Public Health Is Getting an AI Test Drive
America’s public health agencies are about to find out whether generative AI can do more than draft cheerful emails and turn meeting notes into suspiciously tidy bullet points.
The Coalition for Health AI, better known as CHAI, has launched a national program that will allow public health practitioners to test enterprise AI tools supplied by OpenAI and Anthropic. Accenture will help participating agencies run the pilots, train users and turn their experiences into practical guidance.
The program has a suitably energetic acronym: PULSE, short for Public Health Use Case and Learning Scaling Engine.
PULSE will involve 10 state, local, tribal or territorial jurisdictions. Each selected jurisdiction can register up to 200 participants, creating room for approximately 2,000 public health professionals.
That scale makes the initiative more than a cozy laboratory experiment. CHAI wants practitioners to test AI inside real working environments, compare results and document what succeeds—or crashes into a wall.
According to AI News, the pilots are expected to begin in fall 2026. The resulting implementation playbooks should arrive publicly in 2027.
That is the sales pitch, anyway. The real test begins when AI meets government data, old software and workflows held together by experience, patience and the occasional heroic spreadsheet.
Why Public Health Needs Help
Public health departments rarely enjoy the technological resources available to major hospital networks, pharmaceutical companies or Silicon Valley laboratories.
Many agencies operate with aging infrastructure, small technical teams and budgets that must stretch across disease surveillance, emergency preparedness, environmental health, vaccination programs, addiction services and community outreach.
Then somebody walks into the room and says, “Have you considered artificial intelligence?”
The answer, frequently, has been no.
Figures reported by TechTarget’s Healthtech Analytics show how wide the adoption gap remains. Only 5% of local health departments reported using AI in 2024. An eye-catching 84% had no plans to begin using it during the following year.
That does not mean public health workers dislike technology. Nearly 40% of departments that were not using AI still expressed some degree of interest in applying it to their work.
Interest, however, does not magically create budgets, policies or secure infrastructure. It certainly does not turn a chatbot into a trustworthy public health tool.
PULSE attempts to fill that awkward middle ground—the chasm between “AI might help” and “we know how to deploy it without creating a magnificent new category of problems.”
Ten Licenses, About 2,000 Seats
OpenAI and Anthropic have collectively donated 10 enterprise licenses to the initiative, reportedly providing capacity for approximately 2,000 practitioners.
That figure requires a little care. These are not 10 ordinary subscriptions being passed around among 2,000 people like a battered library card. The licenses appear to cover enterprise deployments associated with the 10 selected jurisdictions, with each jurisdiction allowed to enroll as many as 200 team members.
The available reports do not identify the exact products, model versions or technical configurations that agencies will receive. They also do not explain whether every participating jurisdiction will use both companies’ systems or whether access will vary by pilot.
That omission matters.
“AI” is not a single, interchangeable product. Different models have different capabilities, context limits, security arrangements, administrative controls and error profiles. A tool that performs well when summarizing community feedback may struggle with clinical-data queries. A strong English-language model may perform unevenly across less common languages.
Still, providing enterprise access removes one immediate barrier: cost. Public health departments can explore the technology without first fighting a lengthy procurement battle for a tool they have never tested.
That alone could make the program useful.
Accenture Gets the Unflashy but Crucial Job
OpenAI and Anthropic supply the shiny machinery. Accenture gets the less glamorous assignment: helping agencies make the machinery work.
The consulting company’s AI and public health specialists will support participant onboarding and the pilot process. They will also help translate lessons from the trials into playbooks that other agencies can use.
This part could determine whether PULSE produces meaningful results.
Giving someone access to an advanced AI system does not automatically improve a workflow. Agencies must decide who can use the tool, what information users may submit, how they should verify outputs and where human approval remains mandatory.
They also need to measure whether the system actually saves time.
A chatbot may generate a multilingual notice in seconds, for example. If three employees then spend two hours checking its accuracy, tone and legal implications, the apparent productivity miracle becomes less miraculous.
Implementation is where AI projects often lose their sparkle. Passwords need managing. Data need cleaning. Employees need training. Security teams ask uncomfortable but necessary questions. Existing software refuses to cooperate because it was apparently designed during the Bronze Age.
Accenture’s task is to help PULSE participants navigate that messy layer between impressive demonstration and dependable public service.
The Five Problems PULSE Wants to Tackle
CHAI has organized the pilots around five public health use cases:
- Biosurveillance and drug-wave prediction
- Social determinants of health mapping
- Operational efficiency and community-feedback analysis
- Public communications through a multilingual translation hub
- Automated clinical-data retrieval using a FHIR query engine
This is an ambitious menu. It ranges from relatively familiar language tasks, such as translating public notices, to much more consequential analytical work involving health patterns and clinical information.
CHAI’s 17-member leadership council will select participating jurisdictions and place practitioners into use-case communities. These groups will work on pilots, exchange results and help shape the eventual implementation guides.
The design offers an important advantage: agencies will not experiment entirely alone. A county health department can learn from other departments wrestling with similar problems.
That peer network may prove as valuable as the software.
Public institutions tend to face recurring obstacles—procurement rules, limited technical staffing, fragmented databases and strict accountability requirements. Sharing practical solutions can prevent 10 agencies from independently discovering the same five ways to break a workflow.
PULSE is therefore both a technology trial and a knowledge-sharing exercise. The AI may attract the headlines, but the collective learning could produce the lasting value.
Biosurveillance Meets the Prediction Machine
The first use case covers biosurveillance, specifically drug-wave prediction.
Public health teams monitor data for signs of emerging threats. In the context of drugs, they may look for changing overdose patterns, geographic clusters or the appearance of dangerous substances.
AI could potentially help analysts process large amounts of information and detect patterns more quickly. It might organize incoming reports, identify repeated signals or highlight anomalies that deserve human attention.
But prediction is the dangerous end of the AI swimming pool. The water gets deep very quickly.
A weak summary may waste an employee’s time. A weak forecast can redirect resources, distort priorities or create false confidence. Data quality also shapes every result. If reporting is delayed, incomplete or uneven across communities, a polished AI output may merely conceal those weaknesses beneath fluent prose.
The available PULSE announcements do not specify what data the biosurveillance pilots will use. They also do not explain how predictions will be validated or whether outputs will remain inside controlled tests.
That does not make the experiment reckless. It means the important work has barely started.
The pilot must measure false alarms, missed signals and usefulness—not simply whether the system can produce an authoritative-looking answer. AI excels at sounding ready for the press conference. Evidence must decide whether it deserves the microphone.
Mapping the Social Determinants of Health
A second PULSE community will explore mapping social determinants of health, commonly abbreviated as SDoH.
These determinants include nonmedical conditions that influence health, such as housing, income, education, food access, transportation and environmental exposure.
Public health agencies already work with this information. The challenge lies in connecting datasets that may use different formats, geographic boundaries and reporting schedules.
Generative AI could help practitioners search, classify or summarize that material. It might allow an analyst to ask a complex question in ordinary language instead of manually navigating several databases.
Yet SDoH data can be incomplete, outdated or shaped by historical inequities. A model cannot repair those problems through enthusiasm. If the underlying information poorly represents a community, AI may scale the blind spot rather than eliminate it.
Geographic conclusions also require nuance. An area associated with higher health risks is not a collection of identical people. Treating a map as destiny can stigmatize communities and encourage clumsy interventions.
A successful pilot will therefore need to test more than technical speed. It must examine whether the tool preserves context, identifies data limitations and helps practitioners reach better-supported conclusions.
Fast analysis is useful. Fast nonsense is merely nonsense wearing running shoes.
Community Feedback Without the Endless Sorting
Public health departments receive information through surveys, meetings, emails, call centers and community organizations.
That feedback can reveal what residents need, fear or misunderstand. Unfortunately, turning thousands of comments into useful themes takes time.
PULSE will test AI for community-feedback analysis as part of its operations and efficiency work.
This may be one of the initiative’s most immediately practical applications. Language models can categorize text, summarize recurring concerns and surface unusual responses. Staff could spend less time sorting comments and more time investigating what those comments mean.
The danger lies in compression.
Every summary removes detail. A model might emphasize the most frequently stated concern while overlooking a rare but serious warning. It may misunderstand slang, local references or comments written in mixed languages. It could also transform emotional testimony into bloodless administrative categories.
Agencies will need methods for tracing summaries back to the underlying comments. Humans should be able to ask, “Why did the system reach this conclusion?” and inspect the evidence.
PULSE’s eventual playbook could help departments decide when automated analysis is appropriate, how to sample-check results and how to preserve minority viewpoints.
If the program gets this right, AI could make public consultation more manageable without reducing the public to a stack of machine-generated themes.
A Multilingual Translation Hub
Public communication may offer PULSE its clearest early win.
During an outbreak, environmental emergency or vaccination campaign, health departments must communicate quickly. They also need to reach communities that speak different languages.
The initiative will explore a multilingual translation hub powered by generative AI. In theory, practitioners could prepare one message and rapidly produce versions for several language groups.
Speed matters during emergencies. So does consistency.
But translation involves much more than replacing one word with another. Health instructions must preserve medical meaning, cultural context and the correct level of urgency. A minor error can alter dosage guidance, eligibility rules or recommended actions.
The PULSE reports do not specify which languages the pilots will test, how translations will be evaluated or whether qualified speakers must approve messages before publication.
Those details will decide whether the hub becomes a genuine public service tool or an unusually confident phrasebook.
A sensible workflow would use AI to create an initial draft, then require human review before public distribution—especially for high-consequence messages. Agencies could also maintain approved terminology for recurring health concepts.
The model supplies speed. Human experts supply accountability.
Nobody wants to discover during an emergency that the chatbot translated “shelter in place” as “find somewhere pleasant to sit.”
The FHIR Query Engine Is the Technical Heavyweight
The fifth use case involves automated clinical-data retrieval through a FHIR query engine.
FHIR—Fast Healthcare Interoperability Resources—is a standard developed by HL7 for exchanging health information electronically. It helps compatible systems represent and share clinical data in structured ways.
A generative AI interface could make that information easier to query. Instead of
OpenAI and Anthropic Are Sending AI Into America’s Public Health Departments
Public Health Is Getting an AI Test Drive
Artificial intelligence has written essays, generated videos, debugged software, and occasionally invented facts with the confidence of a game-show host. Now it is heading into a considerably less forgiving environment: America’s public health system.
The Coalition for Health AI, commonly called CHAI, has launched a national program designed to help public health agencies evaluate and adopt generative AI. The initiative brings together two of the industry’s biggest rivals—OpenAI and Anthropic—alongside consulting giant Accenture.
Its name is PULSE, short for Public Health Use Case and Learning Scaling Engine.
PULSE will support AI pilots across 10 state, local, tribal, or territorial jurisdictions in the United States. The program could involve as many as 2,000 public health practitioners, according to reports from Artificial Intelligence News and TechTarget.
The goal is not simply to hand government workers a chatbot and wish them luck. PULSE intends to organize practical experiments, document what succeeds, identify what fails, and turn those lessons into implementation playbooks for other public health agencies.
That sounds sensible. It is also far more complicated than it sounds.
The Coalition Behind the Experiment
CHAI is a nonprofit coalition focused on encouraging responsible AI development and adoption in healthcare. Its work sits at the intersection of technology, medicine, regulation, and public trust—a crossroads where even small mistakes can become large problems remarkably quickly.
For PULSE, CHAI will coordinate the participating agencies and oversee the broader initiative. Its 17-member leadership council will select the jurisdictions and place practitioners into specialized communities organized around particular use cases.
Accenture will provide AI and public health experts. Those experts will help onboard participants, support the pilot projects, and translate what the agencies learn into practical guidance.
OpenAI and Anthropic will supply the underlying generative AI tools. The companies have collectively donated 10 enterprise licenses, reportedly providing capacity for approximately 2,000 participants.
That wording matters. These are not merely 10 individual chatbot accounts being passed around like office staplers. The enterprise arrangements are intended to support broad groups of public health professionals across the selected jurisdictions.
The precise products and model versions, however, have not been publicly identified. CHAI has not said which agencies will use OpenAI’s systems, which will use Anthropic’s, or whether participants will compare both.
Why Public Health Needs Help
Hospitals, insurers, pharmaceutical companies, and medical technology businesses have rushed to experiment with AI. Public health departments have moved far more slowly.
The disparity is hardly mysterious.
Many local agencies operate with limited budgets, aging software, fragmented databases, staffing shortages, and procurement processes that can make buying a printer feel like negotiating an international arms treaty. They must also comply with privacy rules, public-records requirements, cybersecurity policies, and layers of government oversight.
Then there is the workforce problem. Public health teams are already juggling disease surveillance, emergency preparedness, environmental health, maternal care, community education, vaccination programs, and mountains of administrative work.
According to data cited by TechTarget, only 5% of local health departments reported using AI in 2024. Eighty-four percent had no plan to use it during the following year.
Yet interest exists. Nearly 40% of departments that were not using AI said they were somewhat or very interested in using it to supplement their work.
That gap—curiosity without capacity—is where PULSE hopes to operate.
Ten Jurisdictions, Two Thousand Practitioners
PULSE will select 10 jurisdictions from a broad range of eligible organizations. The pool includes state and territorial health departments, municipal and county agencies, tribal authorities, Indian health organizations, and large-city health departments.
Each selected jurisdiction may register as many as 200 team members. If every location fills its allocation, the program will reach approximately 2,000 practitioners.
Applications reportedly remain open through August 6, 2026. The first pilots are expected to begin in the fall and run for six months. Public playbooks based on the results should follow in 2027.
That timeline gives the program a clear rhythm: choose the agencies, onboard the workforce, run focused experiments, compare experiences, and publish what everyone learned.
The approach also acknowledges an uncomfortable reality. A technology that works inside a wealthy metropolitan department may collapse inside a smaller rural agency with fewer employees, older systems, or weaker connectivity.
PULSE therefore needs more than impressive demonstrations. It needs evidence that its lessons travel well.
A playbook that assumes every agency has a data-science team, pristine databases, and generous funding would be less of a guide and more of a fairy tale.
Use Case One: Watching Drug Waves
The first proposed use case combines biosurveillance with drug-wave prediction.
Public health departments collect information from emergency rooms, laboratories, death records, poison-control centers, treatment providers, and other sources. Analysts use that information to detect dangerous trends, including the spread of infectious diseases and changes in illicit drug use.
Generative AI could potentially help practitioners review reports, summarize incoming information, identify recurring patterns, or turn technical findings into usable briefings. It might allow an analyst to interrogate large collections of reports using ordinary language rather than building every query manually.
The attraction is obvious. Earlier warnings could help agencies allocate naloxone, alert hospitals, inform community groups, or concentrate outreach where the threat appears to be growing.
But prediction is a treacherous word.
An AI system can detect patterns without understanding their causes. Missing data can skew its conclusions. Historical enforcement practices can distort drug-related datasets. A confident but incorrect warning could send resources to the wrong community.
PULSE will therefore need to examine more than whether a model produces a plausible answer. It must test timeliness, accuracy, false alarms, missed signals, and the consequences of acting on each output.
Use Case Two: Mapping Social Determinants of Health
The second focus area involves mapping social determinants of health, often abbreviated as SDoH.
Health does not begin inside a hospital. Housing quality, employment, income, transportation, food access, education, pollution, and neighborhood safety all influence whether communities thrive or struggle.
Public agencies already collect information about many of these conditions. The trouble is that the data often sits in separate systems, follows inconsistent formats, or arrives with missing pieces.
AI may help agencies combine and interpret those datasets. A practitioner could potentially ask which neighborhoods face overlapping risks, where service gaps exist, or whether transportation barriers correlate with poor access to care.
Used carefully, that capability could sharpen planning and expose needs that broad averages hide.
Used carelessly, it could turn imperfect data into automated stereotyping.
Geographic and demographic datasets can reproduce historical inequalities. A system may appear neutral while relying on records shaped by underinvestment, unequal access, or inconsistent reporting. Small communities may also become identifiable even when names and addresses have been removed.
The central challenge is not drawing a colorful map. Plenty of software can do that. The challenge is ensuring the map reflects reality rather than decorating bias with polished graphics.
Use Case Three: Listening to Communities
PULSE will also examine community-feedback analysis as a way to improve operations and efficiency.
Public health departments receive information through surveys, town-hall meetings, email, call centers, complaint forms, social media, and partnerships with community organizations. Processing that material by hand can consume enormous amounts of staff time.
Generative AI could sort comments into themes, summarize recurring concerns, detect shifts in sentiment, and help departments identify questions that demand a response.
Imagine an agency receiving thousands of comments about a vaccination campaign. AI might quickly separate concerns about clinic hours from questions about eligibility, transportation, side effects, or misinformation. Staff could then respond with greater precision.
There is a catch. Human communication is messy.
Sarcasm, local slang, cultural context, fear, anger, and mistrust do not always survive automated classification. Minority viewpoints may disappear inside a neat summary because most commenters discussed something else.
Efficiency can also become an excuse for distance. If agencies use AI merely to process communities instead of listening to them, the technology will undermine the trust it supposedly helps build.
The useful model is AI-assisted analysis with human interpretation—not a machine declaring what the public thinks after speed-reading a spreadsheet.
Use Case Four: A Multilingual Translation Hub
Public communications form the fourth use case, with PULSE proposing a multilingual translation hub.
This may become one of the program’s most immediately useful applications.
During outbreaks and emergencies, agencies must publish accurate information quickly. That information may include symptoms, testing locations, treatment instructions, evacuation guidance, or warnings about contaminated products.
Communities do not all speak the same language. Traditional translation can take time, and smaller departments may lack enough multilingual staff to respond at emergency speed.
Generative AI can produce drafts in many languages within seconds. It can also simplify dense material and reshape one technical document into a public notice, frequently asked questions page, or social-media post.
That is the attractive half.
The dangerous half is that a small translation error can reverse medical advice, confuse a dosage, soften a warning, or introduce culturally inappropriate language. Fluent prose does not guarantee faithful meaning.
Any serious deployment should require qualified human review before agencies publish high-stakes translations. The pilot will need to test more than grammatical smoothness. It should evaluate medical accuracy, regional vocabulary, cultural clarity, and performance in languages with fewer training resources.
Speed helps only when the message remains correct.
Use Case Five: Asking Health Records Questions
The final use case involves automated clinical-data retrieval through a FHIR query engine.
FHIR—Fast Healthcare Interoperability Resources—is a standard created by HL7 for exchanging electronic health information between compatible systems. In plain English, it helps different health technologies speak a shared technical language.
A generative AI interface could theoretically allow authorized practitioners to retrieve information by asking questions conversationally. Instead of manually constructing a technical query, a user might describe the information needed and let the system generate the request.
That could reduce friction for employees who understand public health but do not write database queries for sport.
Yet this may be the program’s most technically sensitive experiment.
A faulty query could return incomplete information. A model could misinterpret a request, select the wrong field, or summarize results inaccurately. Permissions must prevent users from accessing records outside their authority.
The announcement does not specify whether AI will generate FHIR queries, summarize retrieved records, manage the entire process, or perform some combination of those jobs.
It also does not explain how agencies will validate queries and outputs. That omission does not prove weak controls. It does mean the public cannot assess those controls yet.
The Guardrails Are Still Blurry
PULSE repeatedly emphasizes trust, privacy, governance, transparency, and responsible use. Those are the correct themes. They are not yet a complete operating manual.
As Artificial Intelligence News observed, CHAI has not publicly specified many of the program’s detailed safeguards. The announcement does not define separate evaluation standards for each use case. It does not explain mandatory human-review procedures or disclose precise rules for data retention, access controls, auditing, storage, and cybersecurity.
The program also has not said whether pilots will use identifiable health records, de-identified datasets, aggregate information, or synthetic data.
Those distinctions matter enormously.
HIPAA protections may apply to some workflows and organizations but not others. Public health data operates within a complicated legal landscape, and HIPAA does not automatically govern every dataset simply because it concerns health.
OpenAI and Anthropic say they do not use inputs and outputs from their commercial services to train their models by default. That offers a baseline, but it does not answer every deployment question.
Configuration matters. Contracts matter. User permissions matter. Data flows matter.
“Enterprise” is a product category, not a magic privacy spell.
OpenAI and Anthropic Become Unusual Teammates
OpenAI and Anthropic compete fiercely for customers, developers, talent, prestige, and the right to claim that their latest model has conquered another benchmark.
Inside PULSE, however, they occupy the same marquee.
OpenAI said its donated access would allow public health organizations to test AI through a structured process. Anthropic emphasized the need to give overworked agencies useful tools while incorporating privacy, governance, and responsible-use controls from the beginning.
Their participation brings technology and attention. It also creates an opportunity that PULSE should not waste: comparison.
Different models may behave differently when summarizing community feedback, translating medical information, retrieving structured data, or interpreting ambiguous instructions. Testing systems from two vendors could reveal where one performs better, where both struggle, and which safeguards remain necessary regardless of the provider.
However, CHAI has not announced a head-to-head evaluation or explained how tools will be assigned.
If the program treats each platform separately, it may still produce valuable lessons. If it uses consistent tests across both, it could generate something rarer: genuinely useful comparative evidence from public-sector settings.
Accenture’s Job Is Where Theory Meets Reality
AI companies can supply models. Public health practitioners can supply domain expertise. Someone still has to connect the machinery.
That task falls partly to Accenture.
The firm’s specialists will help agencies onboard participants, conduct pilots, and develop implementation playbooks. This practical layer matters because AI adoption rarely fails solely due to the model.
Projects stumble over incompatible databases, unclear responsibilities, weak training, confused procurement rules, insufficient support, unrealistic expectations, and workflows designed without consulting the people who actually perform the work.
A flashy demonstration can happen in an afternoon. Sustainable implementation takes considerably longer—and usually involves more meetings than any healthy person would voluntarily request.
Accenture and CHAI will need to document the unglamorous details: staffing requirements, training time, integration costs, approval processes, security reviews, and the amount of human supervision each task requires.
Those details may prove more useful to future agencies than any polished example of a chatbot producing a clever answer.
Turning Ten Pilots Into National Guidance
PULSE’s most ambitious promise is not the pilot program itself. It is the plan to convert lessons from 10 jurisdictions into playbooks that other agencies can use.
That requires disciplined documentation.
The playbooks should explain which use cases delivered measurable benefits, what kinds of data were required, how much implementation cost, where models failed, and which safeguards prevented mistakes. They should also describe the contexts in which agencies should not use generative AI.
Failure reports will be especially valuable.
Organizations love publishing success stories. Yet a careful record of rejected approaches, recurring hallucinations, weak translations, unreliable queries, and unexpected workflow problems could save other departments time and money.
The guidance must also account for differences among jurisdictions. A tribal health authority, a small county department, and a major state agency do not share identical legal duties, staffing levels, infrastructure, or community relationships.
Ten pilots cannot represent every public health environment in America. They can, however, produce a strong starting point—provided CHAI resists turning messy findings into universal slogans.
This Is an Experiment, Not a Victory Lap
PULSE deserves attention because it focuses on practical adoption rather than another abstract declaration that AI will “transform healthcare.”
Still, the launch itself proves very little.
The program has not yet demonstrated that generative AI can predict drug waves reliably, map health inequalities fairly, interpret public feedback accurately, translate emergency information safely, or retrieve clinical data without consequential errors.
Those are hypotheses. The pilots must test them.
Success should mean more than enthusiastic users or faster output. CHAI should examine accuracy, workload reduction, response times, equity, security incidents, correction rates, and whether AI-generated work requires so much review that the promised efficiency evaporates.
The initiative also needs candor. If one use case fails, the responsible result is not to massage it into a cheerful case study. It is to say that the tool was not ready, the workflow was poorly designed, or the risks outweighed the benefit.
Public health agencies do not need AI theater. They need reliable systems.
The Bigger Stakes
Public health often becomes visible only when something goes wrong: an outbreak spreads, overdose deaths surge, contaminated water emerges, or emergency communications fail.
Between crises, agencies perform quiet, continuous work with limited resources. That makes them tempting candidates for automation. It also makes careless experimentation dangerous.
PULSE could help departments complete repetitive tasks faster, find patterns buried in fragmented information, communicate across language barriers, and learn from one another. If the program works, its playbooks may give smaller agencies a route into AI that they could not build independently.
But the initiative’s real contribution may be less glamorous.
It can show where generative AI should remain an assistant, where humans must retain control, and where the technology simply does not perform well enough.
OpenAI and Anthropic are supplying powerful tools. Accenture is supplying implementation support. CHAI is supplying the structure. Public health professionals will now supply the reality check.
That is the part worth watching.
A chatbot can draft a confident answer in seconds. Earning trust across America’s public health system will take considerably longer.
Sources
- Artificial Intelligence News — “US public health agencies to test OpenAI and Anthropic AI models”
- TechTarget HealthTech Analytics — “CHAI launches AI adoption support for public health agencies”
- Hospital & Healthcare Management — “Coalition for Health AI Unveils PULSE Initiative to Scale Public Health AI Adoption”
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