.gov’s lessons), require the Treasury and Federal Reserve to ensure every American has a route to instant payments across platforms (leveraging FedNow), and authorize pilot programs for cross-sector data exchange in key areas like social services.

Precedent for such an approach already exists in bipartisan efforts:

Navigating Roadblocks: Federalism, Privacy, and Tech Contractors

Enacting a U.S. Digital Public Infrastructure Act will face several real challenges. It’s important to acknowledge these roadblocks and consider strategies to overcome them:

Federalism and Decentralized Authority

Unlike many countries where a central government can launch a national ID or payments platform by decree, the U.S. must coordinate federal, state, and local authorities. Identity in the U.S. is traditionally a state domain (driver’s licenses, birth certificates), while federal agencies also issue identifiers (Social Security numbers, passports). A DPI solution must respect these layers. States may fear a federal takeover of their DMV role, and agencies might guard their IT turf. Solution: design the system as a federation of trust. The Act could explicitly empower states by providing grants for states to upgrade to digital driver’s licenses (the Improving Digital Identity Act proposed in 2022 did exactly this, offering grants for state DMV mobile IDs). It could also create a governance council with state CIOs and federal officials to jointly set standards.

Civil Liberties and Privacy Concerns

Any mention of a “digital ID” in America raises eyebrows about Big Brother. Civil liberties advocates will rightly question how to prevent government overreach or mass surveillance. The Act should incorporate privacy by design provisions e.g., require minimal data collection, mandate independent audits for security, and give users legal rights over their data. One promising approach is using decentralized identity technologies, where your personal data (like credentials) stay mostly on your device under your control, and only verification proofs are shared. Also, the law can explicitly forbid certain uses, for instance, prohibit law enforcement from fishing through the digital ID system without a warrant, or forbid using the digital ID for profiling citizens. Including groups like the ACLU and EFF in the drafting process could help address concerns early. It’s worth noting that privacy and security can actually be enhanced by a good digital ID: today, Americans hand over copious personal details to random companies for ID checks (e.g. scan of your driver’s license to rent an apartment, which might sit in a landlord’s email forever). A federated ID could reduce exposure by only transmitting a yes/no verification or a single attribute, rather than a photocopy of your entire ID. Conveying that narrative, that this can protect people from identity theft and data breaches, will be key to overcoming knee-jerk opposition. Still, robust safeguards and perhaps a pilot phase to prove the concept will be needed to convince skeptics that a U.S. digital identity won’t become a surveillance tool.

Incumbent Resistance (Big tech and Contractors)

There are vested interests in the current disjointed system. Large federal IT contractors and identity verification vendors profit from selling agencies one-off solutions; big tech companies dominate payments and data silos in the status quo. A unified public infrastructure could be seen as competition or a threat to some business models. For example, if a free government-backed digital ID becomes widely accepted, companies like credit bureaus (which sell ID verification services) or ID.me might lose market share. If open-data sharing is mandated, banks that monetize data might push back. The solution is to engage industry so they can find new opportunities within the ecosystem. Many banks, for instance, actually support digital ID because it would cut fraud costs for them. The banking industry has been calling for better ID verification to fight account takeover and synthetic identities. In fact, a coalition of financial institutions endorsed the earlier Improving Digital Identity legislation.

Fintechs will favor Digital Public Infrastructure (DPI) because it transforms customer acquisition from a slow, expensive manual process into an instant, low-cost digital utility. By plugging into standardized government layers for identity (e-KYC) and data sharing (Account Aggregators), fintechs can instantly verify and underwrite users who lack traditional credit histories. This allows them to scale rapidly and profitably serve millions of previously “unbanked” customers by making lending decisions based on real-time data rather than rigid credit scores.. The Act can create a public-private task force (as earlier bills proposed) to hash out implementation. For government contractors, the reality is that building DPI will still require significant IT work, just more standardized. Contractors who adapt can win contracts to build the new infrastructure.

Political Will and Public Perception

DPI can be a bipartisan win if framed correctly.

For conservatives and fiscal hawks: emphasize the anti-fraud, waste-cutting angle. Stopping improper payments (recall that $236B figure!) and preventing identity theft aligns with the goal of efficient government. The Act  essentially plugs leaky buckets, something everyone can get behind.

For liberals and tech-progressives: emphasize equity and empowerment. How digital infrastructure can help the unbanked access financial services, ensure eligible people aren’t left out of benefits, and give individuals control of their own data (a pro-consumer, anti-monopoly stance). Indeed, digital public goods are often framed as a way to ensure big tech doesn’t exclusively control our digital lives.

The key will be avoiding hot button mis-framings: this is not a surveillance program, not a national social credit system, etc. It’s an upgrade to basic government digital infrastructure. One strategy is to start with pilot programs and voluntary adoption to build trust. For example, the Act could fund a pilot in a few states to link a state’s digital driver’s license with federal Login.gov accounts, showing a working federated ID in action. Or pilot using FedNow for a chunk of tax refunds in one region. Early successes will create momentum and help refine the approach. Champions in the Congress will need to communicate that this is infrastructure in the truest sense: just as U.S. needed electrification and interstate highways, it now needs the digital equivalent to keep America competitive and secure.

Conclusion

A Digital Public Infrastructure Act represents more than a technical upgrade; it is an investment in America’s institutional capacity. The challenges the U.S. faces today like identity theft, improper payments, slow benefit delivery, and fragmented data governance are the predictable consequences of an outdated public digital foundation that has never been treated as national infrastructure. Just as the interstate highway system knit together the physical economy, and just as the early internet created the backbone for the digital economy, the United States now needs a unified, secure, and interoperable set of digital rails to support the next era of public service delivery and economic growth.

Unlike centralized systems elsewhere in the world, the American version of DPI would be federated, privacy-preserving, and deeply respectful of federalism. States would remain primary issuers of identity credentials. Private innovators would continue to build consumer-facing services. Federal agencies would govern standards rather than run monolithic platforms. This hybrid model plays to America’s institutional strengths such as distributed authority, competitive innovation, and strong civil liberties protections.

Congress must enact a Digital Public Infrastructure Act, a recognition that the government’s most fundamental responsibility in the digital era is to provide a solid, trustworthy foundation upon which people, businesses, and communities can build. America has done this before when it built the railroads, electrified the nation, and invested in the early internet. The next great public works project must be digital.

American investment in science is incredibly productive. Yet, it is losing trust with the public, being seen as misaligned with American priorities and very expensive. To increase the real and perceived benefit of research funding, funding agencies should develop challenge goals for their extramural research programs focused on the impact portion of their mission. For example, the NIH could adopt one goal per institute or center “to enhance health, lengthen life, and reduce illness and disability”; NSF could adopt one goal per directorate “to advance the national health, prosperity and welfare; [or] to secure the national defense”. Asking research agencies to consider person-level or economic impacts in advance helps the American people see the value of federal research funding, and encourages funders to approach the problem holistically, from basic to applied research. For almost every problem there are different scientific questions that will yield benefit over multiple time scales and insight from multiple disciplines. 

This plan has three elements: 

1. Focus some agency funding on measurable mission impacts 
2. Fund multiple timescales as part of a single plan
3. Institutionalize the impact funding process across science funders

For example, if NIH wanted to reduce the burden of Major Depression, it could invest in a shorter time frame to learn how to better deliver evidence-based care to everyone who needs it. At the same time, it can invest in midrange work to develop and test new models and medications, and in the decades-long work required to understand how the exosome influences mood disorders. A simple way to implement this approach would be to build on the processes developed by the Government Performance Results Act (GPRA), which already requires goal setting and reporting, though proposals could be worked into any strategic planning process through a variety of administrative mechanisms.

Challenge and Opportunity

In 1945, Vannevar Bush called science the ‘endless frontier’, and argued funding scientific research is fundamental to the obligations of American government. He wrote “without scientific progress no amount of achievement in other directions can insure our health, prosperity, and security as a nation in the modern world”. The legacy of this report is that health, prosperity, and security feature prominently in the missions of most federal research agencies (see Table 1). However, in this century we have begun to drift from his focus on the impacts of science. We have the strange situation where our enterprise is both incredibly productive, and losing trust with the public, viewed as out of touch or misaligned with American priorities. This memo  proposes a simple solution to address this issue for federal funding agencies like NIH and NSF that largely focus on extramural investigator-initiated research. These are research programs where the funding agency signals interest in specific topics and teams of scientists submit their research plans addressing those topics. The agency then funds a subset of those plans with input from external scientific reviewers.

This funding approach is incredibly productive. For example, NIH funds most of the pipeline for the emerging bioeconomy, which accounts for 5.1% of our GDP. From 2010 to 2016, every one of the 210 new entities approved by the FDA had some NIH funding. And yet, there appears to be a disconnect between our funding strategy and the public interest focus of the Endless Frontier operationalized through our federal science agency missions for investigator initiated research. 

A fundamental driver of this disconnect might be a slight misalignment of the incentives of academic scientists, who are rewarded for novelty and scientific impact, with the broader public interest. Our federal agencies are highly attuned to scientific leaders, and place equal or even greater weight on innovation (novelty plus scientific impact) than real world impact. For example, NSF review criteria place equal weight on intellectual merit (‘advance knowledge’) and broader impacts (‘benefit society and contribute to the achievement of specific, desired societal outcomes’). NIH’s impact score of new applications is an ‘assessment of the likelihood for the project to exert a sustained, powerful influence on the research field(s) involved’ [emphasis mine], which is only part of the agency’s mission. The practical implications of this sustained focus away from the impact portion of agencies missions become apparent in figure 1, showing tremendous spending in health research unrelated to a key public interest measure like lifespan, especially when compared to other nations’ health research spending. 

Perhaps the realization that the federal research investment is not strongly linked to their mission impact is one reason why American science has been slowly losing public trust over time. Among the people of 68 nations ranking the integrity of scientists, Americans ranked scientists 7^th highest, whereas we ranked scientists 16^th highest in our estimation of them acting in the public interest. And this is despite the fact that the American investment in science is many times higher than the 15 nations who rated scientists more highly on public interest. A more accurate description of our 21^st century federal science enterprise might be the ‘timeless frontier’, where our science agencies pursue cycles of funding year in and year out, with their functional goal being scientific changes and their primary measure of success being projects funded. Advancing the economy, health, national defense, etc., are almost incidental benefits to our process measures. 

We can do better. In 2024, the National Academy of Medicine called out the lack of high level coordination in research funding. In 2025, the administration has been making drastic cuts and dramatic changes to goals and processes of federal research funding, and the ultimate outcome of these changes is unclear. In the face of this change, Drs. Victor Dzau and Keith Yamamoto, staunch champions of our federal science programs, are calling for “a coherent strategy […] to sustain and coordinate the unrivaled strengths of government-funded research and ensure that its benefits reach all Americans”.

We can build on the incredible success of the federal science enterprise – inarguably the most productive science enterprise in all history. The primary source of American scientific strength is scale. American funding agencies are usually the largest funders in their space. I will highlight some challenges of the current approach and suggest improvements to yield even more impactful approaches more closely aligned with the public interest.

The primary federal funding strategy is broad diversification, where our agencies fund every high scoring application in a topic space (see FAQs). Further, federal science agencies pay little attention to when they expect to see a fundamental impact arising from their research portfolio. For example, a centrally directed program like the Human Genome Project can lead to breakthrough treatments decades later, but in the meantime, other research that generates improvements on faster timescales could have been coordinated, such as developing conventional drug treatments, or research to optimize quality and delivery of existing treatment. 

And yet, the breadth and complexity of broad diversification makes it easy to cherry pick successes. This is a strategic issue, and is bigger than the project selection issues highlighted in the earlier discussion about review criteria. When research funding agencies make their pitch for federal dollars they highlight a handful of successes over tens of thousands of projects funded over many years. They ignore failures, the time when investments were made, and time to benefit. With the goals and metrics we have in place, it is simply too hard to summarize progress in any other way. 

Overly diversified science funding supports both good Congressional testimony and bad strategy. If your problem happens to fall into a unicorn space of success, there is a lot to celebrate. But most problems do not, and we experience inconsistent returns. We need to define the success of research funding more precisely, in advance, and in ways that more obviously align with the public interest. 

Plan of Action 

If we tweak our funding strategy to focus on societal impacts, we can move to a more impactful science enterprise, and help regain public support for science funding. We can focus federal research funding on effective answers to difficult problems demanding both urgency and short term improvements, and fundamental discoveries that may take decades to realize. My solution and implementation actions for agencies, and potentially Congress, are described below. 

Recommendation 1. Focus some agency funding on measurable mission impacts. 

We should empower our science agencies to step away from broad diversification as the predominant funding strategy, and pursue measurable mission impacts with specific time horizons. It can be a challenge for funders to step away from process measures (e.g. projects or consortia funded) and focus on actual changes in mission impact.

Ideally, these specific impacts would be broken into measurable goals that would be selected through a participatory process that includes scientific experts, people with lived experience of the issue, and potential partner agencies. I recommend each agency division (e.g. an NSF Directorate) allocate a percentage of their budget to these mission impact strategies. Further, to avoid strategic errors that can arise from overwhelming power of federal funding to shape the direction of scientific fields, these high level funding plans should be as impact focused as possible, and avoid steering funding to one scientific theory or discipline over another. 

Recommendation 2. Fund multiple timescales as part of a single plan. 

Research funders need to balance their investment portfolios not only across problem areas, but over time. Complex challenges will often require funding different aspects of the solution on different timelines in parallel as part of a larger plan. Balancing time as well as spending allows for a more robust portfolio of funding that draws from a broader array of scientific disciplines and institutions. 

Note, this approach means starting lines of research that may not lead to ultimate impact for decades. This approach might seem strange given our relatively short budget cycles, but is very common in science, where projects like the Human Genome initiative, the Brain Initiative, or the National Nanotechnology Initiative, have all exceeded a single budget cycle and will take years to realize their full impact. These kinds of efforts require milestones to ensure they stay on track over time. 

Recommendation 3. Institutionalize the impact funding process across science funders.

Our research enterprise has become oriented around investigator-initiated, project-based awards. Alternative funding strategies, such as the DARPA model, are viewed as anomalies that must require completely different governance and procedures. These differences in goals are unnecessary. A consistent focus on impacts and strategy in funding across agencies will help the scientific community become more aware of the time to benefit of research, help underscore the value of research investment to the American public, and help research agencies collaborate among themselves and with their partner agencies (e.g. NIH collaborates more closely with CMS, FDA, etc.). 

In short, institutionalizing this process can lead to greater accountability and recognition for our science enterprise. This structure allows our funders to report to the public progress on specific goals on predetermined and preannounced timelines, rather than having to comb through tens of thousands of independent funding decisions and competing strategies to find case studies to highlight. In this way, expected and unexpected scientific results, and even operational challenges, can be discussed within an impact framework that clearly ties to the agency mission and public interest.

Example of Planning using  an Impact Focus

Here is an example of a mission impact goal Reducing the Burden of Major Depressive Disorder that could be put forth by the National Institute of Mental Health (NIMH), and the process to develop it.

Commence Inclusive Planning: NIMH brings together experts from academia, clinical care, industry, people impacted by depression, and FDA and CMS to develop measures, timelines and funding strategies. 

Develop Specific Impact Measures: These should  reflect the agency’s impact portion of their mission. For example, NIH’s mission impact of “enhance health, lengthen life, and reduce illness and disability” requires measuring impact on human beings. Example measurement targets could include:

• Reduced incidence of Major Depressive Disorder 
• Increased productivity (e.g. days worked) of people living with Major Depressive Disorder
• Reduced suicide rates

Fund Multiple Time Scales: Designate time scales in parallel as part of a comprehensive strategy. These different plans would involve different disciplines, funding mechanisms, and private sector and government partners. Examples of plans working at different timescales to support the same goal and measures could include:

• 10 year plan: Increase utilization of evidence based care
• 15 year plan: Develop and implement new treatments
• 30 year plan: Determine how the exposome causes and prevents depression, and how can be changed
• It is likely that NIMH has already obligated funds to projects that support one of these plans, though they may need additional work to ensure that those projects can directly tie to the specific plan measures. 

Implementation Strategies for Impact Goals

Each federal funding agency could allocate a percentage of their budget to these and other  impact goals. The exact amount would depend on the current funding approach of each agency. As this proposal calls for more direct focus on agency mission, and not a change in mission, it is likely that a significant percentage of the agency’s current budget already supports an impact goal on one or more of its time scales. 

For an agency heavily weighted towards project based funding of small investigator teams, like NIH, I would recommend starting with a goal of 20% of their budgets set towards impact spending and consider increases over time. Other agencies with different funding models may want to start in a different place. Further, I would recommend different goals and targeted funds for each major administrative unit, such as an institute or directorate. 

All federal funders already engage in some form of strategic and budget planning, and most also have formal structures for engaging stakeholders into those planning decisions. Therefore, each agency already has sufficient authorities and structures to implement this proposal. However, it is likely that these impact goals will require collaboration across agencies, and that could be difficult for agencies to efficiently conduct by themselves. 

Additional support to make this change could come from Congressional Report language as part of the budget process, through interagency leadership from the White House Office of Science and Technology, or through the Office of Management and Budget. For example, the Government Performance Results Act (GPRA) already requires agency goal setting, reporting and supports cross agency priority goals. That planning process could easily be adapted to this more specific impact focus for research funding agencies, and reporting on those goals could be incorporated into routine reporting of agency activities. 

Conclusion 

We are living through a massive disruption in federal research funding, and as of the fall of 2025, it is not clear what future federal research funding will look like. We have an opportunity to focus the incredibly productive federal research enterprise around the central reasons why Americans invest in it. We can meet Bush’s challenge of the Endless Frontier simply by clearly defining the benefits the American people want to see, and explicitly setting plans, timing and money to make that happen. 

We can call our shots and focus our science funding around impacts, not spending. And we can set our goals with enough emotional resonance and depth to capture both the interests of the average American, and the needs of scientists from different disciplines and types of institutions. We already have the legal authorities in place to adopt these techniques, we just need the will.

The current education research-to-policy pipeline is too slow to keep pace with the urgent needs of districts and states. Researchers face steep barriers to accessing high-quality, multimodal data, while existing R&D infrastructures remain siloed and under-resourced. Without scalable, trusted, systems that enable timely and secure data use, the U.S. risks falling behind in generating actionable and evidence-based insights to guide policy and practice. In this memo, we discuss how privacy-preserving research models can be used to strengthen education R&D capacity. 

Challenge and Opportunity

Learning is a lifelong and multidimensional process, yet data about learning has historically been difficult to obtain. The shift to digital learning platforms (DLPs), accelerated by COVID-19, has created a wealth of data, but accessing it remains complex and slow – especially for researchers with fewer institutional resources.

Additionally, complex privacy laws, such as the Children’s Online Privacy Protection Act (COPPA) and Family Educational Rights and Privacy Act (FERPA), alongside state-specific regulations and institutional risk aversion, create substantial barriers. These laws were not designed to accommodate privacy scenarios within the current environment of pervasive data collection and rapidly advancing AI. 

As such, trusted mechanisms for safe data access that remove barriers to critical R&D, bolster global competitiveness, and leverage innovation to cultivate a skilled STEM workforce, are more important than ever.  Without trusted mechanisms to ensure privacy while enabling secure data access, essential R&D stalls, educational innovation stalls, and U.S. global competitiveness suffers.

Flipping the traditional research model

The landscape of educational research and development (R&D) is rapidly evolving as digital learning platforms (DLPs) capture increasingly rich streams of data about how students learn. These multimodal data streams provide unprecedented opportunities to accelerate insights into how learning happens, for whom, and in what contexts – as well as how these processes, in turn, affect learning outcomes, engagement, and persistence. Yet, despite this potential, access to platform-generated learning data remains highly constrained – particularly for early-career researchers with minimal institutional resources and organizations outside elite academic settings. 

Current challenges to accessing DLP data include privacy risks (e.g., data leaks), opaque legal environments, institutional risk aversion, and the lack of trusted third-party intermediaries to balance privacy with data utility. As a result, promising research is delayed and the research-to-policy pipeline is exacerbated – leaving decision-makers without timely evidence to address urgent needs such as learning recovery, responsible AI integration, or workforce readiness.

Privacy-preserving models offer transformative opportunities to address these barriers. Across sectors, the field is converging on trusted research environments that include secure enclaves that keep data in situ and move analysis to the data. SafeInsights, the U.S. Census’ Federal Statistical Research Data Center (FSRDC), and North Carolina Education Research Data Center (NCERDC) are examples of such systems complemented by privacy-preserving methods.

Privacy-preserving research models, such as SafeInsights, flip the traditional research model: instead of giving data to researchers, it brings researchers’ questions and analyses, encoded as software, to the data. At no point in the research process does the researcher have direct access to raw data, thereby minimizing concerns for data leaks. 

Researchers instead use sample or synthetic data to craft their analyses. Once the researchers’ analysis code is submitted to the owner of the data, it is reviewed by experts for approval. This model minimizes risk, reduces delays in the research-to-policy pipeline, and unlocks data that would otherwise remain inaccessible.

Think of it as a secure research zone: a trusted third-party intermediary where researchers can run analyses using specific tools and applications, but cannot access data directly, ensuring strict security.

Rather than extracting and sharing sensitive data with researchers, privacy-preserving research models bring researchers’ analytic tools to secure data enclaves – preserving privacy while enabling rigorous, scalable, inquiry of DLP data. Through secure enclaves, transparent governance, and standardized compliance frameworks, a durable large-scale infrastructure for research can be created.

Benefits of privacy-preserving research models

• Accelerate time to insight for policy and decision-makers who need rapid, evidence-based guidance. Standardized governance reduces delays arising from fragmented compliance and legal processes. For federal, state, and local level policy and decision-makers, this means actionable insights can be delivered in months rather than years, potentially informing legislative decisions and programs with greater speed.
• Safely join data across platforms, enabling richer analyses of student learning. Shared infrastructure maximizes critical research infrastructure return on investments and spreads costs across funders. Secure, trusted, interoperable research environments protect privacy while enabling cumulative evidence. This aligns with federal agency priorities to modernize research infrastructure and ensure taxpayer investments translate into impact.
• Democratize access and participation in complex research by lowering barriers for early-career researchers with minimal institutional resources and organizations outside elite academic settings. Lowering barriers to entry broadens the reach of federal R&D investments and supports state leaders and research organizations seeking to participate in research.

By securing cross-sector investment for embedding scalable privacy-preserving models into R&D ecosystems and infrastructures, we can expand access to high-value data while supporting long-term research scalability, security, and trust.

Such models can fill a critical gap in the R&D ecosystem by establishing a secure and sustainable research infrastructure that extends well beyond its initial NSF funding and is ideally suited to broker access between DLP developers, school districts, and researchers.

Plan of Action

Promote R&D Infrastructure Development and Sustainability

Privacy-preserving research models have the potential to offer researchers safer, faster, reliable, high-value, de-identified data analyses – while simultaneously saving DLPs and school districts time and resources on compliance reviews and privacy audits. It also creates opportunities for funders to support a sustainable research infrastructure that multiplies the impact of each dollar invested.

To move from promise to practice, interested stakeholders, including research institutions, school districts, and funders, should consider the following actions:

Recommendation 1. Lay the Foundation for Sustainable Large-Scale R&D Infrastructure

• Conduct policy landscape scans, including review of state student privacy laws, to identify commonalities, constraints, and pathways for district participation.
• Interview stakeholders, including district data leads, state education agencies, and platform providers, to understand pain points and demand for trusted intermediaries.
• Review existing research infrastructures and operational frameworks, including research data hub governance, fee structures, data-sharing agreements, IRB support services, and services, adapting effective practices to the privacy-preserving context.

Recommendation 2. Embed Infrastructure Costs into Research Contracts and Budgets

• Require researchers to include service fees for privacy-preserving infrastructure directly in grant applications, with templates to simplify proposal preparation.
• Embed privacy-preserving infrastructure costs in contracting and budgeting to support scalability, drive down the marginal cost of data access across the field, and make rigorous educational research more accessible and sustainable beyond single grants.

Recommendation 3. Catalyze Scaling through Foundation and Philanthropic Support

• Engage major education funders (e.g., Gates Foundation, Carnegie, Hewlett, Chan Zuckerberg Initiative) to support large scale R&D infrastructure efforts by underwriting core operations and reducing costs for under-resourced research organizations and districts.
• Draw on models such as the North Carolina Education Research Data Center (NCERDC), ICPSR (University of Michigan), and Harvard Dataverse, to sustain operations via grant-embedded fees, service contracts, and institutional memberships.

Recommendation 4. Develop Large Scale R&D Infrastructure across Sectors

• Extend privacy-preserving models across sectors, such as education, health, workforce, housing, and finance, to capture increasingly rich streams of data about how people live, learn, work, and access services.
• Enable secure, interoperable, cross-sector research on questions such as how early education experiences impacts long-term workforce outcomes or  how neighborhood-level educational access connects to public health disparities.
• Align with federal agency efforts, such as the Federal Data Strategy, to support the linking of data ecosystems across sectors.

Conclusion

Privacy-preserving research models offer standardized, secure, and privacy-conscious ways to analyze data – helping researchers at the local, state, and federal levels understand long-term educational trends, policy impacts, and demographic disparities with unprecedented clarity.

By accelerating time-to-insight, investing in critical R&D infrastructure, and expanding participation in complex research, privacy-preserving research models offer possibilities for delivering on urgent policy priorities – building towards a modern, responsive, trustworthy education R&D ecosystem.

Americans trade stocks instantly, but spend 13 hours on tax forms. They send cash by text, but wait weeks for IRS responses. The nation’s revenue collector ranks dead last in citizen satisfaction. The problem isn’t just paperwork — it’s how the government builds.

The fix: build for users, not compliance. Ship daily, not yearly. Cultivate talent, don’t rent it. Apple doesn’t outsource the creation of its products; the IRS shouldn’t outsource taxpayer experience. Why?

The goal: make taxes as easy as mobile banking.

The IRS, backed by a Congress and an administration that truly wants real improvements and efficiencies, must invest in building its tax products in house. Start with establishing a Chief Digital Officer (CDO) at the IRS directly reporting to the Commissioner. This CDO must have the authority to oversee digital and business transformation across the organization. This requires hiring hundreds of senior engineers, product managers, and designers—all deeply embedded with IRS accountants, lawyers, and customer service agents to rebuild taxpayer services. This represents true government efficiency: redirecting contractor spending to fund internal teams that build what American taxpayers should own rather than rent. 

This is about more than broken technology. This is a roadmap for building modern, user-centric government organizations. The IRS touches every American, making it the perfect lab for proving the government can work.

Transform the IRS first, then apply these principles across every agency where citizens expect digital experiences that actually work.

Challenge & Opportunity

It’s April 15th. For the first time, you’re not fretting.

You finished filing your taxes on a free app. It took 15 minutes. Your income? Already there. Your credits? Pre-calculated and ready to claim. Your refund? Hitting your bank account tomorrow.

For millions around the world, swift, painless tax filing isn’t a dream. It’s the norm. It should be for Americans, too.

But in the U.S., the IRS experience is still slow, opaque, process-heavy, and frustrating. Tax filing is one of the few universal interactions Americans have with their government—and it’s not one that earns much trust.

It doesn’t have to be this way. We were on the path to delivering that with IRS Direct File and needed to recommit.  To deliver wildly easier taxes for Americans, we can, and must, build an IRS that meets high modern expectations: fast, transparent, digital-first, and relentlessly taxpayer-focused.

The Diagnosis

Each year, the IRS collects more than 96% of the revenue that funds the federal government—$5.1 trillion supporting everything from Social Security, defense, infrastructure, veterans’ services, and investing in America’s future. 

The quote from Justice Oliver Wendell Holmes, carved into the limestone face of the IRS headquarters in D.C., captures the spirit well:

“Taxes are what we pay for civilized society.”

It is not only essential to the functioning of government—it is also a major way most Americans interact with it. And that experience? Frustrating, costly, and confusing. According to a recent Pew survey, Americans rate the IRS less favorably than any other federal agency. The average taxpayer spends 13 hours and $270 out of pocket just to file their return.

The core problem: The IRS needs to be user-focused.

Despite the stakes, the IRS operates far behind what Americans expect. We live in a world where people can tap to pay, split bills by text, or trade stocks in slick apps.  But that world does not include the IRS. 

A staggering 63% of the 10.4 billion hours Americans spend dealing with the federal government are consumed by IRS paperwork. But much of the source of that pain isn’t the IRS, but Congress with the crushing complexity of decades long tax code changes, sedimented on top of each other.  This year was no different. The “One Big Beautiful Bill” runs 331 pages, with large swaths devoted to new, intricate tax changes.  

Dealing with the IRS still often involves paper forms, long phone waits, chasing down documents, and confusing processes. 

If you’ve dealt with the IRS for anything beyond filing, it feels impossible to get a task finished. Will someone pick up the phone? Can I get an answer to my questions and resolve my situation? Would I expect the same answer if I talked to someone else? Last year the IRS answered just 49% of the 100 million calls it received, including automated answering. 

This underperformance is beyond outdated technology—it’s structural and institutional. The IRS’s core systems are brittle and fragmented. Ancient procurement rules and funding constraints have made sustained modernization nearly impossible. Siloed organizations sit within siloes. In place of long-term investment, the agency leans heavily on short-term contractor fixes, band-aids applied to legacy wounds.  

This complexity has stymied scaled change. 

The root cause: The IRS has never treated world-class technology and product development as mission-critical capabilities core to its identity, to be hired, owned, and continually improved by internal teams focused on user outcomes.

A modern service agency builds end-to-end experiences for users—from pre-populating data through to filing and refunds. Empowered teams building these features have a holistic viewpoint and control over their service to ensure taxpayers are able to repeatedly and reliably complete their task.

Today’s reality is different: federal agencies like the IRS treat technical and product expertise as afterthoughts—all nice-to-haves that serve bureaucratic processes rather than core capabilities essential to their mission. Strategy and execution get outsourced by default. This creates a growing divide between “business” and “IT” teams, each lacking a deep understanding of the other’s work, despite both being critical to delivering services that actually function for taxpayers.

This outsourcing has hollowed out the agency’s internal technical capacity. Rather than building technical competency in-house, and paying that talent a salary approaching private companies, the IRS grows more dependent on vendors. It no longer knows what it needs technically, what questions to ask or which paths to pursue. Instead they must trust the vendors–companies financially incentivised towards ballooning scopes, lock-in, and complexity. 

The result: a siloed experience that mirrors a siloed organization, one that is risk-averse, paper-heavy IRS, too slow to meet modern expectations.

The agency approaches service delivery as a compliance and bureaucratic process to digitize, rather than a product to design. “Never ship your org-chart” is a common refrain you’ll hear at tech companies, to explain how products tend to take on the communication style of their builders. Yet IRS product faultlines visibly follow its org structure and thus fail to deliver a holistic experience. 

There were bright spots. Direct File showed what’s possible when empowered teams build for users. A dead simple idea: let Americans file taxes directly on the IRS site was a reality. It worked. It was well regarded. In surveys, users beamed about Direct File: 9 out of 10 gave it an “excellent” or “above average” rating, 74% said they preferred it over what they used before, and 86% said it increased their trust in the IRS. 

The government actually delivered for its citizens, and they felt it.

But it didn’t last. The project was abruptly dismantled due to political ideology, not taxpayer experience or feedback. 

Many of the people with the technical skills and vision to modernize the IRS have left, often without a choice. The agency will likely slide further backward—into deeper dependence on systems built by the lowest bidder or those currying political favor, with poorer service and diminished public trust in return.

We’ve seen this up close. 

Both of us worked at The White House’s technology arm; the U.S. Digital Service. One of us helped lead Direct File into existence and built the Consumer Financial Protection Bureau’s digital team. The other previously led Google’s first large language model products and prototyped AI tools at the IRS to streamline internal knowledge work. 

In our work at the IRS, we witnessed how far the agency must go. Inside the IRS Commissioner’s office, with leaders across the agency, we built a collaborative digital strategic plan. This memo details those proposals since left by the wayside after seven different IRS commissioners rotated in the seat, just this year.

The IRS needs more than modernization. It will need a systemic rebuild from:

• compliance, to user-centered design and product thinking
• vendor dependence to empowered internal product teams
• once-a-year panic to real-time, year-round services
• fragile mainframes to composable platforms and APIs
• waterfall contracting to iterative, continuous delivery

We’re sharing these recommendations for a future Day One—when there’s a refocus on rebuilding the government. When that day comes, the blueprint will be here: drawn from inside experience, built on hard lessons, and focused on what it will take to deliver a digital IRS that truly works for the American people.

What we need is the mandate to build a tax system that makes Americans think: “That was it? That was easy.”

Plan of Action

The IRS must rebuild taxpayer services around citizen needs rather than compliance and bureaucratic processes. This requires in-housing the talent to strategically build it. We propose establishing a Chief Digital Officer directly reporting to the Commissioner, with the authority to oversee digital and business transformation across the organization, hire hundreds of senior engineers, product managers, and designers. The goal, a team empowered to deliver a tax-filling product experience that meets modern expectations.

The Products

Build for Users, Not Internal Compliance

We’ve become accustomed to a user-focused fit-and-finish in the app era. Let’s deliver that same level for taxpayers.

It all starts around building a digital platform that empowers taxpayers, businesses, and preparers with the information, tools and services to handle taxes accurately and confidently. A fully-featured online account becomes the one-stop, self-service hub for all tasks. Taxpayers access their complete tax profile, updated in real-time, with current data across income sources, financial institutions, and full tax history. The system proactively recommends tax breaks, credits, and withholding adjustments they’re eligible for.

Critically, this can’t be built in a vacuum. It requires rapid iteration with users as part of a constant feedback loop. This digital platform runs on robust APIs that power internal tools, IRS public sites, and third-party software. Building this way ensures alignment across IRS teams, eliminates duplicate efforts, and lifts the entire tax software ecosystem. 

This is what we need to build for Americans:

Online tax filing: From annual panic to year-round readiness

Reboot Direct File. Stop forcing everything into tax season. Let taxpayers update information year-round—add a child, change addresses, adjust withholdings, upload documents. When April arrives, their return is already 90% complete. 

This is a natural evolution of Direct File and the existing non-editable online account dashboard into a living, breathing system taxpayers optimize throughout the year. And not just for individuals—this should be extended to businesses—reducing this burden for as many filer types as possible.

Pre-populated returns: Stop making people provide what the IRS already knows

The IRS already has W-2s, 1099s, and financial data. Use it. Pre-populate returns to cut filing time from hours to minutes. Deliver secure APIs so any tax software can access IRS data (with taxpayer permission), and use machine learning to flag issues including fraud before submission. This increases accuracy, reduces errors, and spurs competition by making it easy to switch between tax-filing programs.

Income verification as a service: Turn tax data into financial opportunity

The IRS sits on verified income data that could help Americans access government services, credit, mortgages, and benefits like student aid. Instead of weeks-long transcript requests, offer instant verification through secure APIs. This creates a government-backed source alongside credit bureaus, increases financial access, and reduces paperwork across all government services.

Tax calculator as a platform: One source of truth

Every tax software company recreates the same calculations, each slightly different. Across the organization, the IRS itself uses multiple third-party tax calculators in audits. This should be a core, integral service the IRS offers—build a definitive tax calculator as an API, the single source of truth that internal audits and checks use, and external software can access or run on their own. Make it transparent, auditable, and open source. Put up cash “bounties” to encourage the public to find bugs and errors and invite taxation-critics to review the code. Use generative AI to aid IRS accountants, lawyers and engineers translate tax law changes into code–speeding the roll out of Congressional tax changes.

When everyone calculates taxes the same way the IRS does, errors vanish. When everyone can see how the IRS does it, trust grows.

Modern MeF: From submission pipe to intelligent platform

Today’s Modernized e-File (MeF) is barely modern—it’s a dumb pipe that accepts tax returns and hopes for the best. Transform it into intelligent infrastructure that validates in real-time, catches errors immediately (not weeks later in confusing notices), and stops fraud before refunds are deposited. Build it like a real API, not XML dumps. Enable multi-part submissions so taxpayers can fix mistakes without starting over. This isn’t just a technical upgrade—it’s the foundation that makes every other improvement possible.

The Process

Ship Daily, Not Yearly

Taxpayer-first product development

The IRS is the single largest interaction point between Americans and their government. Every improvement saves millions of hours and builds trust. This requires abandoning bureaucratic processes for product thinking.

Build with taxpayers from day one through constant user testing and feedback loops. Organize around taxpayer journeys—”I need to update my withholdings” or “I’m checking my refund”—not org charts. 

Measure what matters: time-to-file, satisfaction scores, error rates, not only compliance metrics. Internal Objectives and Key Results planning makes priorities clear and syncs the organization towards focused goals. Publish Service Level Objectives on external products to ensure we target creating systems that others can confidently rely and build on.

Give full-stack product teams the authority to make integrated technical, design, policy and legal decisions together. Staff these teams with internal technologists embedded alongside accountants and lawyers in functional organizations, building IRS competency while reducing contractor dependence. Today’s IRS is highly siloed across functions with authority so fragmented it’s unclear who “owns” what. Yet go to any top tech organization and you’ll see what we’re pushing for: aligned and cross-functional teams whose job is delivering with clear ownership. Inherently we’re pushing for more than a new team, we’re factoring out unclear ownership in general away from IT and Business Divisions.

When teams own outcomes, we can better ensure taxpayer experience transforms from painful to painless.

API-first architecture

The IRS is fundamentally a data organization, yet information flows through siloed systems that can’t talk to each other. Amazon solved this with a simple mandate: all teams must expose their data and communicate through APIs. (This mindset set in motion the seeds of Amazon Web Services, the company’s most profitable division). 

The IRS needs the same revolution.

Every team exposes data and functionality through standardized REST APIs—no direct database access, no per-department clones of the data, no exceptions. Design every API to be externalizable (with strong access controls) from day one, unlocking government APIs to become platforms for innovation. When systems communicate through versioned APIs instead of tangled dependencies, teams can ship improvements daily without compromising everything else. This isn’t just technical architecture—it’s how modern organizations move fast without breaking things.

The People

Cultivate it, Don’t Outsource It; Build a Delivery Culture

A digital IRS that delivers for Americans cannot be built by the lowest bidder. Its core capability isn’t digitized forms–it’s people who can understand taxpayers’ needs, imagine solutions, design thoughtfully, ship them fast, listen to users, and keep improving based on feedback.

Silicon Valley understands this instinctively on two fronts. The fight for great engineers is the fight to build teams that can deliver great products. And two, no leading tech company outsources its own R&D. Delivering well-functioning and beloved products requires tight ownership of the product iteration loop. 

Businesses long learned to never outsource a core competency. OpenAI would never outsource the training of its models, Apple its industrial design, Google its search algorithm, or Facebook its social graph. The same should be true for the IRS. 

Yet, despite accepting 93% of its tax returns digitally, it still does not consider itself to be a digital-first agency. Building great teams is inseparable from building great taxpayer experiences. For decades, the agency has outsourced its technical mission and vision.

What we witnessed at the IRS was often vendor theater. Consultants transformed routine meetings into sales presentations that should have been dedicated to improving the products. Solutions specialists added layers of proprietary middleware, despite readily available enterprise-grade open source solutions running on commodity servers could easily meet the objectives. All of this unfolded within an organizational culture where securing contracts took precedence over delivering meaningful outcomes. Contracts that, of course, cost multiples more than the price of a competent internal team.

Commodities like cloud infrastructure or off-the-shelf software that serve broad, generic needs should absolutely be acquired externally. But the IRS’s critical, taxpayer-facing products—the systems at the heart of filing, payments, and taxpayer accounts—must be built and owned internally. There is only one agency that collects taxes for the United States of America. 

When everything is handed to vendors, the IRS sends more than money out the door; it loses institutional memory, technical craft, quality systems, and the ability to move quickly. A modern IRS cannot be built on rented skills.

Talent: Build a Permanent Product Core

This transformation starts with the people: build and keep an in-house corps of top-tier technologists—engineers, product managers, designers, user experience researchers—working in small, empowered, cross-functional teams hand in hand with fellow IRS accountants, auditors, customer service representatives and lawyers. Not a handful of digital specialists scattered in a bureaucracy as it was, but several hundred people whose full-time job is delivering and evolving the IRS’s core taxpayer experiences and services. 

• Create a dedicated Digital Profession inside the IRS, led by a Chief Digital Officer with the authority to hire, fire, and shape teams and technology stacks.
  
• Break the straitjacket of outdated civil service rules by creating specialist pay bands to compete for top talent like the CFPB has done.
  
• Empower cross-functional teams to ship without endless escalation. Start small, test early, iterate quickly, and make product decisions by those close to the work.

Funding: Invest in Teams, Not Projects

Current funding locks the IRS into one-off projects that end when the money runs out, leaving no path for iteration. A product-centered IRS needs enduring funding for enduring teams. Long-lived services, not short-lived milestones. This should be no surprise for a tax organization. There are two certainties in life; death and taxes. We should properly set ourselves up to manage the latter.

• Fund continuous development rather than one-and-done “delivery.”
  
• Tie funding to taxpayer outcomes like faster filing, fewer errors, higher satisfaction, instead of compliance checklists.
  
• Secure multi-year budgets for core product teams so they can improve services year-round, not scramble for appropriations each cycle.

This shift will reduce long-term capital costs and ensure that every dollar invested keeps improving the taxpayer experience. 

Quality & Standards: Build Once, Build Right

Owning our products means owning their quality. That requires clear, enforceable service standards, like performance, usability, scalability, and accessibility, that every IRS product must meet.

• Establish service performance benchmarks and hold teams accountable to them. These should be highly taxpayer centric; time to file, support response time, ease of use.
  
• Create communities of practice inside the organization to share patterns, tooling, and lessons learned across the agency.
  
• Apply spend controls that tie contract renewals to measurable outcomes and prevent redundant vendor builds.

Culture Eats Strategy: Time to Invest in a Delivery Culture

“Culture eats strategy for breakfast,” as Peter Drucker famously said. Yet government agencies too often treat culture-building as off-limits or irrelevant. This is backwards. Creating a shared, collaborative culture centered on delivery isn’t just important; it’s the foundation that makes everything else possible. The hardest and most critical step is investing in people. Give employees space to collaborate meaningfully, contribute their expertise, and take ownership of outcomes. Leadership must empower teams with real authority, establish clear performance standards, and hold everyone accountable for meeting—or exceeding—those benchmarks. Without this cultural shift, even the best strategy becomes just another plan gathering dust.

When every product meets the same high standard, trust in the IRS will grow—because taxpayers will feel it in every interaction.

A template for all agencies

The IRS touches more Americans than any other federal agency–making it the perfect proof point that the government can deliver digital products that work seamlessly. The principles–build for users, not compliance, shipping daily, not yearly, and keeping the talent in house is not unique to the IRS. 

We believe these goals and strategies apply to nearly every agency and level of government. Imagine Social Security retirement planning tools that lead to easy withholding adjustments, a Medicare/Medicaid that is easy to enroll in, or a FEMA with easy to file disaster relief disbursement.

Transform the IRS this towards this path, and then use these lessons to reset and lift up expectations between Americans and their government. One so easy citizens say: “That was it? That was easy.”

As digital technologies reshape every aspect of society, students must be equipped and proficient in not only specialized literacies (such as digital literacy, data literacy, and AI literacy), but with a foundational skill set that allows them to think critically, reason logically, and solve problems effectively. Analytical literacy is the scaffolding upon which more specialized literacies are built. Students in the 21st century need strong critical thinking skills like reasoning, questioning, and problem-solving, before they can meaningfully engage with more advanced domains like digital, data, or AI literacy. Without these skills, students may struggle to engage critically with the technologies shaping their lives. We urge education leaders at the federal, state, and institutional levels to prioritize development of analytical literacy by incentivizing integration across disciplines, aligning standards, and investing in research and professional development.

Introduction

As society becomes increasingly shaped by digital technologies, data-driven decision-making, and artificial intelligence, the ability to think analytically is no longer optional, it’s essential. While digital, data, and AI literacies focus on domain-specific skills, analytical literacy enables students to engage with these domains critically and ethically. Analytical literacy encompasses critical thinking, logical reasoning, and problem-solving, and equips students to interpret complex information, evaluate claims, and make informed decisions. These skills are foundational not only for academic success but for civic engagement and workforce readiness in the 21st century.

Despite its importance, analytical literacy remains unevenly emphasized in K–12 education. These disparities are often driven by systemic inequities in school funding, infrastructure, and access to qualified educators. According to NCES’s Education Across America report, rural schools and those in under-resourced communities frequently lack the professional development opportunities, instructional materials, and technology needed to support analytical skill-building. In contrast, urban and well-funded districts are more likely to offer inquiry-based curricula, interdisciplinary projects, and formative assessment tools that foster deep thinking. Additionally, while some schools integrate analytical thinking through inquiry-based learning, project-based instruction, or interdisciplinary STEM curricula, there is no consistent national framework guiding its development at this time. Instructional strategies vary widely by state or district, and standardized assessments often prioritize procedural fluency over deeper cognitive engagement like analytical reasoning.

Recent research underscores the urgency of this issue. A 2024 literature review from the Center for Assessment highlights analytical thinking as a core competency for future success, noting its role in supporting other 21st-century skills such as creativity, collaboration, and digital fluency. Similarly, a systematic review published in the International Journal of STEM Education emphasizes the need for early engagement with analytical and statistical thinking to prepare students for a data-rich society.

There is growing consensus among educators, researchers, and policy advocates that analytical literacy deserves a more central role in K–12 education. Organizations such as NWEA and Code.org have called for stronger integration of analytical and data literacy skills into curriculum and professional development efforts. However, without coordinated policy action, these efforts remain fragmented.

This memo builds on that emerging momentum. It argues that analytical literacy should be treated as a skill that underpins students’ ability to engage meaningfully with digital, data, and AI literacies. By elevating analytical literacy through standards, instruction, and investment, we can ensure that all students are prepared to participate, innovate, and thrive in a complex and rapidly changing world.

To understand why analytical literacy must be prioritized, we examine the current landscape of specialized literacies and the foundational skills they require.

Challenges and Opportunities

In today’s interconnected world, digital literacy, data literacy, and AI literacy are no longer optional, they are essential skill sets for civic participation, economic mobility, and ethical decision-making. These literacies enable students to navigate online environments, interpret complex datasets, and engage thoughtfully with emerging technologies.  

• Digital literacy encompasses the ability to use technology effectively and critically, including evaluating online information, understanding digital safety, and engaging ethically in digital environments. 
• Data Literacy involves the capacity to understand, interpret, evaluate, and communicate data. This includes recognizing data sources, identifying patterns, and drawing informed conclusions. 
• AI Literacy entails understanding the basic concepts of artificial intelligence, its applications, ethical implications, and how to interact with AI systems responsibly. 

Together, these literacies form a cognitive toolkit that empowers students to be not just consumers of information and technology, but thoughtful participants in civic and digital life.

While these literacies address specific domains, they all fundamentally rely on what should be called Analytical Literacy. Analytical literacy, at its core, involves the ability to: 

• Ask insightful questions. Identifying the core issues and seeking relevant information. 
• Evaluate information critically. Assessing the credibility, bias, and relevance of sources. 
• Identify patterns and relationships. Recognizing connections and trends in complex information.
• Reason logically. Constructing sound arguments and drawing valid inferences. 
• Solve problems effectively. Applying analytical skills to find solutions and make informed decisions.

Yet, without structured development of these foundational skills, students risk becoming passive consumers of technology rather than active, informed participants. This presents an urgent opportunity: by centering Analytical Literacy in standards and assessment, instruction, and professional learning, we can create enduring pathways for students to participate, innovate, and thrive in an increasingly data-driven world. 

Examples of implementation must include:

• In Standards and Assessment. States should revise academic standards to include grade-level expectations for analytical reasoning across disciplines. For example, middle school science standards might require students to construct evidence-based arguments using data, while high school civics assessments could include open-ended questions that ask students to evaluate competing claims in news media.
• In Instruction. Teachers should embed analytical skill development into daily practice through inquiry-based learning, Socratic seminars, or interdisciplinary projects. A math teacher could guide students in analyzing real-world datasets to identify trends and make predictions, while an English teacher might use argument mapping to help students deconstruct persuasive texts.
• In Professional Learning. Districts should offer workshops that train educators to use formative assessment strategies that surface student reasoning such as think-alouds, peer critiques, or performance tasks. Coaching cycles should focus on how to scaffold questioning techniques that push students beyond recall toward deeper analysis.

By embedding these practices systemically, we move from episodic exposure to analytical thinking toward a coherent, equitable framework that prepares all students for the demands of the digital age.

Addressing these gaps requires coordinated action across multiple levels of the education system. The following plan outlines targeted strategies for federal, state, and institutional leaders.

Plan of Action

To strengthen analytical literacy in K–12 education, we recommend targeted efforts from three federal offices, supported by state agencies, educational organizations, and teacher preparation programs.

Recommendation 1. Federal Offices

Federal agencies have the capacity to set national priorities, fund innovation, and coordinate cross-sector efforts. Their leadership is essential to catalyzing systemic change. For example:

White House Office of Science and Technology Policy (OSTP)

OSTP now chairs the newly established White House Task Force on Artificial Intelligence Education, per the April 2025 Executive Order on Advancing AI Education. This task force is charged with coordinating federal efforts to promote AI literacy and proficiency across the K–12 continuum. We recommend that OSTP:

• Expand the scope of the Task Force to explicitly include analytical literacy as a foundational competency for AI readiness.
• Ensure that public-private partnerships and instructional resources developed under the order emphasize reasoned decision-making as a core component, not just technical fluency.
• Use the Presidential Artificial Intelligence Challenge as a platform to showcase interdisciplinary student work that demonstrates analytical thinking applied to real-world AI problems.

This alignment would ensure that analytical literacy is not treated as an adjacent concern, but as a central pillar of the federal AI education strategy.

Institute of Education Sciences (IES)

IES should coordinate closely with the Task Force to support the Executive Order’s goals through a National Analytical Literacy Research Agenda. This agenda could:

• Fund studies that explore how analytical thinking supports AI literacy across grade levels.
• Evaluate the effectiveness of instructional models that integrate analytical reasoning into AI and computer science curricula.
• Develop scalable tools and assessments that measure students’ analytical readiness for AI-related learning pathways.

IES could also serve as a technical advisor to the Task Force, ensuring that its initiatives are grounded in evidence-based practice.

Office of Elementary and Secondary Education (OESE)
In light of the Executive Order’s directive for educator training and curriculum innovation, OESE should:

Prioritize analytical literacy integration in discretionary grant programs that support AI education.

Develop guidance for states on embedding analytical competencies into AI-related standards and instructional frameworks.

Collaborate with the Task Force to ensure that professional development efforts include training on how to teach analytical thinking—not just how to use AI tools.

National Science Foundation (NSF)

The National Science Foundation plays a pivotal role in advancing STEM education through research, innovation, and capacity-building. To support the goals of the Executive Order and strengthen analytical literacy as a foundation for AI readiness, we recommend that NSF:

• Establish a dedicated grant program focused on developing and scaling instructional models that integrate analytical literacy into STEM and AI education. This could include interdisciplinary curricula, project-based learning frameworks, and performance-based assessments that emphasize reasoning, problem-solving, and data interpretation.
• Fund research-practice partnerships that explore how analytical thinking develops across grade levels and how it supports students’ engagement with AI concepts. These partnerships could include school districts, universities, and professional organizations working collaboratively to design and evaluate scalable models.
• Support educator capacity-building initiatives, such as fellowships or professional learning networks, that equip teachers to foster analytical literacy in STEM classrooms. This aligns with NSF’s recent Dear Colleague Letters on expanding K–12 resources for AI education.
• Invest in technology-enhanced learning tools that provide real-time feedback on student reasoning and support formative assessment of analytical skills. These tools could be piloted in diverse school settings to ensure equity and scalability.

By positioning analytical literacy as a research and innovation priority, NSF can help ensure that K–12 students are not only technically proficient but cognitively prepared to engage with emerging technologies in thoughtful, ethical, and creative ways.

Note: Given the evolving organizational landscape within the U.S. Department of Education—including the elimination of offices like Educational Technology—it is critical to identify stable federal anchors. The agencies named above have longstanding mandates tied to research, policy innovation, and K–12 support, making them well-positioned to advance this work.

Recommendation 2. State Education Policymakers

While federal agencies can provide vision and resources, states hold the levers of implementation. Their role is critical in translating policy into classroom practice.

While federal agencies can provide strategic direction and funding, the implementation of analytical literacy must be led by states. Each state has the authority—and responsibility—to shape standards, assessments, and professional development systems that reflect local priorities and student needs. To advance analytical literacy meaningfully, we recommend the following actions:

Elevate Analytical Literacy in Academic Standards

States should conduct curriculum audits to identify where analytical skills are currently embedded—and where gaps exist. This process should inform the revision of academic standards across disciplines, ensuring that analytical literacy is treated as a foundational competency, not an ancillary skill. California’s ELA/ELD Framework, for example, emphasizes inquiry, argumentation, and evidence-based reasoning across subjects—not just in English language arts. Similarly, the History–Social Science Framework promotes critical thinking and source evaluation as core civic skills.

States can build on these models by:

• Developing cross-disciplinary analytical literacy frameworks that guide integration from elementary through high school.
• Embedding analytical competencies into STEM, humanities, and career technical education standards.
• Aligning revisions with the goals of the Executive Order, which calls for foundational skill-building to support digital and AI literacy.

Invest in Professional Development and Instructional Capacity

States should fund and scale professional learning ecosystems that equip educators to teach analytical thinking explicitly. This includes:

• Training on inquiry-based learning, Socratic dialogue, and formative assessment strategies that surface student reasoning.
• Development of microcredential pathways for educators to demonstrate expertise in fostering analytical literacy across content areas.
• Support for instructional coaches and teacher leaders to model analytical practices and mentor peers.

California’s professional learning modules aligned to the Common Core State Standards and ELA/ELD frameworks offer a useful starting point for designing scalable, standards-aligned training.

Redesign Student Assessments to Capture Deeper Thinking

States should move beyond traditional standardized tests and invest in assessment systems that measure analytical reasoning authentically. States can catalyze this innovation by issuing targeted Requests for Proposals (RFPs) that invite districts, assessment developers, and research-practice partnerships to design and pilot new models of assessment aligned to analytical literacy. These RFPs should prioritize:

• Performance tasks that require students to analyze real-world problems and propose solutions.
• Portfolio assessments that document students’ growth in reasoning and problem-solving over time.
• Open-ended questions that ask students to evaluate claims, synthesize evidence, and construct logical arguments.
• Scalable models that can inform statewide systems over time.

By using the RFP process strategically, states can surface promising practices, support local innovation, and build a portfolio of assessment approaches that reflect the complexity of students’ analytical capabilities.

Recommendation 3. Professional Education Organizations

Beyond government, professional education organizations shape the field through resources, advocacy, and collaboration. They are key partners in scaling analytical literacy.

Professional education organizations play a vital role in shaping the landscape of K–12 education. These groups—ranging from subject-specific associations like the National Council of Teachers of English (NCTE) and the National Science Teaching Association (NSTA), to broader coalitions like ASCD and the National Education Association (NEA)—serve as hubs for professional learning, policy advocacy, resource development, and field-wide collaboration. They influence classroom practice, inform state and federal policy, and support educators through research-based guidance and community-building.

Because these organizations operate at the intersection of practice, policy, and research, they are uniquely positioned to champion analytical literacy as a foundational skill across disciplines. To advance this work, we recommend the following actions:

• Develop Flexible, Discipline-Specific Resources. Create adaptable instructional materials—such as lesson plans, assessment templates, and classroom protocols—that help educators integrate analytical thinking into diverse subject areas. For example, NCTE could develop resources that support argument mapping in English classrooms, while NSTA might offer tools for teaching evidence-based reasoning in science labs.
• Advocate for Analytical Literacy as a National Priority. Publish position papers, host public events, and build strategic partnerships that elevate analytical literacy as essential to digital and civic readiness. Organizations can align their advocacy with the federal directive for AI education, emphasizing the role of analytical thinking in preparing students for ethical and informed engagement with emerging technologies.
• Foster Cross-Sector Collaboration. Convene working groups, research-practice partnerships, and educator networks to share best practices and scale effective models. For example, AERA could facilitate studies on how analytical literacy develops across grade levels, while CoSN might explore how digital tools can support real-time feedback on student reasoning.

By leveraging their convening power, subject-matter expertise, and national reach, professional education organizations can accelerate the adoption of analytical literacy and ensure it is embedded meaningfully into the fabric of K–12 education.

Recommendation 4. Teacher Preparation Programs

To sustain long-term change, we must begin with those entering the profession. Teacher preparation programs are the foundation for instructional capacity and must evolve to meet this moment.

Teacher preparation programs (TPPs) are the gateway to the teaching profession. Housed in colleges, universities, and alternative certification pathways, these programs are responsible for equipping future educators with the knowledge, skills, and dispositions needed to support student learning. Their influence is profound: research consistently shows that well-prepared teachers are the most important in-school factor for student success.

Yet many TPPs face persistent challenges. Too often, graduates report feeling underprepared for the realities of diverse, data-rich classrooms. Coursework may emphasize theory over practice, and clinical experiences vary widely in quality. Critically, few programs offer explicit training in how to foster analytical literacy—despite its centrality to digital, data, and AI readiness. In response to national calls for foundational skill-building and educator capacity, TPPs must evolve to meet this moment.

While federal funding for teacher preparation has become more limited, states are stepping in through innovative models like teacher residencies, registered apprenticeships, and microcredentialing pathways. These initiatives are often supported by modified use of Title II funds, state general funds, and workforce development grants. To accelerate this momentum, federal programs like Teacher Quality Partnership (TQP) grants and Supporting Effective Educator Development (SEED) grants could be adapted to prioritize analytical literacy, while states can issue targeted RFPs to redesign coursework, practicum experiences, and capstone projects that center reasoning, problem-solving, and ethical decision-making. To ensure that new teachers are ready to cultivate analytical thinking in their students, we recommend the following actions:

• Integrate Analytical Pedagogy into Coursework and Practicum. Embed instructional strategies that center analytical literacy into pre-service coursework. This includes training in inquiry-based learning, argumentation, and data interpretation. Practicum experiences should reinforce these strategies through guided observation and practice in real classrooms.
• Ensure Faculty Model Analytical Thinking. Faculty must demonstrate analytical reasoning in their own teaching—whether through modeling how to deconstruct complex texts, facilitating structured debates, or using data to inform instructional decisions. This modeling helps pre-service teachers internalize analytical habits of mind.
• Strengthen Field Placements for Analytical Instruction. Partner with districts to place candidates in classrooms where analytical literacy is actively taught. Provide structured mentorship from veteran teachers who use questioning techniques, performance tasks, and formative assessments to surface student reasoning.
• Develop Capstone Projects Focused on Analytical Literacy. Require candidates to complete a culminating project that demonstrates their ability to design, implement, and assess instruction that builds students’ analytical skills. These projects could be aligned with state standards and local district priorities.
• Align Program Outcomes with Emerging Policy Priorities. Ensure that program goals reflect the competencies outlined in federal initiatives like the AI Education Executive Order. This includes preparing teachers to support foundational literacies that enable students to engage critically with digital and AI technologies.

Together, these actions form a coherent strategy for embedding analytical literacy across the K–12 continuum. But success depends on bold leadership and sustained commitment. By reimagining teacher preparation through the lens of analytical literacy, we can ensure that every new educator enters the classroom equipped to foster deep thinking, ethical reasoning, and problem-solving—skills that students need to thrive in a complex and rapidly changing world.

Conclusion

Analytical literacy is not a nice-to-have, it is a prerequisite for the specialized proficiencies students need in today’s complex world. By embedding critical thinking, logical reasoning, and problem-solving across the K–12 continuum, we empower students to meet challenges with curiosity and discernment. We urge policymakers, educators, and institutions to act boldly by demanding analytical literacy be established as a cornerstone of 21st-century education. and co-create a future where every student has the analytical tools essential for meaningful participation, innovative thinking, and long-term success in the digital age and beyond.

Through partnership with the Doris Duke Foundation, FAS is advancing a vision for healthcare innovation that centers safety, equity, and effectiveness in artificial intelligence. Inspired by work from the Social Science Research Council (SSRC) and Arizona State University (ASU) symposiums, this memo explores new research models such as large-scale behavioral “megastudies” and how they can transform our understanding of what drives healthier choices for longer lives. Through policy entrepreneurship FAS engages with key actors in government, research, academia and industry. These recommendations align with ongoing efforts to integrate human-centered design, data interoperability, and evidence-based decision-making into health innovation.

By shifting funding from small underpowered randomized control trials to large field experiments in which many different treatments are tested synchronously in a large population using the same objective measure of success, so-called megastudies can start to drive people toward healthier lifestyles. Megastudies will allow us to more quickly determine what works, in whom, and when for health-related behavioral interventions, saving tremendous dollars over traditional randomized controlled trial (RCT) approaches because of the scalability. But doing so requires the government to back the establishment of a research platform that sits on top of a large, diverse cohort of people with deep demographic data. 

Challenge and Opportunity

According to the National Research Council, almost half of premature deaths (< 86 years of age) are caused by behavioral factors. Poor diet, high blood pressure, sedentary lifestyle, obesity, and tobacco use are the primary causes of early death for most of these people. Yet, despite studying these factors for decades, we know surprisingly little about what can be done to turn these unhealthy behaviors into healthier ones. This has not been due to a lack of effort. Thousands of randomized controlled trials intended to uncover messaging and incentives that can be used to steer people towards healthier behaviors have failed to yield impactful steps that can be broadly deployed to drive behavioral change across our diverse population. For sure, changing human behavior through such mechanisms is controversial, and difficult. Nonetheless studying how to bend behavior should be a national imperative if we are to extend healthspan and address the declining lifespan of Americans at scale.

Limitations of RCTs

Traditional randomized controlled trials (RCTs), which usually test a single intervention, are often underpowered, and expensive, and short-lived, limiting their utility even though RCTs remain the gold standard for determining the validity of behavioral economics studies. In addition, because the diversity of our population in terms of biology, and culture are severely limiting factors for study design, RCTs are often conducted on narrow, well-defined populations. What works for a 24-year-old female African American attorney in Los Angeles may not be effective for a 68-year-old male white fisherman living in Mississippi. Overcoming such noise in the system means either limiting the population you are examining through demographics, or deploying raw power of numbers of study participants that can allow post study stratification and hypothesis development. It also means that health data alone is not enough. Such studies require deep personal demographic data to be combined with health data, and wearables. In essence, we need a very clear picture of the lives of participants to properly identify interventions that work and apply them appropriately post-study on broader populations. Similarly, testing a single intervention means that you cannot be sure that it is the most cost-effective or impactful intervention for a desired outcome. This further limits the ability to deploy RCTs at scale. Finally, the data sometimes implies spurious associations. Therefore, preregistration of endpoints, interventions, and analysis of such studies will make for solid evidence development even if the most tantalizing outcomes come from sifting through the data later to develop new hypotheses that can be further tested. 

Value of Megastudies

Professors Angela Duckworth and Katherine Milkman, at the University of Pennsylvania, have proposed an expansion of the use of megastudies to gain deeper behavioral insights from larger populations. In essence, megastudies are “massive field experiments in which many different treatments are tested synchronously in a large sample using a common objective outcome.” This sort of paradigm allows independent researchers to develop interventions to test in parallel against other teams. Participants are randomly assigned across a large cohort to determine the most impactful and cost-effective interventions. In essence, the teams are competing against each other to develop the most effective and practical interventions on the same population for the same measurable outcome. 

Using this paradigm, we can rapidly assess interventions, accelerate scientific progress by saving time and money, all while making more appropriate comparisons to bend behavior towards healthier lifestyles. Due to the large sample sizes involved and deep knowledge of the demographics of participants, megastudies allow for the noise that is normal in a broad population that normally necessitates narrowing the demographics of participants. Further, post study analysis allows for rich hypothesis generation on what interventions are likely to work in more narrow populations. This enables tailored messaging and incentives to the individual. A centralized entity managing the population data reduces costs and makes it easier to try a more diverse set of risk-tolerant interventions. A centralized entity also opens the door to smaller labs to participate in studies. Finally, the participants in these megastudies are normally part of ongoing health interactions through a large cohort study or directly through care providers. Thus, they benefit directly from participation and tailored messages and incentives. Additionally, dataset scale allows for longer term study design because of the reduction in overall costs. This enables study designers to determine if their interventions work well over a longer period of time or if the impact of interventions wane and need to be adjusted.

Funding and Operational Challenges

But this kind of “apples to apples” comparison has serious drawbacks that have prevented megastudies from being used routinely in science despite their inherent advantage. First, megastudies require access to a large standing cohort of study participants that will remain in the cohort long term. Ideally, the organizer of such studies should be vested in having positive outcomes. Here, larger insurance companies are poor targets for organizing. Similarly, they have to be efficient, thus, government run cohorts, which tend to be highly bureaucratic, expensive, and inefficient are not ideal. Not everything need go through a committee. (Looking at you, All of Us at NIH and Million Veterans Program at the VA). 

Companies like third party administrators of healthcare plans might be an ideal organizing body, but so can companies that aim to lower healthcare costs as a means of generating revenue through cost savings. These companies tend to have access to much deeper data than traditional cohorts run by government and academic institutions and could leverage that data for better stratifying participants and results. However, if the goal of government and philanthropic research efforts is to improve outcomes, then they should open the aperture on available funds to stand up a persistent cohort that can be used by many researchers rather than continuing the one-off paradigm, which in the end is far more expensive and inefficient. Finally, we do not imply that all intervention types should be run through megastudies. They are an essential, albeit underutilized tool in the arsenal, but not a silver bullet for testing behavioral interventions.

Fear of Unauthorized Data Access or Misuse 

There is substantial risk when bringing together such deep personal data on a large population of people. While companies compile deep data all the time, it is unusual to do so for research purposes and will, for sure, raise some eyebrows, as has been the case for large studies like the aforementioned All of Us and the Million Veteran’s Program. 

Patients fear misuse of their data, inaccurate recommendations, and biased algorithms—especially among historically marginalized populations. Patients must trust that their data is being used for good, not for marketing purposes and determining their insurance rates. 

Icons © 2024 by Jae Deasigner is licensed under CC BY 4.0

Need for Data Interoperability

Many healthcare and community systems operate in data silos and data integration is a perennial challenge in healthcare. Patient-generated data from wearables, apps, or remote sensors often do not integrate with electronic health record data or demographic data gathered from elsewhere, limiting the precision and personalization of behavior-change interventions. This lack of interoperability undermines both provider engagement and user benefit. Data fragmentation and poor usability requires designing cloud-based data connectors and integration, creating shared feedback dashboards linking self-generated data to provider workflows, and creating and promoting policies that move towards interoperability. In short, given the constantly evolving data integration challenge and lack of real standards for data formats and integration requirements, a dedicated and persistent effort will have to be made to ensure that data can be seamlessly integrated if we are to draw value from combining data from many sources for each patient.

Additional Barriers

One of the largest barriers to using behavioral economics is that some rural, tribal, low-income, and older adults face access barriers. These could include device affordability, broadband coverage, and other usability and digital literacy limitations. Megastudies are not generally designed to bridge this gap leaving a significant limitation of applicability for these populations. Complicating matters, these populations also happen to have significant and specific health challenges unique to their cohorts. As the use of behavioral economic levers are developed, these communities are in danger of being left behind, further exacerbating health disparities. Nonetheless, insight into how to reach these populations can be gained for individuals in these populations that do have access to technology platforms. Communications will have to be tailored accordingly. 

External motivators have been consistently shown to be the essential drivers of behavioral change. But motivation to sustain a behavior change and continue using technology often wanes over time. Embedding intrinsic-value rewards and workplace incentives may not be enough. Therefore, external motivations likely have to be adjusted over time in a dynamic system to ensure that adjustments to the behavior of the individual can be rooted in evidence. Indeed, study of the dynamic nature of driving behavioral change will be necessary due to the likelihood of waning influence of static messaging. By designing reward systems that tie personal values and workplace wellness programs sustained engagement through social incentives and tailored nudges may keep users engaged.

Plan of Action 

By enabling a private sector entity to create a research platform using patient data combined with deep demographic data, and an ethical framework for access and use, we can create a platform for megastudies. This would  allow the rapid testing of behavioral interventions that steer people towards healthier lifestyles, saving money, accelerating progress, and better understanding what works, in whom, and when for changing human behavior. 

This could have been done through either the All of Us program or Million Veterans program or a different large cohort study, but neither program has the deep demographic and lifestyle data required to stratify their population. Both are mired in bureaucratic lethargy that is common in large scale government programs. Health insurance companies and third-party administrators of health insurance can gather such data, be nimbler, create a platform for communicating directly with patients, coordinate with their clinical providers. But one could argue that neither entity has a real incentive to bend behavior and encourage healthy lifestyles. Simply put, that is not their business.

Recommendation 1. Issue a directive to agencies to invest in the development of a megastudy platform for health behavioral economics studies.

The White House of HHS Secretary should direct the NIH or ARPA-H to develop a plan for funding the creation of a behavioral economics megastudy platform. The directive should include details on the ethical and technical framework requirements as well as directions for development of oversight of the platform once it is created. The platform should be required to establish a sustainability plan as part of the application for a contract to create the megastudy platform. 

Recommendation 2. Government should fund the establishment of a megastudy platform.

ARPA-H and/or DARPA should develop a program to establish a broad research platform in the private sector that will allow for megastudies to be conducted. Then research teams can, in parallel, test dozens of behavioral interventions on populations and access patient data. This platform should have required ethical rules and be grounded in data sovereignty that allows patients to opt out of participation and having their data shared.

Data sovereignty is one solution to the trust challenge. Simply put, data sovereignty means that patients have access to the data on themselves (without having to pay a fee that physicians’ offices now routinely charge for access) and control over who sees and keeps that data. So, if at any time, a participant changes their mind, they can get their data and force anyone in possession of that data to delete it (with notable exceptions, like their healthcare providers). Patients would have ultimate control of their data in a ‘trust-less’ way that they never need to surrender, going well past the rather weak privacy provisions of HIPAA, so there is no question that they are in charge.

We suggest that using blockchain and token systems for data transfer would certainly be appropriate. Data held in a federated network to limit the danger of a breach would also be appropriate. 

Recommendation 3. The NIH should fund behavioral economics megastudies using the platform. 

Once the megastudy platform(s) are established, the NIH should make dedicated funds available for researchers to test for behavioral interventions using the platform to decrease costs, increase study longevity, and improve speed and efficiency for behavioral economics studies on behavioral health interventions. 

Conclusion

Randomized controlled trials have been the gold standard for behavioral research but are not well suited for health behavioral interventions on a broad and diverse population because of the required number of participants, typical narrow population, recruiting challenges, and cost. Yet, there is an urgent need to encourage and incentivize d health related behaviors to make Americans healthier. Simply put, we cannot start to grow healthspan and lifespan unless we change behaviors towards healthier choices and habits. When the U.S. government funds the establishment of a platform for testing hundreds of behavioral interventions on a large diverse population, we will start to better understand the interventions that will have an efficient and lasting impact on health behavior. Doing so requires private sector cooperation and strict ethical rules to ensure public trust.

This memo produced as part of Strengthening Pathways to Disease Prevention and Improved Health Outcomes.

Through partnership with the Doris Duke Foundation, FAS is advancing a vision for healthcare innovation that centers safety, equity, and effectiveness in artificial intelligence. Informed by the NYU Langone Health symposium on transforming health systems into learning health systems, FAS seeks to ensure that AI tools are developed, deployed, and evaluated in ways that reflect real-world clinical practice. FAS is leveraging its role in policy entrepreneurship to promote responsible innovation by engaging with key actors in government, research, and software development. These recommendations align with emerging efforts across health systems to integrate human-centered AI and evidence-based decision-making into digital transformation. By shaping AI grant requirements and post-market evaluation standards, these ideas aim to accelerate safe, equitable implementation while supporting ongoing learning and improvement.   

The United States must ensure AI improves healthcare while safeguarding patient safety and clinical expertise. There are three priority needs:

• Embedding clinician involvement in the development and testing of AI tools
• Using representative data and promoting human-centered design
• Maintaining continuous oversight through post-market evaluation and outcomes-based contracting

This memo examines the challenges and opportunities related to integrating AI tools into healthcare. It emphasizes how human-centered design must ensure these technologies are tailored to real-world clinical environments. As AI adoption grows in healthcare, it is essential that clinician feedback is embedded into the federal grant requirements for AI development to ensure these systems are effective and aligned with real-world needs. Embedding clinician feedback into grant requirements for healthcare AI development and ensuring the use of representative data will assist with promoting safety, accuracy, and equity in healthcare tools. In addition, regular updates to these tools based on evolving clinical practices and patient populations must be part of the development lifecycle to maintain long-term reliability. Continuous post-market surveillance is necessary to ensure these tools remain both accurate and equitable. By taking these steps, healthcare systems can harness the full potential of AI while safeguarding patient safety and clinician expertise. Federal agencies such as the Office of the National Coordinator for Health Information Technology (ONC), the Food and Drug Administration (FDA) can incentivize clinician involvement through outcomes-based contracting approaches that link funding to measurable improvements in patient care. This strategy ensures that grant recipients embed clinician expertise at key stages of development and testing, ultimately aligning incentives with real-world health outcomes.

Challenge and Opportunity

The use of AI tools such as predictive triage classifiers and large language models (LLMs) have the potential to improve care delivery. However, there are significant challenges in integrating these tools effectively into daily clinical workflows without meaningful clinician involvement. As just one  example, AI tools used in chronic illness triage can be particularly useful in helping to prioritize patients based on the severity of their condition, which can lead to timely care delivery. However, without direct involvement from clinicians in validating, interpreting, and guiding AI recommendations, these tools can suffer from poor usability and limited real-world effectiveness. Even highly accurate tools can become irrelevant if they are not adopted and clinicians do not engage with them, thereby reducing the positive impact they can have on patient outcomes.

Mysterious Inner Workings

The mysterious box of AI has fueled skepticism among healthcare providers and undermined trust among patients. Moreover, when AI systems lack clear and interpretable explanations, clinicians are more likely to avoid or distrust them. This response is attributed to what’s known as algorithm aversion. Algorithm aversion occurs when clinicians lose trust in a tool after seeing it make errors, making future use less likely, even if the tool is usually accurate. Designing AI with human-centered principles, particularly offering clinicians a role where they can validate, interpret, and guide AI recommendations, will help build trust and ensure decisions remain grounded in clinical expertise. A key approach to increasing trust and usability would be institutionalizing clinician engagement in the early stages of the development process. By involving clinicians during the development and testing phases, AI developers can ensure the tools fit seamlessly into clinical workflows. This will also help to mitigate concerns about the tool’s real-world effectiveness, as clinicians will be more likely to adopt tools they feel confident in. Without this collaborative approach, AI tools risk being sidelined or misused, preventing health systems from becoming genuinely adaptive and learning oriented. 

Lack of Interoperability

A significant challenge in deploying AI tools across healthcare systems is the issue of interoperability. Most patients receive care across multiple providers and healthcare settings, making it essential for AI tools to be able to seamlessly integrate with electronic health records (EHR) and other clinical systems. Not having this integration could lead to tools losing their clinical relevance, effectiveness, and ability to be adopted on a larger scale. This lack of connectivity can lead to inefficiencies, duplicate testing, and other harmful errors. One way to address this is through a contracting process called Outcomes-based contracting (OBC), discussed shortly.

Trust in AI and Skill Erosion

Beyond trust and usability, there are broader risks associated with sidelining clinicians during AI integration. The use of AI tools without clinician input also presents the risk of clinician deskilling. Deskilling refers to the occurrence where clinicians’ skills and decision-making abilities erode over time due to their reliance on AI tools. This skill erosion  leads to a decline in the judgement in situations where AI may not be readily available or suitable. Recent evidence from the ACCEPT trial shows that endoscopists’ performance dropped in non-AI settings after months of AI-assisted procedures. This presents a troubling phenomenon that we should aimt to prevent. AI-induced skill erosion also raises ethical concerns, particularly in complex environments where over-reliance on AI could erode clinical judgement and autonomy. If clinicians become too dependent on automated outputs, their ability to make critical decisions may be compromised, potentially impacting patient safety.

Embedded Biases

In addition to the erosion of human skills, AI systems also risk embedding biases if trained on unrepresentative data, leading to unfair or inaccurate outcomes across different patient groups. AI tools may present errors that appear plausible, such as generating nonexistent terms, which pose serious safety concerns, especially when clinicians don’t catch those mistakes. A systematic review of AI tools found that 22% of studies involved clinicians throughout the development phase. This lack of early clinician involvement has contributed to usability and integration issues across AI healthcare tools.

All of these issues underscore how critical clinician involvement is in the development of AI tools to ensure they are usable, effective, and safe. Clinician  involvement should include defining relevant clinical tasks, evaluating interpretability of the system, validating performance across diverse patient groups, and setting standards for handoff between AI and clinician decision-making. Therefore, funding agencies should require AI developers to incorporate representative data and meaningful clinician involvement in order to mitigate these risks. Recognizing these challenges, it’s crucial to understand that implementing and maintaining AI requires continual human oversight and substantial infrastructure. Many health systems find this infrastructure too resource-intensive to properly sustain. Given the complexity of these challenges, without adequate governance, transparency, clinician training, and ethical safeguards, AI may hinder rather than help the transition to an enhanced learning health system.

Outcome-based Models (OBM)

To ensure that AI tools deliver properly, the federal contracting process should reinforce clinical involvement through measurable incentives. Outcomes-based contracting (OBC), a model where payments or grants are tied to demonstrated improvements in patient outcomes, can be a powerful tool. This model is not only a financing mechanism, but serves as a lever to institutionalize clinician engagement. By tying funding to real-world clinical impact, this compels developers to design tools that clinicians will use and find value in, ultimately increasing usability, trust, and adoption. This model provides a clear reward for impact rather than just for building tools or producing novel methods.

Leveraging outcomes-based models could also help institutionalize clinician engagement in the funding lifecycle. This would ensure developers demonstrate explicit plans for clinician participation through staff integration or formal consultation as a prerequisite for funding. Although AI tools may be safe and effective at the initial onset of their use, performance can change over time due to various patient populations, changes in clinical practice, and updates to software. This is known as model degradation. Therefore, a crucial component of using these AI tools needs to be regular surveillance to ensure the tools remain accurate, responsive to real-world use with clinicians and patients, and equitable. However, while clinician involvement is essential, it is important to acknowledge that including clinicians in all stages of the AI tool development, testing, deployment, and evaluation may not be realistic given the significant time cost for clinicians, their competing clinical responsibilities, and their limited familiarity with AI technology. Despite these factors, there are ways to engage clinicians effectively at key decision points during the AI development and testing process without requiring their presence at every stage.

Urgency and Federal Momentum

Major challenges associated with integrating AI into clinical workflows due to poor usability, algorithm aversion, clinician skepticism, and the potential for embedded biases in these tools highlight a need for thoughtful deployment of these tools. These challenges have presented a sense of urgency in light of recent healthcare shifts, particularly with the rapid acceleration of AI adoption after the COVID-19 pandemic. This drove breakthroughs in the areas of telemedicine, diagnostics, and pharmaceutical innovation that simply weren’t possible before. However, with the rapid pace of integration also comes the risk of unregulated deployment, potentially embedding safety vulnerabilities. Federal momentum supports this growth, with directives placing emphasis on AI safety, transparency, and responsible deployment, including the authorization of over 1,200 AI powered medical devices, primarily used in radiology, cardiology, and pathology, which tend to be areas that are complex in nature. However, without clinician involvement and the use of representative data for training, algorithms for devices such as the ones mentioned may remain biased and fail to integrate smoothly into care delivery. This disconnect could delay adoption, reduce clinical impact, and increase the risk of patient harm. Therefore, it’s imperative we set standards, embed clinician expertise in AI design, and ensure safe, effective deployment for the specific use of care delivery. 

Furthermore, this moment of federal momentum aligns with broader policy shifts. As highlighted by a recent CMS announcement, the White House and national health agencies are working with technology leaders to create a patient-centric healthcare ecosystem. This includes a push for interoperability, clinical collaboration, and outcomes-driven innovation, all of which bolster the case for clinician engagement being woven into the very fabric of AI development. AI can potentially improve patient outcomes dramatically, as well as increase cost-efficiency in healthcare. Yet, without structured safeguards, these tools may deepen existing health inequities. However,  with proper input from clinicians, these tools can reduce diagnostic errors, improve accuracy in high-stakes cases such as cancer detection, and streamline workflows, ultimately saving lives and reducing unnecessary costs. 

As AI systems become further embedded into clinical practice, they will help to shape standards of care, influencing clinical guidelines and decision-making pathways. Furthermore, interoperability is essential when using these tools because most patients receive care from multiple providers across systems. Therefore, AI tools must be designed to communicate and integrate data from various sources, including electronic health records (EHR), lab databases, imaging systems, and more. Enabling shared access can enhance the coordination of care and reduce redundant testing or conflicting diagnoses. To ensure this functionality, clinicians must help design AI tools that account for real-world care delivery across what is currently a fragmented system.

Reshaping Healthcare AI 

These challenges and risks culminate in a moment of opportunity where we can reshape and revolutionize the way AI supports healthcare delivery to ensure that its design is trustworthy and focused on outcomes. To fully realize this opportunity, clinicians must be embedded into various stages of AI development technology to  improve its safety, usability, and adoption in healthcare settings. While some developers do involve clinicians during development, this practice is not the standard. Bridging this gap requires targeted action to ensure clinical expertise is consistently incorporated from the start. One way to achieve this is through federal agencies requiring AI developers to integrate representative data and clinician feedback into their AI tools as a condition of funding eligibility. This approach would improve the usability of the tool and enhance its contextual relevance to diverse patient populations and practice environments. Further, it would address current shortcomings as evidence has shown that some AI tools are poorly integrated into clinical workflows, which not only reduces their impact, but also undermines broader adoption and clinician confidence in the systems. Moreover, creating a clinician feedback loop for these systems will reduce the clerical burden that many clinicians experience and allow them to spend more dedicated time with their patients. Through the incorporation of human-centered design, we can mitigate issues that would normally arise by using clinician expertise during the development and testing process. This approach would build trust amongst clinicians and improve patient safety, which is most important when aiming to reduce errors and misinterpretations of diagnoses. With strong requirements and funding standards in place as safeguards, AI can transform health systems into adaptable learning environments that produce evidence and deliver equitable and higher quality care. This is a pivotal opportunity to showcase how innovation can support human expertise and strengthen trust in healthcare. 

AI has the potential to dramatically improve patient outcomes and healthcare cost-efficiency, particularly in high-stakes diagnostic and treatment decisions like oncology, and critical care. In these areas, AI can analyze imaging, lab, and genomic data to uncover patterns that may not be immediately apparent to clinicians. For example, AI tools have shown promise in improving diagnostic accuracy in cancer detection and reducing the time clinicians spend on tasks like charting, allowing for more face-to-face time with patients. 

However, these tools must be designed with clinician input at key stages, especially for higher-risk conditions, or tools may be prone to errors or fail to integrate into clinical workflows. By embedding outcome-based contracting (OBC) into federal funding and aligning financial incentives with clinical effectiveness, we are encouraging the development and use of AI tools that have the ability to improve patient outcomes and strengthen the healthcare system’s shift toward value-based care. This supports a broader shift toward value-based care where outcomes, not just outputs, define success. 

The connection between OBC and clinician involvement is straightforward. When clinicians are involved in the design and testing of AI tools, these tools are more likely to be effective in real-world settings, thereby improving outcomes and justifying the financial incentives tied to OBC. AI tools can provide significant value for healthcare use in high-stakes, diagnostic and treatment decisions (oncology, cardiology, and critical care) where errors have large consequences on patient outcomes. In those settings, AI can assist by analyzing imaging, lab, and genomic data to uncover patterns that may not be immediately apparent to clinicians. However, these tools should not function autonomously, and input from clinicians is critical to validate AI outputs, specifically for issues where mortality or morbidity is high. In contrast, for lower-risk or routine care of common colds or minor dermatologic conditions, AI may be useful as a time-saving tool that does not require the same depth of clinician oversight. 

Plan of Action

These actionable recommendations aim to help federal agencies and health systems embed clinician involvement, representative data, and continuous oversight into the lifecycle of healthcare AI.

Recommendation 1. Federal Agencies Should Require Clinician Involvement in the Development and Testing of AI Tools used in Clinical Settings.

Federal agencies should require clinician involvement  in all aspects of the development and testing of AI healthcare tools. This mechanism could be enforced through a combination of agency guidance and tying funding eligibility to specific roles and checkpoints for clinicians. Specifically, agencies like the Office of the National Coordinator for Health Information Technology (ONC), the Food and Drug Administration (FDA) can issue guidance mandating clinician participation, and can tie AI tool development funding to the inclusion of clinicians in the design and testing phases. Guidance can mandate clinician involvement at critical stages for: (1) defining clinical tasks and user interface requirements (2) validating interpretability and performance for diverse populations (3) piloting in real workflows and (4) reviewing for safety and bias metrics. This would ensure AI tools used in clinical settings are human-centered, effective, and safe.

Key stakeholders who may wish to be consulted in this process include offices underneath the Department of Health and Human Services (HHS) such as the Office of the National Coordinator for Health Information Technology (ONC), the Food and Drug Administration (FDA), and the Agency for Healthcare Research and Quality (AHRQ). ONC and FDA should work to issue guidance encouraging clinician engagement during the premarket review. This would  allow experts thorough review of scientific data and real-world evidence to  ensure that the tools used are human-centered and have the ability to improve the quality of care. 

Recommendation 2. Incentivize Clinician Involvement Through Outcomes-Based Contracting

Federal agencies such as the Department of Health and Human Services (HHS), the Centers for Medicare and Medicaid Services (CMS), and the Agency for Healthcare Research and Quality (AHRQ) should incorporate outcomes-based contracting requirements into AI-related healthcare grant programs. Funding should be awarded to grantees who: (1) include clinicians as part of their AI design teams or advisory boards, (2) develop formal clinician feedback loops, and (3) demonstrate measurable outcomes such as improved diagnostic accuracy or workflow efficiency. These outcomes are essential when thinking about clinician engagement and how it will improve the usability of AI tools and their clinical impact.

Key stakeholders  include HHS, CMS, ONC, AHRQ, as well as clinicians, AI developers, and potentially patient advocacy organizations. These requirements should prioritize funding for entities that demonstrate clear clinician involvement at key development and testing phases, with metrics tied to improvements in patient outcomes and clinician satisfaction. This model would align with CMS’s ongoing efforts to foster a patient-centered, data-driven healthcare ecosystem that uses tools designed with clinical needs in mind, as recently emphasized during the  health tech ecosystem initiative meeting. Embedding outcomes-based contracting into the federal grant process will link funding to clinical effectiveness and incentivize developers to work alongside clinicians through the lifecycle of their AI tools. 

Recommendation 3. Develop Standards for AI Interoperability

ONC should develop interoperability guidelines that enable AI systems to share information across platforms while simultaneously protecting patient privacy. As the challenge of healthcare data fragmentation has become evident, AI tools must seamlessly integrate with diverse electronic healthcare records (EHRs) and other clinical platforms to ensure their effectiveness. 

An example of successful interoperability frameworks can be seen through the Trusted Exchange Framework and Common Agreement (TEFCA), which aims to establish a nationwide interoperability infrastructure for the exchange of health information. Using a model such as this one can establish seamless integration across different healthcare settings and EHR systems, ultimately promoting efficient and accurate patient care. This effort would involve the consultation of clinicians, electronic health record vendors, patients, and AI developers. These guidelines will help ensure that AI tools can be used safely and effectively across clinical settings. 

Recommendation 4. Establish Post-Market Surveillance and Evaluation of Healthcare AI Tools to Enhance Performance and Reliability

Federal agencies such as FDA and AHRQ should establish frameworks that can be used for the continuous monitoring of AI tools in clinical settings. These frameworks for privacy-protected data collection should incorporate feedback loops that allow real-world data from clinicians and patients to inform ongoing updates and improvements to the systems. This ensures the effectiveness and accuracy of the tools over time. Special emphasis should be placed on bias audits that can detect disparities in the system’s performance across different patient groups. Bias audits will be key to identifying whether AI tools inadvertently present disadvantages to specific populations based on the data they were trained on. Agencies should require that these audits be conducted routinely as part of the post-market surveillance process. The surveillance data collected can be used for future development cycles where AI tools are updated or re-trained to address shortcomings. 

Evaluation methods should track clinician satisfaction, error rates, diagnostic accuracy, and reportability of failures. During this ongoing evaluation process, incorporating routine bias audits into post-market surveillance will ensure that these tools remain equitable and effective over time. Funding for this initiative could potentially be provided through a zero-cost, fee-based structure or federally appropriated grants. Key stakeholders in this process could include clinicians, AI developers, and patients, all of whom would be responsible for providing oversight. 

Conclusion

Integrating AI tools into healthcare has an immense amount of potential to improve patient outcomes, streamline clinical workflows, and reduce errors and bias. However, without clinician involvement in the development and testing of these tools, we risk continual system degradation and patient harm. Requiring that all AI systems used for healthcare are human-centered through clinician input will ensure these systems are effective, safe, and align with real-world clinical needs. This human-centered approach is critical not only for usability, but also for building trust among clinicians and patients, fostering the adoption of AI tools, and ensuring they function properly in real-world clinical settings. 

In addition, aligning funding and clinical outcomes through outcomes-based contracting adds a mechanism that forces accountability and ensures lasting impact. When developers are rewarded for improving safety, usability, and equity through clinician involvement, we can transform AI tools into safer care. There is an urgency to address these challenges due to the rapid adoption of AI tools which will require safeguards and ethical oversight. By embedding these recommendations into funding opportunities, we will move America toward building trustworthy healthcare systems that enhance patient safety, clinician expertise, and are adaptive while maximizing AI’s potential for improving patient outcomes. Clinician engagement, both in the development process and through ongoing feedback loops will be the foundation of this transformation. With the right structures in place, we can ensure AI becomes a trusted partner in healthcare and not a risk to it.

This memo produced as part of Strengthening Pathways to Disease Prevention and Improved Health Outcomes.

Despite spending over 17% of GDP on health care, Americans live shorter and less healthy lives than their peers in other high-income countries. Rising chronic disease and mental health challenges as well as clinician burnout expose the limits of a system built to treat illness rather than create health. Addressing chronic disease while controlling healthcare costs is a bipartisan goal, the question now is how to achieve this shared goal? A policy window is opening now as Congress debates health care again – and in our view, it’s time for a “whole health” upgrade.

Whole Health is a proven, evidence-based framework that integrates medical care, behavioral health, public health, and community support so that people can live healthier, longer, and more meaningful lives. Pioneered by the Veterans Health Administration, Whole Health offers a redesign to U.S. health and social systems: it organizes how health is created and supported across sectors, shifting power and responsibility from institutions to people and communities. It begins with what matters most to people–their purpose, aspirations, and connections–and aligns prevention, clinical care, and social supports accordingly. Treating Whole Health as a shared public priority would help ensure that every community has the conditions to thrive. 

Figure 1. Whole Health from Framework to Practice.

Whole Health is a systems framework that aligns health care, public health, and community & social systems to create the conditions for well-being. Guided by five principles–People-Centered, Upstream-Focused, Equitable & Accountable, Comprehensive & Holistic, and Team Well-Being–the framework is put into practice through models such as the VHA Whole Health System, which centers on Meaning and Purpose and operates through three core functions: Empower, Equip, and Care.

(Adapted from the National Academies of Sciences, Engineering, and Medicine, Achieving Whole Health, 2023, and the Department of Veterans Affairs, Office of Patient Centered Care and Cultural Transformation, 2019).

Challenge and Opportunity

The U.S. health system spends over $4 trillion annually, more per capita than any other nation, yet underperforms on life expectancy, infant mortality, and chronic disease management. The prevailing fee-for-service model fragments care across medical, behavioral, and social domains, rewarding treatment over prevention. This fragmentation drives costs upward, fuels clinician burnout, and leaves many communities without coordinated support.

At this inflection point in our declining health outcomes and growing public awareness of the failures of our health system, federal prevention and public health programs are under review, governors are seeking cost-effective chronic disease solutions, and the National Academies is advocating for new healthcare models. Additionally, public demand for evidence-based well-being is growing, with 65% of Americans prioritizing mental and social health. There is clear demand for transformation in our health care system to deliver results in a much more efficient and cost effective way.

Veterans Health Administration’s Whole Health System Debuted in 2011

Whole Health offers a system-wide redesign for the challenge at hand. As defined by the National Academies of Sciences, Engineering, and Medicine, Whole Health is a framework for organizing how health is created and supported across sectors. It integrates medical care, behavioral health, public health, and community resources. As shown in Figure 1, the framework connects five system principles—People-Centered, Upstream-Focused, Equitable & Accountable, Comprehensive & Holistic, and Team Well-Being–that guide implementation across health and social support systems. The nation’s largest health system, the Veterans Health Administration’s (VHA), has demonstrated this framework in clinical practice through their Whole Health System since 2011. The VHA’s Whole Health System operates through three core functions: Empower (helping individuals define purpose), Equip (providing community resources like peer support), and Clinical Care (delivering coordinated, team-based care). Together, these elements align with what matters most to people, shifting the locus of control from expert-driven systems to shared agency through partnerships. The Whole Health System at the VHA has reduced opioid use and improved chronic disease outcomes.

Successful State Examples

Beyond the VHA, states have also demonstrated the possibility and benefits of Whole Health models. North Carolina’s Healthy Opportunities Pilots extended Medicaid coverage to housing, food, and transportation, showing fewer emergency visits and savings of about $85 per member per month. Vermont’s Blueprint for Health links primary care practices with community health teams and social services, reducing expenditures by about $480 per person annually and boosting preventive screenings. Finally, the Program of All-Inclusive Care for the Elderly (PACE), currently being implemented in 33 states, utilizes both Medicare and Medicaid funding to coordinate medical and social care for older adults with complex medical needs. While improvements can be made to national program-wide evaluation, states like Kansas have done evaluations that have found that the PACE program is less expensive than nursing homes per beneficiary and that nursing home admissions decline by 5% to 15% for beneficiaries. 

Success across each of these examples relies on three pillars: (1) integrating medical, behavioral, social, and public health resources; (2) sustainable financing that prioritizes prevention and coordination; and (3) rigorous evaluation of outcomes that matter to people and communities. While these programs are early signs of success of Whole Health models, without coordinated leadership, efforts will fragment into isolated pilots and it will be challenging to learn and evolve.

A policy window for rethinking the health care system is opening. At this national inflection point, the U.S. can work to build a unified Whole Health strategy that enables a more effective, affordable and resilient health system. 

Plan of Action

To act on this opportunity, federal and state leaders can take the following coordinated actions to embed Whole Health as a unifying framework across health, social, and wellbeing systems.

Recommendation 1. Declare Whole Health a Federal and State Priority.

Whole Health should become a unifying value across federal and state government action on health and wellbeing, embedding prevention, connection, and integration into how health and social systems are organized, financed, and delivered. Actions include:

• Federal Executive Action. The Executive Office of the President should create a Whole Health Strategic Council that brings together Veterans Affairs (VA), Health and Human Services (e.g. Centers for Disease Control and Prevention, Centers for Medicare and Medicaid (CMS), and Health Resources and Services Administration (HRSA)), Housing and Urban Development (HUD), and the U.S. Department of Agriculture (USDA) to align strategies, budgets, and programs with Whole Health principles through cross-agency guidance and joint planning. This council should also work with Governors to establish evidence-based benchmarks for Whole Health operations and evaluation (e.g., person-centered planning, peer support, team integration) and shared outcome metrics for well-being and population health.
• U.S. Congressional Action. Authorize whole health benefits, like housing assistance, nutrition counseling, transportation to appointments, peer support programs, and well-being centers as reimbursable services under Medicare, Medicaid and the Affordable Care Act health subsidies.
• State Action. Adopt Whole Health models through Medicaid managed-care contracts and through CDC and HRSA grant implementation. States should also develop support for Whole Health services in trusted local settings such as libraries, faith-based organizations, senior centers, to reach people where they live and gather.

Recommendation 2. Realign Financing and Payment to Reward Prevention and Team-Based Care.

Federal payment modalities need to shift from a fee-for-service model toward hybrid value-based models. Models such as per-member-per-month payments with quality incentives, can sustain comprehensive, team-based care while delivering outcomes that matter, like reductions in chronic disease and overall perceived wellbeing. Actions include:

• Federal Executive Action. Expand Advanced Primary Care Management (APCM) payments to include Whole Health teams, including clinicians, peer coaches, and community health workers. Ensure that this funding supports coordination, person-centered planning, and upstream prevention, such as food as medicine programs. Further, CMS can expand reimbursements to community health workers and peer support roles and standardize their scope-of-practice rules across states.
• U.S. Congressional Action. Invest in Medicare and Medicaid innovation programs, such as the CMS Innovation Center (CMMI), that reward prevention and chronic disease reduction. Additionally, expand tools for payment flexibility, through Medicaid waivers and state innovation funds, to help states adapt Whole Health models to local needs.
• State Action. Require Medicaid managed-care contracts to reimburse Whole Health services, particularly in underserved and rural areas, and encourage payers to align benefit designs and performance measures around well-being. States should also leverage their state insurance departments to guide and incentivize private health insurers to adopt Whole Health payment models.

Recommendation 3. Strengthen and Expand the Whole Health Workforce. 

Whole Health practice needs a broad team to be successful: clinicians, community health workers, peer coaches, community organizations, nutritionists, and educators. To build this workforce, governments need to modernize training, assess the workforce and workplace quality, and connect the fast-growing well-being sector with health and community systems. Actions include:

• Federal Executive Action. Through VA and HRSA establish Whole Health Workforce Centers of Excellence to develop national curricula, set standards, and disseminate evidence on effective Whole Health team-building. Further, CMS should track workforce outcomes such as retention, burnout, and team integration, and evaluate the benefits for health professionals working in Whole Health systems versus traditional health systems.
• U.S. Congressional Action. Expand CMS Graduate Medical Education Funds and HRSA workforce programs to support Whole Health training, certifications, and placements across clinical and community settings.
• State Action. As a part of initiatives to grow the health workforce, state governments should expand the definition of a “health professional” to include Whole Health practitioners. Further, states can leverage their role as a licensure for professionals by creating a “whole health” licensing process that recognizes professionals that meet evidence-based standards for Whole Health.

Recommendation 4. Build a National Learning and Research Infrastructure.

Whole Health programs across the country are proving effective, but lessons remain siloed. A coordinated national system should link evidence, evaluation, and implementation so that successful models can scale quickly and sustainably.

• Federal Executive Action. Direct the Agency for Healthcare Research and Quality, National Institutes of Health, and partner agencies (VA, HUD, USDA) to run pragmatic trials and cost-effectiveness studies of Whole Health interventions that measure well-being across clinical, biomedical, behavioral, and social domains. The federal government should also embed Whole Health frameworks into government-wide research agendas to sustain a culture of evidence-based improvement.
• U.S. Congressional Action. Charter a quasi-governmental entity, modeled on Patient-Centered Outcomes Research Institute (PCORI), to coordinate Whole Health demonstration sites and research. This new entity should partner with CMMI, HRSA and VA to test Whole Health payment and delivery models under real-world conditions. This entity should also establish an interagency team as well as state network to address payment, regulatory, and privacy barriers identified by sites and pilots.
• State Action. Partner with federal agencies through innovation waivers (e.g. 1115 waivers and 1332 waivers) and learning collaboratives to test Whole Health models and share data across state systems and with the federal government.

Conclusion

The United States spends more on health care than any other nation yet delivers poorer outcomes. Whole Health offers a proven path to reverse this trend, reframing care around prevention, purpose, and integration across health and social systems. Embedding Whole Health as the operating system for America’s health requires three shifts: (1) redefining the purpose from treating disease to optimizing health and well-being; (2) restructuring care to empower, equip, and treat through team-based and community-linked approaches; and (3) rebalancing control from expert-driven systems to partnerships guided by what matters most to people and communities. Federal and state leaders have the opportunity to turn scattered Whole Health pilots to a coordinated national strategy. The cost of inaction is continued fragmentation; the reward of action is a healthier and more resilient nation.

This memo produced as part of Strengthening Pathways to Disease Prevention and Improved Health Outcomes.

Moving postsecondary education data collection to the states is the best way to ensure that the U.S. Department of Education can meet its legislative mandates in an era of constrained federal resources. Students, families, policymakers, and businesses need this data to make decisions about investments in education, but cuts to the federal government make it difficult to collect. The Commissioner of the National Center for Education Statistics should use their authority to establish state cooperative groups to  collect and submit data from the postsecondary institutions in each state to the federal government, like the way that K12 schools report to the U.S. Department of Education (ED). With funding from the State Longitudinal Data System grant program and quality measures like the Common Education Data Standards, this new data reporting model will give more power to states, improve trust in education data, and make it easier for everyone to use the data.

Challenge and Opportunity

The Integrated Postsecondary Education Data System (IPEDS), was hit hard by staffing and contract cuts at the U.S. Department of Education in early 2025. Without the staff to collect and clean the data, or the contractors to run the websites and reports, this is the first time in its decades-long history that IPEDS may not be available to the public next year. IPEDS is a vast data collection, including information on grants and scholarships, tuition prices, graduation rates, and staffing levels. This has serious implications for students and their families choosing colleges, as well as for policymakers who want to ensure that these colleges graduate students on time, for businesses who want to find trained workers, and for everyone who cares about educating tomorrow’s citizens . Not to mention that these data are required by law under the Higher Education Act and the Civil Rights Act of 1964, among others. 

Moving IPEDS data collection to the states is the best way to ensure that the data continue to be collected and released. States already play a large role in collecting data on elementary and secondary education, a model that could work for postsecondary data like IPEDS. 

Why do we collect K12 data through states but not postsecondary data? K12 systems are substantially different from postsecondary data due to federal legislation. No Child Left Behind catalyzed the expansion of K12 data infrastructure, requiring regular reporting on student test scores, disaggregated achievement data for student groups, and information about teacher qualifications. Though the accountability measures attached to these data were controversial, the reporting processes they catalyzed vastly surpassed those in the postsecondary data system, which was built piecemeal over decades.

In K12 data systems, local education agencies report data to state education agencies who report to the National Center for Education Statistics (NCES). Reviews at each step in this process ensures that data are high quality and made available quickly for analysis. In postsecondary data, thousands of institutions individually report to NCES, which takes months to review and release the data for the whole country. Some institutions do report to a state coordinator, like Maryland which has one reporter for all public postsecondary institutions and one for all privates. The role of state coordinators varies widely across states. Using the state reporting model is an opportunity to further streamline this process.

Reporting postsecondary data at the state level has another benefit: it gives states control over future student-level data reporting. That is because states, in addition to fulfilling reporting requirements, also collect student-level data from K12 systems that can be linked to students’ postsecondary and workforce outcomes over time. These statewide longitudinal systems (SLDS) were supported through a federal grant program that began in 2006, and many of the measures collected are required for federal K12 reporting. Some SLDSs contain postsecondary measures like tuition and graduation rates, which are also collected by IPEDS. Though IPEDS does not require student-level data, advocates have been pushing for such data for several years. A student unit record system was proposed in the College Transparency Act. Moving IPEDS data collection to the states will  help states develop the systems necessary to implement future student-level data collection in postsecondary education if this or similar legislation passes Congress.

Plan of Action

The NCES Commissioner should establish state-level groups to collect and submit IPEDS data. Instead of receiving thousands of individual reports from postsecondary institutions, NCES would receive 59, one from each state plus Washington D.C. and the territories that already report to IPEDS. For states that need support to manage this reporting process, ED could provide funding through an existing grant program. The IPEDS data definitions and reporting requirements would not change, but they could be improved through integration into other data standards.

This plan has several advantageous outcomes. First, IES would be able to meet its data collection and reporting requirements despite limited staff and funding. This increases efficiency and saves taxpayer dollars. Second, states would have access to their data more quickly, thus minimizing pressure on IES to release data on shorter timelines. This allows data users to work with more current information and give states the power to conduct their own analysis.

Step 1. The U.S. Department of Education can use its authority to establish state cooperatives to move IPEDS data collection to the states

Under 20 U.S.C. §9547, the NCES Commissioner has the authority to set up cooperatives to produce education statistics. These cooperatives could serve as the governing body and fiscal agent for collecting and submitting data from each state to the federal government. In states that already have a coordinator to submit IPEDS data, this cooperative group builds on existing processes for collecting, reviewing, and submitting data, including existing IPEDS state coordinators. The cooperatives should also involve state higher education executive officers, representatives from public, non-profit, and private postsecondary institutions, and experts in data systems and institutional research.

NCES should publish a charter that states can adopt as they organize their cooperatives. Multi-state education data groups, like the Multi-State Data Collaborative, have developed charters that could be used as a starting point. The sample charter should encourage the development of federated data systems, one model that has been successful in K12 data collection. Federated data systems, as opposed to centralized ones, operate on agreements to link and share data upon request, after which the linked data are destroyed. This model offers stronger protections for data privacy and can be established quickly.

Step 2. States should commit to financial support to support data submissions

States will also need financial support to develop or expand data storage systems, pay staff for quality reviews, and support data submissions. Some of this infrastructure already exists  through funding from the IES SLDS grant program. Future grant awards could be used to fund the expansion of these systems to include IPEDS data collection and submission by setting priorities in the grant selection process. 

In addition, NCES could contract with a technical assistance provider to support state infrastructure development. Something like the data academy offered by the State Higher Education Executive Officers Association (SHEEO) or the institutional research training offered by the Association for Institutional Research (AIR) would be useful to states that need personalized assistance.

Step 3. States should continue to use the data definitions and guidance developed by NCES

To ensure that the data retains the same high-quality standard of federal IPEDS collection, states should continue to use the data definitions and guidance developed by NCES. Further integrating these definitions with the Common Education Data System (CEDS), ensures that states understand and have access to these definitions. CEDS, the voluntary national standard for reporting K12 data, already includes some postsecondary data elements. Incorporating all IPEDS data definitions into CEDS will  streamline data standards across K12 and postsecondary. CEDS also has recommendations for building out data infrastructure, like data stores (repositories for multiple databases and file types), helpful for states who need to expand theirs for this effort.

Conclusion

Moving IPEDS data collection to the states is the best way to ensure that NCES meets its legislative mandates in an era of constrained federal resources. This new collection method has other benefits as well. A more decentralized data collection will give more power to states to represent their unique institutions and contexts. By serving as stewards of this data, states will have better access to it, allowing for quicker reporting and analysis. With more access to and control over the data, trust and usage of the data will improve.

Supporting the world’s youngest children is one of the smartest, most effective investments in U.S. strength and soft power. The cancellation of 83 percent of foreign assistance programs in early 2025, coupled with the dismantling of the U.S. Agency for International Development (USAID), not only caused unnecessary suffering of millions of young children in low-income countries, but also harmed U.S. security, economic competitiveness, and global leadership. As Congress crafts legislation to administer foreign assistance under a new America First focused State Department, it should recognize that renewed attention and support for young children in low-income countries will help meet stated U.S. foreign assistance priorities to make America safer, stronger, and more prosperous. Specifically, Congress should: (1) prioritize funding for programs that promote early childhood development; (2) bolster State Department staffing to administer resources efficiently; and (3) strengthen accountability and transparency of funding.

Challenge and Opportunity 

Supporting children’s development through health, nutrition, education, and protection programs helps the U.S. achieve its national security and economic interests, including the Administration’s priorities to make America “safer, stronger, and more prosperous.” Investing in global education, for example, generates economic growth overseas, creating trade opportunities and markets for the U.S. In fact, 11 of America’s top 15 trading partners once received foreign aid. Healthy, educated populations are associated with less conflict and extremism, which reduces pressures on migration. Curbing the spread of infectious diseases like HIV/AIDS and Ebola makes Americans safer from disease both abroad and at home. As a diplomacy tool, providing support for early childhood development, which is a priority in many partner countries, increases U.S. goodwill and influence in these countries and contributes to its geopolitical competitiveness.

Helping young children thrive in low-income countries is a high-return investment in stable economies, skilled workforces, and a stronger America on the world stage. In a July 2025 press release, the State Department recognized how investing in children and families globally contributes to America’s national development and priorities:

Supporting children and families strengthens the foundation of any society. Investing in their protection and well-being is a proven strategy for ensuring American security, solidifying American strength, and increasing American prosperity. When children and families around the world thrive, nations flourish.

The first five years of a child’s life is a period of unprecedented brain development. Investments in early childhood programs – including parent coaching, child care, and quality preschool – yield large and long-term benefits for individuals and society-at-large, up to a 13% return on investment, particularly when these interventions are targeted to the most vulnerable and disadvantaged populations. Despite the promise of early childhood interventions, 43% of children under five in low- and middle-income countries are at elevated risk of poor development, leaving them vulnerable to the long-term negative impacts of adversity, such as poverty, malnutrition, illness, and exposure to violence. The costs of inaction are high; countries that underinvest in young children are more likely to have less healthy and educated populations and to struggle with higher unemployment and lower GDPs.

Informed by this powerful evidence, the bipartisan Global Child Thrive Act of 2020 required U.S. Government agencies to develop and implement policies to advance early childhood development – the cognitive, physical, social, and emotional development of children up to age 8 – in partner countries. This legislation supported early childhood development through nutrition, education, health, and water, sanitation, and hygiene interventions. It mandated the U.S. Government Special Advisor for Children in Adversity to lead a coordinated, comprehensive, and effective U.S. government response through international assistance. The bipartisan READ Act complements the Thrive Act by requiring the U.S. to implement an international strategy for basic education, starting with early childhood care and education.

Three examples of USAID-funded early childhood programs terminated in 2025 illustrate how investments in young children not only achieve multiple development and humanitarian goals, but also address U.S. priorities to make America safer, stronger, and more prosperous:

• Cambodia. Southeast Asia is of strategic importance to U.S. security given risks of China’s political and military influence in the region. The Integrated Early Childhood Development activity ($20 million) helped young children (ages 0-2) and their caregivers through improved nutrition, responsive caregiving, agricultural practices, better water, sanitation, and hygiene, and support for children with developmental delays or disabilities. Within a week of cancellation, China filled the USAID vacuum and gained a soft-power advantage by announcing funding for a program to achieve almost identical goals. 
• Honduras. Foreign assistance mitigates poverty, instability, and climate shocks that push people to migrate from Central America (and other regions) to the U.S. The Early Childhood Education for Youth Employability activity ($8 million) aimed to improve access to quality early learning for more than 100,000 young children (ages 3-6) while improving the employability and economic security for 25,000 young mothers and fathers, a two-generation approach to address drivers of irregular migration.
• Ethiopia. The U.S. has a long-standing partnership with Ethiopia to increase stability and mitigate violent extremism in the Horn of Africa. Fostering peace and promoting security, in turn, expands markets for American businesses in the region. Through a public-private partnership with the LEGO Foundation, the Childhood Development Activity ($46 million) reached 100,000 children (ages 3-6+) in the first two years of the program with opportunities for play-based learning and psycho-social support for coping with negative effects of conflict and drought.

Drastic funding cuts have jeopardized the wellbeing of vulnerable children worldwide and the “soft power” the U.S. has built through relationships with more than 175 partner countries. In January 2025, the Trump Administration froze all foreign assistance and began to dismantle the USAID, the lead coordinating agency for children’s programs under the Global Child Thrive Act and READ Act. By March 2025, sweeping cuts ended most USAID programs focused on children’s education, health, water and sanitation, nutrition, infectious diseases (malaria, tuberculosis, neglected tropical diseases, and HIV/AIDS), and support for orphans and vulnerable children. In total, the U.S. eliminated around $4 billion in foreign assistance intended for children in the world’s poorest countries. As a result, an estimated 378,000 children have died from preventable illnesses, such as HIV, malaria, and malnutrition.

In July 2025, Congress voted to approve the Administration’s rescission package, which retracts nearly $8 billion of FY25 foreign assistance funding that was appropriated, but not yet spent. This includes support for 6.6 million orphans and vulnerable children (OVC) and $142 million in core funding to UNICEF, the UN agency which helps families in emergencies and vulnerable situations globally. An additional $5 billion of foreign assistance funding expired at the end of the fiscal year while being withheld through a pocket rescission.

As Congress works to reauthorize the State Department, and what remains of USAID, it should see that helping young children globally supports both American values and strategic interests. 

Recent U.S. spending on international children’s programs accounted for only 0.09% of the total federal budget and only around 10% of foreign assistance expenditure. If Congress does not act, this small, but impactful funding is at risk of disappearing from the FY 2026 budget.

Plan of Action

For decades, the U.S. has been a leader in international development and humanitarian assistance. Helping the world’s youngest children reach their potential is one of the smartest, most effective investments the U.S. government can make. Congress needs to put in place funding, staffing, and accountability mechanisms that will not only support the successful implementation of the Global Child Thrive Act, but also meet U.S. foreign policy priorities.

Recommendation 1. Prioritize funding for early childhood development through the Department of State

In the FY26 budget currently under discussion, Congress has the responsibility to fund global child health, education, and nutrition programs under the authority of the State Department. These child-focused programs align with America’s diplomatic and economic interests and are vital to young children’s survival and well-being globally. 

To promote early childhood development specifically, the Global Child Thrive Act should be reauthorized under the auspices of the State Department. While there is bipartisan support in the House Foreign Affairs Committee to extend authorization of the Global Child Thrive Act through 2027, the current bill had not made it to the House floor as of October 2025, and the Senate bill was delayed by a federal government shutdown.

Congress should pass legislation to appropriate $1.5 billion in FY26 funding for life-saving and life-changing programs for young children, including:

• The Vulnerable Children’s Account which funds multi-sectoral, evidence-based programs that support the objectives of the Global Child Thrive Act and the Advancing Protection and Care for Children in Adversity Strategy ($50 million).
• PEPFAR 10% Orphans and Vulnerable Children Set Aside which protects and promotes the holistic health and development of children affected by HIV/AIDS ($710 million).
• UNICEF core funding, given the agency’s track record in advancing early childhood development programs in development and humanitarian settings ($300 million).
• Commitments to government-philanthropy partnerships with pooled funds that prioritize the early years including the Global Partnership for Education, Education Cannot Wait, the Early Learning Partnership, and the Global Financing Facility ($430 million).

Funding should be written into legislation so that it is protected from future cuts.

Recommendation 2. Adequately staff the State Department to coordinate early childhood programs

The State Department needs to rebuild expertise on global child development that was lost when USAID collapsed. As a first step, current officials need to be briefed on relevant legislation including the Global Child Thrive Act and the READ Act. In response to the reduced capacity, Congress should fund a talent pipeline in order to attract a cadre of professionals within the State Department in Washington, DC and at U.S. Embassies who can focus on early years issues across sectors and funding streams. Foreign nationals who have a deep understanding of local contexts should be considered for these roles.

In the context of scarce resources, coordination and collaboration is more important than ever. The critical role of the USG Special Advisor for Children in Adversity should be formally transferred to the State Department to provide technical leadership and implementation support for children’s issues. Within the reorganized State Department, the Special Advisor should sit in the office of the Under Secretary for Foreign Assistance, Humanitarian Affairs and Religious Freedom (F), where s/he can serve as a leading voice for children and foster inter-agency coordination across the Departments of Agriculture and Labor, the Millennium Challenge Corporation, etc.

Congress also should seek clarification on how the new Special Envoy for Best Future Generations will contribute specifically to early childhood development. The State Department appointed the Special Envoy in June 2025 as a liaison for initiatives impacting the well-being of children under age 18 in the U.S. and globally. In the past three months, the Special Envoy has met with U.S. government officials at the White House and State Department, representatives from 14 countries at the U.N., and non-governmental organizations to discuss coordinated action on children’s issues, such as quality education, nutritious school meals, and ending child labor and trafficking.

Recommendation 3. Increase accountability and transparency for funds allocated for young children

Increased oversight over funds can improve efficiency, prevent delays, and reduce risks of funds expiring before they reach intended families. The required reporting on FY24 programs is overdue and should be submitted to Congress by the end of December 2025.

Going forward, Congress should require the State Department to report regularly and testify on how money is being spent on young children. Reporting should include evidence-based measures of Return on Investment (ROI) to help demonstrate the impact of early childhood programs. In addition, the Office of Foreign Assistance should issue a yearly report to Congress and to the public which tracks annual inter-agency progress toward implementing the Global Child Thrive Act using a set of indicators, including the approved pre-primary indicator and other relevant and feasible indicators across age groups, programs, and sectors.

Conclusion

Investing in young children’s growth and learning around the world strengthens economies, builds goodwill, and secures America’s position as a trusted global leader. To help reach U.S. foreign policy priorities, Congress must increase funding, staffing and accountability of the State Department’s efforts to promote early childhood development, while also strengthening multi-agency coordination and accountability for achieving results. The Global Child Thrive Act provides the legislative mandate and a technical roadmap for the U.S. Government to follow.

State education agencies (SEAs) are poised to deploy federal funding for artificial intelligence tools in K–12 schools. Yet, the nation risks repeating familiar implementation failures that have limited educational technology for more than a decade. The July 2025 Dear Colleague Letter from the U.S. Department of Education (ED) establishes a clear foundation for responsible artificial intelligence (AI) use, and the next step is ensuring these investments translate into measurable learning gains. The challenge is not defining innovation—it is implementing it effectively. To strengthen federal–state alignment, upcoming AI initiatives should include three practical measures: readiness assessments before fund distribution, outcomes-based contracting tied to student progress, and tiered implementation support reflecting district capacity. Embedding these standards within federal guidance—while allowing states bounded flexibility to adapt—will protect taxpayer investments, support educator success, and ensure AI tools deliver meaningful, scalable impact for all students.

Challenge and Opportunity

For more than a decade, education technology investments have failed to deliver meaningful results—not because of technological limitations, but because of poor implementation. Despite billions of dollars in federal and local spending on devices, software, and networks, student outcomes have shown only minimal improvement. In 2020 alone, K–12 districts spent over $35 billion on hardware, software, curriculum resources, and connectivity—a 25 percent increase from 2019, driven largely by pandemic-related remote learning needs. While these emergency investments were critical to maintaining access, they also set the stage for continued growth in educational technology spending in subsequent years. 

Districts that invest in professional development, technical assistance, and thoughtful integration planning consistently see stronger results, while those that approach technology as a one-time purchase do not. As the University of Washington notes, “strategic implementation can often be the difference between programs that fail and programs that create sustainable change.” Yet despite billions spent on educational technology over the past decade, student outcomes have remained largely unchanged—a reflection of systems investing in tools without building the capacity to understand their value, integrate them effectively, and use them to enhance learning. The result is telling: an estimated 65 percent of education software licenses go unused, and as Sarah Johnson pointed out in an EdWeek article, “edtech products are used by 5% of students at the dosage required to get an impact”.

Evaluation practices compound the problem. Too often, federal agencies measure adoption rates instead of student learning, leaving educators confused and taxpayers with little evidence of impact. As the CEO of the EdTech Evidence Exchange put it, poorly implemented programs “waste teacher time and energy and rob students of learning opportunities.” By tracking usage without outcomes, we perpetuate cycles of ineffective adoption, where the same mistakes resurface with each new wave of innovation.

Implementation Capacity is Foundational

A clear solution entails making implementation capacity the foundation of federal AI education funding initiatives. Other countries show the power of this approach. Singapore, Estonia, and Finland all require systematic teacher preparation, infrastructure equity, and outcome tracking before deploying new technologies, recognizing, as a Swedish edtech implementation study found, that access is necessary but not sufficient to achieve sustained use. These nations treat implementation preparation as essential infrastructure, not an optional add-on, and as a result, they achieve far better outcomes than market-driven, fragmented adoption models.

The United States can do the same. With only half of states currently offering AI literacy guidance, federal leadership can set guardrails while leaving states free to tailor solutions locally. Implementation-first policies would allow federal agencies to automate much of program evaluation by linking implementation data with existing student outcome measures, reducing administration burden and ensuring taxpayer investments translate into sustained learning improvements. 

The benefits would be transformational:

• Educational opportunity. Strong implementation support can help close digital skill gaps and reduce achievement disparities. Rural districts could gain greater access to technical assistance networks, students with disabilities could benefit from AI tools designed with accessibility at their core, and all students could build the AI literacy necessary to participate in civic and economic life. Recent research suggests that strategic implementation of AI in education holds particular promise for underserved and geographically isolated communities. 
• Workforce development. Educators could be equipped to use AI responsibly, expanding coherent career pathways that connect classroom expertise to emerging roles in technology coaching, implementation strategy, and AI education leadership. Students graduating from systematically implemented AI programs would enter the workforce ready for AI-driven jobs, reducing skills gaps and strengthening U.S. competitiveness against global rivals.

In short, implementation is not a secondary concern; it is the primary determinant of whether AI in education strengthens learning or repeats the costly failures of past ed-tech investments. Embedding implementation capacity reviews before large-scale rollout—focused on educator preparation, infrastructure adequacy, and support systems—would help districts identify strengths and gaps early. Paired with outcomes-based vendor contracts and tiered implementation support that reflects district capacity, this approach would protect taxpayer dollars while positioning the United States as a global leader in responsible AI integration.      

Plan of Action

AI education funding must shift to being both tool-focused and outcome-focused, reducing repeated implementation failures and ensuring that states and districts can successfully integrate AI tools in ways that strengthen teaching and learning. Federal guidance has made progress in identifying priority use cases for AI in education. With stronger alignment to state and local implementation capacity, investments can mitigate cycles of underutilized tools and wasted resources.

A hybrid approach is needed: federal agencies set clear expectations and provide resources for implementation, while states adapt and execute strategies tailored to local contexts. This model allows for consistency and accountability at the national level, while respecting state leadership.

Recommendation 1. Establish AI Education Implementation Standards Through Federal–State Partnership

To safeguard public investments and accelerate effective adoption, the Department of Education, working in partnership with state education agencies, should establish clear implementation standards that ensure readiness, capacity, and measurable outcomes. 

• Implementation readiness benchmarks. Federal AI education funds should be distributed with expectations that recipients demonstrate the enabling systems necessary for effective implementation—including educator preparation, technical infrastructure, professional learning networks, and data governance protocols. ED should provide model benchmarks while allowing states to tailor them to local contexts.
• Dedicated implementation support. Funding streams should ensure AI education investments include not only tool procurement but also consistent, evidence-based professional development, technical assistance, and integration planning. Because these elements are often vendor-driven and uneven across states, embedding them in policy guidance helps SEAs and local education agencies (LEAs) build sustainable capacity and protect against ineffective or commodified approaches—ensuring schools have the human and organizational capacity to use AI responsibly and effectively.
• Joint oversight and accountability. ED and SEAs should collaborate to monitor and publicly share progress on AI education implementation and student outcomes. Metrics could be tied to observable indicators, such as completion of AI-focused professional development, integration of AI tools into instruction, and adherence to ethical and data governance standards. Transparent reporting builds public trust, highlights effective practices, and supports continuous improvement, while recognizing that measures of quality will evolve with new research and local contexts.

Recommendation 2. Develop a National AI Education Implementation Infrastructure

The U.S. Department of Education, in coordination with state agencies, should encourage a national infrastructure that helps and empowers states to build capacity, share promising practices, and align with national economic priorities.

• Regional implementation hubs. ED should partner with states to create regional AI education implementation centers that provide technical assistance, professional development, and peer learning networks. States would have flexibility to shape programming to their context while benefiting from shared expertise and federal support.
• Research and evaluation. ED, in coordination with the National Science Foundation (NSF), should conduct systematic research on AI education implementation effectiveness and share annual findings with states to inform evidence-based decision-making.
• Workforce alignment. Federal and state education agencies should continue to coordinate AI education implementation with existing workforce development initiatives (Department of Labor) and economic development programs (Department of Commerce) to ensure AI skills align with long-term economic and innovation priorities.

Recommendation 3. Adopt Outcomes Based Contracting Standards for AI Education Procurement

The U.S. Department of Education should establish outcomes based contracting (OBC) as a preferred procurement model for federally supported AI education initiatives. This approach ties vendor payment directly to demonstrated student success, with at least 40% of contract value contingent on achieving agreed-upon outcomes, ensuring federal investments deliver measurable results rather than unused tools.

• Performance-based payment structures. ED should support contracts that include a base payment for implementation support and contingent payments earned only as students achieve defined outcomes. Payment should be based on individual student achievement rather than aggregate measures, ensuring every learner benefits while protecting districts from paying full price for ineffective tools.
• Clear outcomes and mutual accountability:. Federal guidance should encourage contracts that specify student populations served, measurable success metrics tied to achievement and growth, and minimum service requirements for both districts and vendors (including educator professional learning, implementation support, and data sharing protocols).
• Vendor transparency and reporting. AI education vendors participating in federally supported programs should provide real-time implementation data, document effectiveness across participating sites, and report outcomes disaggregated by student subgroups to identify and address equity gaps.
• Continuous improvement over termination. Rather than automatic contract cancellation when challenges arise, ED should establish systems that prioritize joint problem-solving, technical assistance, and data-driven adjustments before considering more severe measures.

Recommendation 4. Pilot Before Scaling

To ensure responsible, scalable, and effective integration of AI in education, ED and SEAs should prioritize pilot testing before statewide adoption while building enabling conditions for long-term success.

• Pilot-to-scale strategy. Federal and state agencies could jointly identify pilot districts representing diverse contexts (rural, urban, and suburban) to test AI implementation models before large-scale rollout. Lessons learned would inform future funding decisions, minimize risk, and increase effectiveness for states and districts.
• Enabling conditions for sustainability. States could build ongoing professional learning systems, technical support networks, and student data protections to ensure tools are used effectively over time.
• Continuous improvement loop. ED could coordinate with states to develop feedback systems that translate implementation data into actionable improvements for policy, procurement, and instruction, ensuring educators, leaders, and students all benefit.

Recommendation 5. Build a National AI Education Research & Development Network

To promote evidence-based practice, federal and state agencies should co-develop a coordinated research and development infrastructure that connects implementation data, policy learning to practice, and global collaboration.

• Implementation research partnerships. Federal agencies (ED, NSF) should partner with states and research institutions to fund systematic studies on effective AI education implementation, with emphasis on scalability and outcomes across diverse student populations. Rather than creating a new standalone program, this would coordinate existing ED and NSF investments while expanding state-level participation.
• Testbed site networks. States should designate urban, suburban, and rural AI education implementation labs or “sandboxes”, modeled on responsible AI testbed infrastructure, where funding supports rigorous evaluation, cross-district peer learning, and local adaptation.
• Evidence-to-policy pipeline. Federal agencies should integrate findings from these research-practice partnerships into national AI education guidance, while states embed lessons learned into local technical assistance and professional development.
• National leadership and evidence sharing. Federal and state agencies should establish mechanisms to share evidence-based approaches and emerging insights, positioning the U.S. as a leader in responsible AI education implementation. This collaboration should leverage continuous, practice-informed research, called living evidence, which integrates real-world implementation data, including responsibly shared vendor-generated insights, to inform policy, guide best practices, and support scalable improvements.

Conclusion

The Department’s guidance on AI in education marks a pivotal step toward modernizing teaching and learning nationwide. To realize the promise of AI in education, funding should support both the acquisition of tools and the strategies that ensure their effective implementation. To realize its promise, we must shift from funding tools to funding effective implementation. Too often, technologies are purchased only to sit on the shelf while educators lack the support to integrate them meaningfully. International evidence shows that countries investing in teacher preparation and infrastructure before technology deployment achieve better outcomes and sustain them.

Early research also suggests that investments in professional development, infrastructure, and systems integration substantially increase the long-term impact of educational technology. Prioritizing these supports reduces waste and ensures federal dollars deliver measurable learning gains rather than unused tools. The choice before us is clear: continue the costly cycle of underused technologies or build the nation’s first sustainable model for AI in education—one that makes every dollar count, empowers educators, and delivers transformational improvements in student outcomes.

Through partnership with the Doris Duke Foundation, FAS is working to ensure that rigorous, evidence-based ideas on the cutting edge of disease prevention and health outcomes are reaching decision makers in an effective and timely manner. To that end, we have been collaborating with the Strengthening Pathways effort, a series of national conversations held in spring 2025 to surface research questions, incentives, and overlooked opportunities for innovation with potential to prevent disease and improve outcomes of care in the United States. FAS is leveraging its skills in policy entrepreneurship, working with session organizers, to ensure that ideas surfaced in these symposia reach decision-makers to drive impact in active policy windows. 

On this World Patient Safety Day 2025, we share a set of recommendations that align with the National Quality Strategy of Centers for Medicare and Medicaid Services (CMS) goal for zero preventable harm in healthcare. Working with Patients for Patient Safety US, which co-led one of Strengthening Pathways conversations this spring with the Johns Hopkins University Armstrong Institute for Patient Safety and Quality, the issue brief below outlines a bold, modernized approach that uses Artificial Intelligence technology to empower patients and drive change. FAS continues to explore the rapidly evolving AI and healthcare nexus.

Patient safety is an often-overlooked challenge in our healthcare systems. Whether safety events are caused by medical error, missed or delayed diagnoses, deviations from standards of care, or neglect, hundreds of billions of dollars and hundreds of thousands of lives are lost each year due to patient safety lapses in our healthcare settings. But most patient safety challenges are not really captured and there are not enough tools to empower clinicians to improve. Here we present four critical proposals for improving patient safety that are worthy of attention and action.

Challenge and Opportunity

Reducing patient death and harm from medical error surfaced as a U.S. public health priority at the turn of the century with the landmark National Academy of Sciences (NAS) report, To Err is Human: Building a Safer Health System (2000).  Research shows that medical error is the 3rd largest cause of preventable death in the U.S. Analysis of Medicare claims data and electronic health records by the Department of Health and Human Services (DHHS) Office of the Inspector General (OIG) in a series of reports from 2008 to 2025 consistently finds that 25-30% of Medicare recipients experience harm events across multiple healthcare settings, from hospitals to skilled nursing facilities to long term care hospitals to rehab centers.  Research on the broader population finds similar rates for adult patients in hospitals.  The most recent study on preventable harm in ambulatory care found that 7% of patients experienced at least one adverse event, with wide variation of 1.8% to 23.6% from clinical setting to clinical setting. Improving diagnostic safety has emerged as the largest opportunity for patient harm prevention. New research estimates 795,000 patients in the U.S. annually experience death or harm due to missed, delayed or ineffectively communicated diagnoses. The annual cost to the health care system of preventable harm and its health care cascades is conservatively estimated to exceed $200 billion. This cost is ultimately borne by families and taxpayers.

In its National Quality Strategy, the Centers for Medicare and Medicaid Services (CMS) articulated an aspirational goal of zero preventable harm in healthcare.  The National Action Alliance for Patient and Workforce Safety, now managed by the Agency for Healthcare Research and Quality (AHRQ), has a goal of 50% reduction in preventable harm by 2026. These goals cannot be achieved without a bold, modernized approach that uses AI technology to empower patients and drive change. Under-reporting negative outcomes and patient harms keeps clinicians and staff from identifying and implementing solutions to improve care. In its latest analysis (July 2025), the OIG finds that fewer than 5% of medical errors are ever reported to the systems designed to gather insights from them. Hospitals failed to capture half of harm events identified via medical record review, and even among captured events, few led to investigation or safety improvements. Only 16% of events required to be reported externally to CMS or State entities were actually reported, meaning critical oversight systems are missing safety signals entirely.

Multiple research papers over the last 20 years find that patients will report things that providers do not. But there has been no simple, trusted way for patient observations to reach the right people at the right time in a way that supports learning and Improvement. Patients could be especially effective in reporting missed or delayed diagnoses, which often manifest across the continuum of care, not in one healthcare setting or a single patient visit. The advent of AI systems provides an unprecedented opportunity to address patient safety and improve patient outcomes if we can improve the data available on the frequency and nature of medical errors. Here we present four ideas for improving patient safety.

Recommendation 1. Create AI-Empowered Safety Event Reporting and Learning System With and For Patients 

The Department of Health and Human Services (HHS) can, through CMS, AHRQ or another HHS agency, develop an AI-empowered National Patient Safety Learning and Reporting System that enables anyone, including patients and families, to directly report harm events or flag safety concerns for improvement, including in real or near real time. Doing so would make sure everyone in the system has the full picture — so healthcare providers can act quickly, learn faster, and protect more patients. 

This system will:

• Develop a reporting portal to collect, triage and analyze patient reported data directly from beneficiaries to improve patient and diagnostic safety.
• Redesign and modernize Consumer Assessment of Healthcare Providers and Systems

(CAHPS) surveys to include questions that capture beneficiaries’ experiences and outcomes related to patient and diagnostic safety events.

• Redefine the Beneficiary and Family Centered Care Quality Improvement Organizations (BFCC QIO) scope of work to integrate the QIOs into the National Patient Safety Learning and Reporting System.

The learning system will:

• Use advanced triage (including AI) to distinguish high-signal events and route credible

reports directly to the care team and oversight bodies that can act on them.

• Solicit timely feedback and insights in support of hospitals, clinics, and nursing homes to prevent recurrence, as well as feedback over time on patient outcomes that manifest later, e.g. as a result of missed or delayed diagnoses.
• Protect patients and providers by focusing on efficacy of solutions, not blame assignment.
• Feed anonymized, interoperable data into a national learning network that will spot systemic risks sooner and make aggregated data available for transparency and system learning.

Recommendation 2. Create a Real-time ‘Patient Safety Dashboard’ using AI 

HHS should build an AI-driven platform that integrates patient-reported safety data — including data from the new National Patient Reporting and Learning System, recommended above — with clinical data from electronic health records to create a real-time ‘patient safety dashboard’ for hospitals and clinics. This dashboard will empower providers to improve care in real time, and will: 

• Assist health care providers make accurate and timely diagnoses and avoid errors.
• Make patient reporting easy, effective, and actionable.
• Use AI to triage harm signals and detect systemic risk in real time.
• Build shared national infrastructure for healthcare reporting for all stakeholders.  
• Align incentives to reward harm reduction and safety.

By harnessing the power of AI providers will be able to respond faster, identify patients at risk more effectively, and prevent harm thereby improving outcomes. This “central nervous system” for patient safety will be deployed nationally to help detect safety signals in real time, connect information across settings, and alert teams before harm occurs.

Recommendation 3. Mine Billing Data for Deviations from Standards of Care

Standards of care are guidelines that define the process, procedures and treatments that patients should receive in various medical and professional contexts. Standards ensure that individuals receive appropriate and effective care based on established practices. Most standards of care are developed and promulgated by medical societies.  But not all clinicians and clinical settings adhere to standards of care, and deviations from standards of care are normal depending upon the case before them. Nonetheless, standards of care exist for a reason and deviations from standards of care should be noted when medical errors result in negative outcomes for patients so that clinicians can learn from these outcomes and improve. 

Some patient safety challenges are evident right in the billing data submitted to CMS and insurers. For example, deviations from standards of care can be captured in billing data by comparing clinical diagnosis codes with billing codes and then compared to widely accepted standards of care. By using CMS billing data, the government could identify opportunities for driving the development, augmentation, and wider adoption of standards of care by showing variability and compliance with standards of care for patients, reducing medical error and improving outcomes.

Giving standard setters real data to adapt and develop new standards of care is a powerful tool for improving patient outcomes. 

Recommendation 4. Create a Patient Safety AI Testbed

HHS can also establish a Patient Safety AI Testbed to evaluate how AI tools used in diagnosis, monitoring, and care coordination perform in real-world settings. This testbed will ensure that AI improves safety, not just efficiency — and can be co-led by patients, clinicians, and independent safety experts. This is an expansion of the testbeds in the HHS AI Strategic Plan. 

The Patient Safety Testbed could include:

• Funding for independent AI test environments to monitor real-world safety and performance over time.
• Public reliability benchmarks and “AI safety labeling”.
• Required participation by AI vendors and provider systems.

Conclusion

There are several key steps that the government can take to address the major loss of health, dollars, and lives due to medical errors, while simultaneously bolstering treatment guidelines, driving the development of new transparent data, and holding the medical establishment accountable for improving care. Here we present four proposals. None of them are particularly expensive when juxtaposed against the tremendous savings they will drive throughout  our healthcare system. We can only hope that the Administration’s commitment to patient safety is such that they will adopt them and drive a new era where caregivers, healthcare systems and insurance payers work together to improve patient safety and care standards.

This memo produced as part of Strengthening Pathways to Disease Prevention and Improved Health Outcomes.