NHS England and Microsoft announced on June 8, 2026, that Microsoft 365 Copilot will be rolled out to about 505,000 clinicians and support staff across NHS services in England by October 2026. The promise is disarmingly simple: give overstretched staff an AI assistant for the paperwork that surrounds care, and recover time for patients. The harder truth is that the NHS is not merely buying a productivity tool; it is testing whether one of the world’s largest public health systems can safely industrialize generative AI without turning clinical work into another software dependency. If the numbers hold, this could be one of the most consequential deployments of Microsoft’s AI stack yet.
The headline figure is 43 minutes per staff member per day. That is the average administrative time reportedly saved during a large NHS trial of Microsoft 365 Copilot involving more than 30,000 workers across 90 NHS organizations. Scaled to half a million users, it becomes the sort of number that makes ministers, procurement teams, and hospital executives sit up: millions of hours returned each month, at least on paper.
That framing matters because the NHS has never lacked digital ambition. What it lacks is slack. Clinicians and support staff are often asked to absorb new systems while continuing to meet service targets, handle backlogs, and document everything with a level of precision that modern healthcare demands. A tool that drafts, summarizes, searches, formats, and analyzes within the Microsoft 365 environment is appealing precisely because it attacks the accreted administrative burden around care rather than attempting to replace clinical judgment outright.
Microsoft’s language is carefully pitched at that boundary. Copilot is presented as a personal assistant, not a diagnostician. It can help draft documents, summarize meetings, analyze data, and assist with routine knowledge work. In NHS terms, that means fewer minutes spent wrestling with correspondence, board papers, service documents, inbox triage, meeting notes, and internal reports.
The crucial distinction is that the NHS is not announcing a moonshot AI doctor. It is announcing AI for the boring work. That is why the announcement may prove more important than many flashier health-tech pilots: healthcare systems often bleed time not at the dramatic edge of medicine but in the mundane connective tissue of administration.
But average time-saved figures are tricky beasts. They compress different roles, habits, workloads, levels of training, and adoption patterns into a single productivity claim. A senior manager producing documents all day may find Copilot immediately useful; a ward-based clinician with little desk time may experience the tool quite differently. Some staff may use it daily, others only occasionally, and some may not trust it enough to let it meaningfully change their workflow.
That does not make the number useless. It makes it a hypothesis at national scale. The trial suggests there is real administrative friction Copilot can reduce, but national deployment will reveal whether those savings survive the messy realities of staff turnover, uneven digital maturity, local governance, clinical pressure, and the simple fact that a tool only saves time when people can actually use it in the flow of work.
This is where Microsoft has an unusually favorable starting position. The NHS already runs heavily on Microsoft 365, so Copilot enters through familiar applications rather than as a completely foreign platform. That reduces friction, but it also deepens lock-in. Once the productivity layer, AI assistant, document corpus, identity system, and collaboration workflows all point in the same direction, changing course becomes harder.
An AI assistant helps a user complete a task. An AI agent starts to participate in a workflow. In a health system, that difference is enormous. Once organizations begin creating agents for HR, finance, procurement, operational analysis, reporting, scheduling, or internal service requests, the AI layer stops being a convenience feature and becomes part of the administrative architecture.
That is both the opportunity and the hazard. The NHS is full of repeatable processes that are slow, fragmented, and expensive because they rely on humans copying information between systems, generating documents from templates, and interpreting internal policies. AI tools can compress that work. They can also produce confident errors, obscure responsibility, and turn governance into a game of chasing automation after it has already entered practice.
The likely early wins will not be glamorous. Drafting internal updates, preparing summaries from meetings, turning notes into structured documents, analyzing spreadsheet data, and producing first-pass reports are exactly the tasks where generative AI tends to feel useful. These are also tasks where small errors are manageable if a human remains firmly in the loop.
The real stress test will come later, when local organizations want to connect agents to more sensitive workflows. The temptation will be obvious: if Copilot can summarize, why not triage? If it can analyze, why not recommend? If it can draft, why not complete? NHS England’s challenge will be to draw bright lines before the productivity story quietly becomes a decision-support story.
That is why this rollout is politically attractive. The NHS can tell staff that the tool exists to remove drudgery. It can tell patients that clinicians should have more time for care. It can tell taxpayers that the health service is using its purchasing power to improve productivity. Microsoft, meanwhile, gets a flagship deployment in one of the most visible public-sector environments in the world.
There is a strategic lesson here for the broader enterprise market. Generative AI may not first transform organizations by replacing expert work. It may transform them by attaching itself to the clerical shadow of expert work: the notes, forms, minutes, summaries, emails, policy documents, and analysis that professionals must produce to keep institutions moving.
In the NHS, that shadow is huge. Modern healthcare is documentation-heavy for good reasons: safety, accountability, funding, auditability, legal defensibility, continuity of care. But the cumulative effect is that highly trained staff can spend painful chunks of their day producing, reading, reformatting, and rediscovering information. If AI can reduce even a modest fraction of that load reliably, the operational case becomes powerful.
The risk is that recovered time is rarely recovered cleanly. An hour saved in one workflow can be swallowed by another demand before it ever reaches the patient. For Copilot to produce visible benefits, NHS organizations will need to measure not only whether staff generate documents faster, but whether clinical capacity, service responsiveness, or staff wellbeing actually improves.
Microsoft 365 Copilot is not a standalone chatbot in the classic sense. Its value depends on proximity to organizational data: emails, calendars, Teams meetings, Word documents, PowerPoint decks, Excel workbooks, SharePoint sites, and permissions managed through Microsoft’s identity stack. The more an organization lives inside Microsoft 365, the more plausible Copilot becomes as a unifying assistant.
That architecture gives Microsoft an advantage competitors cannot easily match. A generic chatbot may be impressive, but an assistant embedded in the tools workers already use has a different adoption path. It appears where the work already happens. It inherits familiar permissions. It can summarize the meeting you just had and draft the document you already need.
But that same architecture raises the stakes for configuration and governance. If permissions are messy, Copilot can expose organizational mess at machine speed. If old files are over-shared, if SharePoint sites have become dumping grounds, if sensitive documents sit in locations with broad access, then an AI assistant grounded in enterprise data may surface information more effectively than the organization intended.
For sysadmins, this is the unglamorous but essential lesson. AI readiness is not just a training deck. It is identity hygiene, data classification, lifecycle management, retention policy, audit logging, endpoint security, conditional access, and a clear understanding of who can see what. The NHS rollout will depend as much on boring Microsoft 365 administration as on any model capability.
That last point deserves attention. AI productivity tools generate telemetry. They can show usage, activity, and patterns of engagement. In a strained public service, staff may reasonably worry that a tool marketed as relief could become a mechanism for monitoring or squeezing more output from already overburdened teams. The NHS will need to be explicit that time saved is not merely a way to raise the administrative treadmill.
There is also the question of confidence. Copilot can produce fluent text that still needs checking. In administrative work, that may be acceptable when the output is a first draft or a summary. But in healthcare-adjacent contexts, a subtly wrong summary, an omitted caveat, or a misread data point can matter. Staff must have enough training not only to use the tool, but to distrust it appropriately.
The best deployments will likely treat Copilot as a drafting and synthesis aid rather than a final authority. That sounds obvious, but enterprise AI failures often begin when convenience outruns review. The faster a tool makes work feel, the easier it is for verification to become perfunctory.
NHS England’s planned adoption and AI skilling program will therefore be more than a support function. It will be the difference between a productivity program and a reputational risk. Staff need examples rooted in their actual roles, not generic prompts. They need clear rules on patient-identifiable information, internal confidentiality, output checking, and escalation when the tool behaves unexpectedly.
First, the tool must be available to the right staff. Then those staff must use it often enough for meaningful savings. Then those savings must occur in parts of the day where time can be reallocated rather than merely absorbed. Then management must resist the instinct to fill the newly available minutes with additional documentation, meetings, or targets. Only then does the patient experience improve in a way ordinary people can feel.
That does not mean the claim is hollow. Administrative overload is one of the great hidden enemies of care quality. It contributes to burnout, delays communication, reduces responsiveness, and forces clinicians to divide attention between people and systems. If Copilot can reduce the time spent producing low-value paperwork, even partially, the patient-facing upside is real.
But the NHS should be judged on outcomes, not just deployment scale. A half-million licenses is not a health outcome. Forty-three minutes saved in a trial is not the same as shorter waits, better communication, faster discharge letters, improved staff retention, or safer handovers. Those are the measures that will determine whether the rollout becomes a model or a cautionary tale.
The most honest version of the promise is not that AI will magically fix NHS capacity. It is that AI may remove some of the administrative drag that makes existing capacity feel smaller than it is. That is worth pursuing, but it is not a substitute for staffing, funding, estate modernization, interoperability, or reform of broken processes.
For the NHS, the trade-off is more complicated. Microsoft brings mature enterprise controls, procurement familiarity, integration with existing systems, and the sheer gravitational pull of a platform already embedded in daily work. Those are strong reasons to choose it. They are also the reasons dependency grows quietly.
Once staff build habits around Copilot, once local teams create agents in Copilot Studio, once workflows assume Microsoft’s AI layer is present, the cost of leaving increases. That does not make the deal wrong; lock-in is not automatically a scandal. But public-sector buyers should be candid about it. AI procurement is not like buying another office application. It can become a layer through which institutional knowledge is accessed and work is routed.
The NHS will also need to keep pressure on value for money. Microsoft 365 Copilot is typically positioned as a premium per-user product in the enterprise market, and even discounted public-sector terms can add up quickly at this scale. The return on investment depends on sustained usage, measurable time savings, and actual operational benefit. Licenses that sit idle are expensive; licenses that change workflows without improving care are worse.
This is where central purchasing power can help. The NHS can negotiate at a scale few organizations can match, and it can standardize governance, training, and measurement. But central scale can also hide local variation. A trust that gets transformative value and a trust that barely uses the tool may look identical in a national rollout dashboard unless measurement is designed carefully.
Microsoft and NHS England will emphasize enterprise-grade controls, existing permissions, and governance tooling. Those controls matter. But the practical question is how well each organization has managed the data estate Copilot will be allowed to reason over. AI does not magically create a clean information architecture. It reveals the consequences of the one you already have.
The most immediate danger is not necessarily a cinematic breach. It is oversharing, misclassification, inappropriate summarization, accidental inclusion of sensitive content, and misplaced confidence in generated text. A staff member asking Copilot to summarize a document may not always know what source material has influenced the answer. A manager using it to analyze internal data may not understand the limitations of the dataset. A team building an agent may not anticipate every context in which it will be used.
That is why governance cannot be confined to IT or information governance teams. Line managers, clinical safety officers, Caldicott Guardians, security teams, records managers, and frontline staff all have a role. The NHS has institutional experience with confidentiality and information governance; the challenge is translating those principles into the faster, more fluid world of generative AI.
The rollout should also sharpen public debate about where patient data sits in AI-enabled productivity tools. Even when vendors provide contractual assurances and technical boundaries, public trust depends on clear communication. Patients do not need marketing language. They need to know whether their information is being used, for what purpose, under what controls, and with what human oversight.
Microsoft’s Biggest NHS Pitch Is Not Intelligence, but Time
The headline figure is 43 minutes per staff member per day. That is the average administrative time reportedly saved during a large NHS trial of Microsoft 365 Copilot involving more than 30,000 workers across 90 NHS organizations. Scaled to half a million users, it becomes the sort of number that makes ministers, procurement teams, and hospital executives sit up: millions of hours returned each month, at least on paper.That framing matters because the NHS has never lacked digital ambition. What it lacks is slack. Clinicians and support staff are often asked to absorb new systems while continuing to meet service targets, handle backlogs, and document everything with a level of precision that modern healthcare demands. A tool that drafts, summarizes, searches, formats, and analyzes within the Microsoft 365 environment is appealing precisely because it attacks the accreted administrative burden around care rather than attempting to replace clinical judgment outright.
Microsoft’s language is carefully pitched at that boundary. Copilot is presented as a personal assistant, not a diagnostician. It can help draft documents, summarize meetings, analyze data, and assist with routine knowledge work. In NHS terms, that means fewer minutes spent wrestling with correspondence, board papers, service documents, inbox triage, meeting notes, and internal reports.
The crucial distinction is that the NHS is not announcing a moonshot AI doctor. It is announcing AI for the boring work. That is why the announcement may prove more important than many flashier health-tech pilots: healthcare systems often bleed time not at the dramatic edge of medicine but in the mundane connective tissue of administration.
The 43-Minute Claim Will Carry the Whole Project
Every big technology rollout needs a number simple enough to survive politics, procurement, and public skepticism. For this one, 43 minutes is that number. It implies roughly five weeks of administrative time per person annually, and NHS leaders have translated that into a more human metric: around two days of admin time back every month.But average time-saved figures are tricky beasts. They compress different roles, habits, workloads, levels of training, and adoption patterns into a single productivity claim. A senior manager producing documents all day may find Copilot immediately useful; a ward-based clinician with little desk time may experience the tool quite differently. Some staff may use it daily, others only occasionally, and some may not trust it enough to let it meaningfully change their workflow.
That does not make the number useless. It makes it a hypothesis at national scale. The trial suggests there is real administrative friction Copilot can reduce, but national deployment will reveal whether those savings survive the messy realities of staff turnover, uneven digital maturity, local governance, clinical pressure, and the simple fact that a tool only saves time when people can actually use it in the flow of work.
This is where Microsoft has an unusually favorable starting position. The NHS already runs heavily on Microsoft 365, so Copilot enters through familiar applications rather than as a completely foreign platform. That reduces friction, but it also deepens lock-in. Once the productivity layer, AI assistant, document corpus, identity system, and collaboration workflows all point in the same direction, changing course becomes harder.
The NHS Is Buying an AI Layer for Its Existing Bureaucracy
The most revealing part of the deal is not that staff will get Microsoft 365 Copilot. It is that the agreement also includes access to Copilot Studio and governance through Agent 365, positioning the NHS to build and deploy AI agents on top of existing processes. That moves the story beyond drafting emails and summarizing meetings.An AI assistant helps a user complete a task. An AI agent starts to participate in a workflow. In a health system, that difference is enormous. Once organizations begin creating agents for HR, finance, procurement, operational analysis, reporting, scheduling, or internal service requests, the AI layer stops being a convenience feature and becomes part of the administrative architecture.
That is both the opportunity and the hazard. The NHS is full of repeatable processes that are slow, fragmented, and expensive because they rely on humans copying information between systems, generating documents from templates, and interpreting internal policies. AI tools can compress that work. They can also produce confident errors, obscure responsibility, and turn governance into a game of chasing automation after it has already entered practice.
The likely early wins will not be glamorous. Drafting internal updates, preparing summaries from meetings, turning notes into structured documents, analyzing spreadsheet data, and producing first-pass reports are exactly the tasks where generative AI tends to feel useful. These are also tasks where small errors are manageable if a human remains firmly in the loop.
The real stress test will come later, when local organizations want to connect agents to more sensitive workflows. The temptation will be obvious: if Copilot can summarize, why not triage? If it can analyze, why not recommend? If it can draft, why not complete? NHS England’s challenge will be to draw bright lines before the productivity story quietly becomes a decision-support story.
Healthcare AI Has Learned to Enter Through the Back Office
For years, the public debate around AI in healthcare has focused on diagnosis, imaging, drug discovery, and clinical decision support. Those are important, but they are also heavily regulated, clinically sensitive, and difficult to scale. Administrative AI is a softer entrance. It promises immediate relief without asking patients to trust an algorithm with a diagnosis.That is why this rollout is politically attractive. The NHS can tell staff that the tool exists to remove drudgery. It can tell patients that clinicians should have more time for care. It can tell taxpayers that the health service is using its purchasing power to improve productivity. Microsoft, meanwhile, gets a flagship deployment in one of the most visible public-sector environments in the world.
There is a strategic lesson here for the broader enterprise market. Generative AI may not first transform organizations by replacing expert work. It may transform them by attaching itself to the clerical shadow of expert work: the notes, forms, minutes, summaries, emails, policy documents, and analysis that professionals must produce to keep institutions moving.
In the NHS, that shadow is huge. Modern healthcare is documentation-heavy for good reasons: safety, accountability, funding, auditability, legal defensibility, continuity of care. But the cumulative effect is that highly trained staff can spend painful chunks of their day producing, reading, reformatting, and rediscovering information. If AI can reduce even a modest fraction of that load reliably, the operational case becomes powerful.
The risk is that recovered time is rarely recovered cleanly. An hour saved in one workflow can be swallowed by another demand before it ever reaches the patient. For Copilot to produce visible benefits, NHS organizations will need to measure not only whether staff generate documents faster, but whether clinical capacity, service responsiveness, or staff wellbeing actually improves.
The Windows and Microsoft 365 Angle Is Bigger Than a Health Story
For WindowsForum readers, this is not just an NHS procurement item. It is a live case study in Microsoft’s broader strategy: make AI a default layer across work, identity, documents, security, collaboration, and process automation. The NHS rollout shows how that strategy lands when the customer is not a startup or a bank, but a national health service.Microsoft 365 Copilot is not a standalone chatbot in the classic sense. Its value depends on proximity to organizational data: emails, calendars, Teams meetings, Word documents, PowerPoint decks, Excel workbooks, SharePoint sites, and permissions managed through Microsoft’s identity stack. The more an organization lives inside Microsoft 365, the more plausible Copilot becomes as a unifying assistant.
That architecture gives Microsoft an advantage competitors cannot easily match. A generic chatbot may be impressive, but an assistant embedded in the tools workers already use has a different adoption path. It appears where the work already happens. It inherits familiar permissions. It can summarize the meeting you just had and draft the document you already need.
But that same architecture raises the stakes for configuration and governance. If permissions are messy, Copilot can expose organizational mess at machine speed. If old files are over-shared, if SharePoint sites have become dumping grounds, if sensitive documents sit in locations with broad access, then an AI assistant grounded in enterprise data may surface information more effectively than the organization intended.
For sysadmins, this is the unglamorous but essential lesson. AI readiness is not just a training deck. It is identity hygiene, data classification, lifecycle management, retention policy, audit logging, endpoint security, conditional access, and a clear understanding of who can see what. The NHS rollout will depend as much on boring Microsoft 365 administration as on any model capability.
Staff Trust Will Decide Whether Copilot Becomes a Tool or Shelfware
Technology vendors often describe adoption as if it were a rollout problem: assign licenses, train users, measure engagement, repeat. In healthcare, adoption is more intimate. Staff will use a tool if it helps under pressure, if it does not slow them down, if it does not create new risks, and if they believe leadership will not weaponize the data against them.That last point deserves attention. AI productivity tools generate telemetry. They can show usage, activity, and patterns of engagement. In a strained public service, staff may reasonably worry that a tool marketed as relief could become a mechanism for monitoring or squeezing more output from already overburdened teams. The NHS will need to be explicit that time saved is not merely a way to raise the administrative treadmill.
There is also the question of confidence. Copilot can produce fluent text that still needs checking. In administrative work, that may be acceptable when the output is a first draft or a summary. But in healthcare-adjacent contexts, a subtly wrong summary, an omitted caveat, or a misread data point can matter. Staff must have enough training not only to use the tool, but to distrust it appropriately.
The best deployments will likely treat Copilot as a drafting and synthesis aid rather than a final authority. That sounds obvious, but enterprise AI failures often begin when convenience outruns review. The faster a tool makes work feel, the easier it is for verification to become perfunctory.
NHS England’s planned adoption and AI skilling program will therefore be more than a support function. It will be the difference between a productivity program and a reputational risk. Staff need examples rooted in their actual roles, not generic prompts. They need clear rules on patient-identifiable information, internal confidentiality, output checking, and escalation when the tool behaves unexpectedly.
The Patient Benefit Is Plausible, but Not Automatic
The most emotionally compelling claim is that Copilot will give staff more time for patient care. It might. But the chain of causality is longer than the press release version suggests.First, the tool must be available to the right staff. Then those staff must use it often enough for meaningful savings. Then those savings must occur in parts of the day where time can be reallocated rather than merely absorbed. Then management must resist the instinct to fill the newly available minutes with additional documentation, meetings, or targets. Only then does the patient experience improve in a way ordinary people can feel.
That does not mean the claim is hollow. Administrative overload is one of the great hidden enemies of care quality. It contributes to burnout, delays communication, reduces responsiveness, and forces clinicians to divide attention between people and systems. If Copilot can reduce the time spent producing low-value paperwork, even partially, the patient-facing upside is real.
But the NHS should be judged on outcomes, not just deployment scale. A half-million licenses is not a health outcome. Forty-three minutes saved in a trial is not the same as shorter waits, better communication, faster discharge letters, improved staff retention, or safer handovers. Those are the measures that will determine whether the rollout becomes a model or a cautionary tale.
The most honest version of the promise is not that AI will magically fix NHS capacity. It is that AI may remove some of the administrative drag that makes existing capacity feel smaller than it is. That is worth pursuing, but it is not a substitute for staffing, funding, estate modernization, interoperability, or reform of broken processes.
Microsoft Gets a Flagship, the NHS Gets a Dependency
The commercial significance for Microsoft is obvious. A 505,000-user healthcare deployment gives Copilot credibility at a scale few case studies can match. It shows that Microsoft can sell AI not merely as a premium enterprise add-on, but as infrastructure for public services.For the NHS, the trade-off is more complicated. Microsoft brings mature enterprise controls, procurement familiarity, integration with existing systems, and the sheer gravitational pull of a platform already embedded in daily work. Those are strong reasons to choose it. They are also the reasons dependency grows quietly.
Once staff build habits around Copilot, once local teams create agents in Copilot Studio, once workflows assume Microsoft’s AI layer is present, the cost of leaving increases. That does not make the deal wrong; lock-in is not automatically a scandal. But public-sector buyers should be candid about it. AI procurement is not like buying another office application. It can become a layer through which institutional knowledge is accessed and work is routed.
The NHS will also need to keep pressure on value for money. Microsoft 365 Copilot is typically positioned as a premium per-user product in the enterprise market, and even discounted public-sector terms can add up quickly at this scale. The return on investment depends on sustained usage, measurable time savings, and actual operational benefit. Licenses that sit idle are expensive; licenses that change workflows without improving care are worse.
This is where central purchasing power can help. The NHS can negotiate at a scale few organizations can match, and it can standardize governance, training, and measurement. But central scale can also hide local variation. A trust that gets transformative value and a trust that barely uses the tool may look identical in a national rollout dashboard unless measurement is designed carefully.
The Data Governance Story Will Not Stay in the Background
Every major AI deployment eventually becomes a data governance story. In healthcare, it starts there. Even if Copilot is used primarily for administration, NHS workers operate in environments where sensitive personal, clinical, workforce, and financial information is everywhere.Microsoft and NHS England will emphasize enterprise-grade controls, existing permissions, and governance tooling. Those controls matter. But the practical question is how well each organization has managed the data estate Copilot will be allowed to reason over. AI does not magically create a clean information architecture. It reveals the consequences of the one you already have.
The most immediate danger is not necessarily a cinematic breach. It is oversharing, misclassification, inappropriate summarization, accidental inclusion of sensitive content, and misplaced confidence in generated text. A staff member asking Copilot to summarize a document may not always know what source material has influenced the answer. A manager using it to analyze internal data may not understand the limitations of the dataset. A team building an agent may not anticipate every context in which it will be used.
That is why governance cannot be confined to IT or information governance teams. Line managers, clinical safety officers, Caldicott Guardians, security teams, records managers, and frontline staff all have a role. The NHS has institutional experience with confidentiality and information governance; the challenge is translating those principles into the faster, more fluid world of generative AI.
The rollout should also sharpen public debate about where patient data sits in AI-enabled productivity tools. Even when vendors provide contractual assurances and technical boundaries, public trust depends on clear communication. Patients do not need marketing language. They need to know whether their information is being used, for what purpose, under what controls, and with what human oversight.
The Five Checks That Will Decide Whether This Rollout Is More Than a Press Release
The NHS and Microsoft have chosen a pragmatic version of healthcare AI: less robot doctor, more administrative exoskeleton. That makes the project easier to defend and potentially easier to scale. It also means success will be measured in dull but decisive operational details rather than dazzling demos.- The 43-minute saving must be tested continuously against real-world usage after the October 2026 rollout, not treated as a permanent benefit guaranteed by the trial.
- NHS organizations must clean up permissions, document repositories, and data classification before Copilot makes old information-governance problems easier to discover.
- Staff training must teach workers when not to trust AI output, because fluent drafting is not the same thing as accuracy.
- Patient benefit should be measured through service outcomes such as faster communication, reduced administrative delays, and improved staff capacity, not merely by counting assigned licenses.
- Copilot Studio and AI agents should be governed as workflow infrastructure, because automation that begins in the back office can quickly move toward higher-risk decisions.
- The NHS should publish enough evidence over time for the public to distinguish genuine productivity gains from vendor optimism and political wish-casting.
References
- Primary source: Pharmacy Business
Published: 2026-06-08T15:02:07.265262
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