Over 6+ years, SCALE Community convened executives, policymakers, clinicians, innovators, and global health leaders for candid, unscripted conversations. This e-book distills what they actually said.
What 44 conversations with the industry’s leading minds actually revealed
Over three years, the Scale Community’s Analyzing Healthcare podcast convened executives, policymakers, clinicians, innovators, and global health leaders for candid, unscripted conversations. This e-book distills what they actually said — the consensus views, the tensions, the hard truths, and the genuine optimism — organized around the themes that matter most to healthcare’s next chapter.
Conversations
Key themes
Countries represented
Years of dialogue
The gap between AI’s promise and its reality inside hospitals — and what it takes to close it
Across dozens of conversations, one theme emerged with near-universal agreement: AI will be the most transformative force in healthcare in the coming decade. But the distance between a promising model and a functioning clinical tool is vast — and most of the challenge is organizational, not technical. The executives and clinicians who have navigated this gap shared hard-won lessons about what makes AI adoption work and what makes it fail.
81% of FDA-cleared AI models underperform in real-world clinical settings
The regulatory landscape gives hospitals a false sense of security. FDA clearance is necessary but far from sufficient. What works in a controlled study population often fails when deployed across the varied patient populations, scanner types, workflow habits, and hanging protocols of a real health system. This isn’t a fringe concern — it describes the majority of commercially available AI tools.
The implication is that hospitals need their own validation infrastructure, not just vendor white papers. The organizations that are succeeding with clinical AI are those that test models against their own patient populations before deployment, monitor performance after go-live, and have governance frameworks to replace models when they drift or degrade.
The hardest part of AI adoption is not the technology — it is the culture
Senior healthcare technology leaders were consistent on this point: digital transformation is roughly 70% organizational change and 30% technology. The number one predictor of success isn’t which platform a health system chooses — it’s whether clinicians are leading the initiative or merely being consulted on it.
AI tools imposed on physicians fail. Tools designed with physicians — starting from a real clinical problem, validated in clinical workflows, and governed by clinicians — get used. The health systems making the most progress on AI are those where the CMO and the CDO are aligned, where innovation labs are tightly connected to clinical operations, and where there is a clear pathway from a promising model to deployed care.
The corollary: AI governance committees staffed by clinicians are emerging as a critical organizational capability. Health systems that treat AI governance as an IT function are consistently behind those that treat it as a clinical leadership function.
Domain expertise is a prerequisite, not an obstacle
A data scientist with decades in clinical research put it plainly: the difference between rapid AI adoption and expensive failure is whether the organization applying AI has deep domain expertise or is starting from scratch with pure software solutions. This pattern shows up repeatedly across RCM billing startups, call center AI, clinical decision support, and drug development analytics.
AI amplifies existing operational acumen — it does not substitute for it. Organizations that have invested in clinical and operational depth get dramatically more from their AI investments than those chasing technology first.
The next frontier: agentic AI and generative models in clinical workflows
The conversation has already moved beyond diagnostic AI in imaging (though that market is still maturing). Generative AI agents are beginning to touch documentation, care navigation, prior authorization, and clinical decision support. The governance problem that already exists for imaging AI compounds dramatically when the AI is reasoning across broader data sets and more complex clinical scenarios.
The organizations that built governance infrastructure early — shared compute, unified integration layers, ongoing model monitoring — are best positioned to deploy this next generation safely. Those that adopted AI tools one-off, with separate contracts and data sharing agreements for each vendor, face a compounding security and governance debt.
Voices from the conversations
“My uncle died from lung cancer that was missed by his doctors over several years. That cancer could have been found with AI tools that were available and cost his doctors pennies to use. We have a healthcare system that struggles to adopt these technologies — and that lack of adoption has real costs. Millions of patients per year are receiving suboptimal care because their doctors don’t have access to the tools they need.”
Founder and CEO of a clinical AI governance company
“AI is the most transformational technology in medicine since the internet. My concern is automation bias — physicians who over-rely on AI outputs without maintaining their own clinical reasoning. We cannot allow AI to deskill the next generation of physicians. Physicians must lead adoption, not just receive it.”
Chair of Medicine at a major academic medical center
“Innovation labs that are disconnected from operational reality die. We start with high-volume, high-risk clinical use cases where the ROI is clear — not with what the technology can do, but with what the clinical problem actually is.”
Chief Medical and Innovation Officer at a large health system
Two decades into the value-based care movement — an honest accounting from the people who built it
The guests who built the value-based care infrastructure — former CMS administrators, CMMI directors, ACO architects — brought unusual candor to these conversations. Their perspective is not cheerleading. They see both what has genuinely worked and what remains structurally broken. Understanding the difference is the prerequisite for any organization trying to navigate these models today.
The ACA was the most transformational event in American healthcare — but the mission is unfinished
A CEO who has led health insurance at the highest levels showed acetate slides from 1991 that contained ideas about virtual healthcare, social determinants of health, and the human genome — and noted that healthcare had made “so little progress in so long.” The system is still fundamentally organized around break-fix episodic care rather than the human condition and longitudinal health.
The ACA expanded coverage dramatically and introduced value-based contracting at scale. But the delivery system transformation it intended to accelerate has been slower and more inconsistent than its architects hoped. The gap is not a failure of the idea — it is a failure of the implementation ecosystem: insufficient primary care investment, misaligned incentives that have been gamed rather than embraced, and a regulatory environment that simultaneously pushes innovation and creates barriers to it.
Model design is the most underestimated variable in value-based care
Former CMMI architects were direct about this: the difference between a value-based care model that works and one that fails is usually in the details of attribution, risk adjustment, and financial reconciliation — not in the headline concept. Beneficiary attribution models determine whether practices are accountable for the patients who actually generate costs or only for those they can influence.
The evolution from Medicare Shared Savings to Global and Professional Direct Contracting to ACO REACH reflects hard-won learning about these design details. Organizations considering advanced APMs need to treat model design as the core technical challenge — not a compliance exercise — and invest in the actuarial and data infrastructure to navigate it competently.
Primary care is the structural linchpin — and it remains structurally broken
Multiple guests described the same underlying dynamic from different angles: primary care is chronically underfunded, the least reimbursed, the most administratively burdened, and the most critical leverage point for population health improvement. Fee-for-service payment systematically undervalues primary care’s coordination function and rewards volume over health outcomes.
Value-based care programs that don’t address primary care economics first are building on a broken foundation. The physician groups and health systems making the most progress in value-based models are those that have invested specifically in primary care capacity and primary care physician satisfaction — before layering in attribution, panels, and shared savings.
The physician shortage crisis compounds this: forecasts of 80,000+ physician shortages are already materializing in access wait times. A healthcare system short on primary care physicians cannot deliver on the population health promise of value-based models, regardless of payment model design.
Health equity is no longer optional in payment model design
Health equity has moved from a programmatic add-on to a core design requirement of major CMS payment models. Risk adjustment now incorporates social determinants of health factors. Quality metrics explicitly measure performance disparities by race, income, and geography. Organizations that treat health equity as a separate initiative disconnected from their value-based care operations are structurally misaligned with where CMS and major payers are heading.
For Medicare Advantage plans specifically, this creates both an obligation and an opportunity: not-for-profit MA plans that genuinely address social determinants — housing, nutrition, transportation, social isolation — can achieve better outcomes at lower cost and reinvest those surpluses into member benefits.
Voices from the conversations
“States vary enormously in their approach to healthcare. When you try to use ChatGPT to build a state-by-state comparison on healthcare performance metrics — outcomes, coverage, spend, readmissions — the data it returns is simply unreliable. The tools aren’t yet there for this kind of policy analysis. We still need human expertise in the room.”
Former Director of the CMS Innovation Center
“TEFCA and interoperability are the foundation of everything else we want to accomplish in healthcare. Prior authorization reform is the single most important operational change needed for value-based care to scale. Without data flowing freely and without administrative friction being removed, we’re asking organizations to run a marathon with ankle weights.”
Former CMS Administrator and current health data executive
“Medicare Advantage must serve seniors’ social and clinical needs together — not as parallel tracks. The social determinants aren’t ancillary to the clinical work. For the senior population, loneliness, food insecurity, and lack of transportation cause more downstream health costs than most clinical risk factors we manage.”
CEO of the largest not-for-profit Medicare Advantage plan in the US
How physician group consolidation is reshaping specialty care — and what separates models that work
Cardiology, urology, orthopedics, oncology, mental health, dental — specialty care consolidation is reshaping every corner of the physician enterprise. The leaders building these models described a consistent set of dynamics: independent practices face unsustainable economics, consolidation creates leverage and infrastructure, but the physician equity model and the management philosophy determine whether the result is a high-performing care organization or a financial extraction vehicle.
Physician-led MSOs consistently outperform purely financial consolidation vehicles
The operators building large specialty MSOs were consistent on this: physician equity and physician leadership in governance are not soft preferences — they are operational prerequisites. Physicians who own a stake in the organization behave differently than physicians who are employees of a financially-led consolidation vehicle. They engage on quality metrics. They refer internally. They accept the trade-offs that come with building a sustainable practice rather than maximizing short-term income.
The MSOs that struggle — high turnover, deteriorating quality metrics, failed payer contracting — are most often those where the governance is purely financial, where the physicians feel acquired rather than partnered, and where the management layer between the physicians and the financial sponsors has insufficient clinical credibility.
The consolidation playbook is spreading from cardiology to every specialty
Cardiology and dermatology consolidated first, at scale. Urology is now following — the largest urology MSO in the US spans 14 states and 730+ providers, built on a model that combines specialist-level quality metrics with the infrastructure advantages of scale. Orthopedics, oncology, mental health, and dental are all in active consolidation phases with varying degrees of maturity.
Each specialty has its own dynamics. Oncology consolidation in the community setting — bringing specialist-level cancer care closer to where patients live — is driven primarily by the access gap between academic cancer centers and community practices. Mental health consolidation is driven primarily by the capacity crisis: there simply aren’t enough therapists to meet demand without technology-enabled scale. Dental consolidation is entering a new phase as dental-medical integration creates a whole-person care rationale beyond pure dental service delivery.
Employer healthcare is the next consolidation frontier
Self-insured employers spending $15,000+ per employee annually are being approached by navigation platforms that bundle surgery centers, specialty care, and benefits navigation into packaged offerings. This channel is increasingly bypassing the traditional insurance intermediary altogether for high-cost, high-variation procedures: joint replacement, cardiac procedures, oncology, and mental health.
For specialty MSOs, the employer direct contracting channel represents both an opportunity and a disruption. Centers of Excellence designations, bundled payment arrangements, and direct employer contracting reward quality outcomes and patient experience in ways that traditional fee-for-service contracts do not. MSOs with strong quality data and patient satisfaction metrics have structural advantages in this emerging channel.
The physician shortage is a designed crisis, not an inevitable one
A senior cardiologist and training program director was blunt: the 80,000+ physician shortage is the predictable result of deliberate policy choices — residency funding capped in the early 1990s, board certification requirements that function as a monopoly restricting supply, regulatory barriers to telemedicine that technically make it illegal under an expired 1998 Act. These are not forces of nature. They are policy choices that can be reversed.
The practical near-term implications for specialty MSOs: recruiting and retaining physicians in competitive markets is existential, not just operational. MSOs that invest in training programs, work-life structure, and reduced administrative burden have measurable recruitment and retention advantages. The organizations that will grow have solved the people problem, not just the financial model.
Voices from the conversations
“We have created a physician shortage by design. Board certification requirements function as a monopoly that restricts supply. Residency funding was capped in the early nineties and hasn’t meaningfully grown since. Telemedicine, which worked extraordinarily well during COVID, is technically illegal under a statute that expired and hasn’t been renewed. Anytime you see singular supply in healthcare — a single association defining certification, a single payer setting rates — you should tread carefully. Monopolies in healthcare harm patients.”
Senior cardiologist and physician training program director
Why healthcare’s most important resource — its people — is also its most acute constraint
The healthcare workforce crisis was not caused by COVID — the pandemic accelerated trends that had been building for a decade. What emerges from these conversations is a picture of a system that has systematically underinvested in the people who deliver care, overloaded them with administrative burden, and designed careers that are increasingly unattractive to the next generation entering the field.
One in three nurses planned to leave the profession after COVID — the structural causes have not been fixed
The nursing workforce crisis is a national emergency with clear structural causes: chronic underpayment relative to credential and responsibility levels, administrative burden from documentation and compliance requirements, staffing ratios that make safe and satisfying care delivery difficult, and a educational pipeline that is underfunded and capacity-constrained.
Nursing diversity compounds the access problem. Nurses from diverse backgrounds have been shown to improve patient outcomes for populations that have historically been underserved by the healthcare system. Diversifying the nursing workforce is simultaneously a moral imperative and a clinical quality strategy — but the pipeline investments required are long-term, and most health systems are focused on the short-term supply problem.
Administrative burden is the primary driver of clinician dissatisfaction
When physicians and nurses describe why they are leaving clinical practice or reducing their hours, the common thread is not patient care — it is the administrative system layered on top of patient care. Documentation requirements, prior authorization processes, credentialing, maintenance of certification, payer audit processes — these consume clinical time and psychological bandwidth that clinicians entered the profession to spend on patients.
AI has a genuine opportunity to address this — ambient documentation, automated prior authorization, smarter scheduling and panel management — but only if implemented in a way that actually reduces burden rather than adding a new layer of complexity. The health systems making progress here are those that involved clinicians in defining the problem before selecting the solution.
The residency bottleneck is policy-solvable but politically stuck
Graduate medical education funding was effectively capped in the early 1990s. The result is a structural constraint on physician supply that no amount of medical school expansion can overcome if residency slots don’t keep pace. Expanding residency funding requires congressional action — and the political will to act on a crisis that is visible in patient access data but abstract in political urgency terms.
The near-term implication: any healthcare organization that is not aggressively building workforce development programs, residency partnerships, and clinical pipeline investments is betting that the shortage will resolve on its own. It will not.
Voices from the conversations
“One in three nurses said after COVID that they were planning to leave the profession. This is a national emergency. Nursing education is underfunded. The clinical pipeline is capacity-constrained. And we know that a diverse nursing workforce improves outcomes for all patients — not just for underrepresented populations. We cannot separate the workforce investment problem from the access and equity problem. They are the same problem.”
President and CEO of the National League for Nursing
Perspectives from Europe, South Asia, the Middle East, Canada, and Africa on what works — and what America gets wrong
The Scale Community’s podcast deliberately reached beyond the US healthcare bubble. The global perspectives that emerged are not simply comparative curiosities — they surface structural choices the US has made that look very different from the outside, and models of care delivery, innovation, and financing that challenge American assumptions about what is possible.
Decentralized healthcare governance is a double-edged sword
A clinical data scientist who holds dual citizenship in Germany and the UK captured the tension precisely: the UK’s more centralized system (NHS, UK Biobank, UK Health Data Research Organisation) enables faster clinical research and cleaner national data infrastructure investments. Germany’s highly decentralized system, where each state maintains its own hospital networks and regulatory environments, creates richer diversity of approach but significantly slower consensus on innovation.
The US version of this tension is visible in the state-by-state variation in healthcare performance — variation that CMS has documented but struggled to address systematically. States that have made deliberate investments in data infrastructure, ACO penetration, and primary care — regardless of political orientation — consistently outperform states that have not.
Longitudinal data investment is the infrastructure that determines who leads in healthcare innovation
The countries and institutions that invested early in large, well-governed, consent-inclusive biobanks and clinical data repositories are today reaping disproportionate research and innovation advantages. The UK Biobank and Mayo Clinic’s data infrastructure were built with decades of patient investment — and they are now accelerating drug development, pandemic response, and clinical AI research in ways that cannot be replicated quickly.
Nigeria, Switzerland, and other countries are making deliberate decisions right now about whether to invest in sovereign health data infrastructure — not because it has immediate ROI but because the competitive and population health implications are generational. Healthcare systems that don’t make these investments are ceding the future of innovation to those that do.
Telehealth in emerging markets is not a workaround — it is the primary care system
In Pakistan, a founder built the largest telehealth platform in the country specifically by connecting women physicians who couldn’t leave their homes with underserved patients in a country with severe physician shortages. This is not a convenience model — it is the foundational care delivery model for hundreds of millions of people who have no realistic alternative.
The Middle East healthcare market, particularly Saudi Arabia, represents a generation-scale investment opportunity for international operators — but only for those who approach it as genuine market development and local community benefit, not as tactical revenue extraction. The operators succeeding there are building local equity, training local talent, and treating it as a 10-year commitment.
Innovation in resource-constrained environments produces disproportionate insights
Hospital Clínic de Barcelona has operated in the same physical facility for 120 years. Its constrained spaces force constant creative problem-solving. Its innovation program produces clinical improvements using VR headsets that cost near nothing, gamified rehabilitation exercises built into hospital corridors, and home care models that replace inpatient stays with remote monitoring. The insight is counterintuitive: resource constraint can accelerate innovation when paired with strong research culture and clinical ownership of the problem.
The contrast with US health system innovation is instructive. American health system innovation labs are often well-resourced but disconnected from clinical reality — they generate interesting pilots that never scale. European public hospital innovation tends to be lower-budget but higher-adoption, because the clinical problem and the solution are defined by the same people.
Voices from the conversations
“I tell every startup the same thing: I don’t care if you’re the best startup in the world. Unless it improves my operations significantly, it is not for us. In a public hospital, you need alignment from the medical director, nursing director, IT, legal, finance, and patient experience — all of them. Unless the value is undeniable, the solution doesn’t make sense for us. We have plenty of inefficiencies, and we know exactly what they are.”
Chief Innovation Officer of a major European hospital
“We built this platform for the women physicians who couldn’t leave their homes and the patients who had no realistic access to a doctor. In Pakistan, both groups exist at scale. Telehealth isn’t a feature for us — it is the entire model. It is the only way hundreds of millions of people get care.”
Founder and CEO of a South Asian telehealth platform
“COVID revealed that the global health financing architecture has a catastrophic gap between the WHO, which does the standards and surveillance work, and the World Bank, which does the long-term development lending. When a pandemic hits and you need $2 billion in 90 days to shore up health system capacity in 50 low-income countries, neither institution was built to move that fast. That is the gap the Pandemic Fund was designed to fill.”
Founding Executive Head of a global pandemic preparedness fund
Why 70% of patients can’t pay their bills — and the AI-powered solutions closing the gap
The revenue cycle is where the dysfunction of US healthcare financing becomes most visceral for patients. Bills arrive weeks or months after care. The amounts are unpredictable and often incomprehensible. Payment options are rigid. The result is a massive collection problem for providers and a source of genuine financial harm for patients. The entrepreneurs and operators addressing this space described both the scale of the problem and the tractability of AI-powered solutions.
Half of patient calls to healthcare practices don’t result in a booked appointment
A decade of AI analytics applied to front desk and call center phone calls has surfaced a striking finding: approximately 50% of inbound patient calls to medical and dental practices — calls from patients who are actively trying to schedule care — do not result in a booked appointment. The causes fall into three categories: the front desk or call center agent fails to convert an interested patient (sales/communication); marketing drove the wrong patient to call (lead quality); or the practice lacks capacity, takes the wrong insurance, or doesn’t offer the service (operational constraints).
The financial implication for MSOs and DSOs is significant. New patients have higher lifetime value and contribute disproportionately to same-store revenue growth. Every unconverted call is a lost patient relationship. AI that identifies where the conversion failures are occurring — by location, by agent, by marketing channel, by service type — enables targeted operational improvement that directly affects revenue.
Marketing attribution in healthcare is fundamentally broken — and AI can fix it
Google and Meta count any phone call over 20 seconds as a “conversion.” But a 25-second call where a patient asks about hours and hangs up is not a booked appointment. Healthcare marketing investment — across Instagram, Google, Facebook, TV, radio, and digital — is being evaluated against metrics that bear no relationship to actual patient acquisition.
AI call analytics that connect the marketing channel to the booked appointment, and feed validated booking signals back to ad platforms to improve algorithmic targeting, creates a compounding advantage: lower cost per acquisition, higher lead quality, and more accurate budget allocation across channels. The practices and MSOs using this approach are allocating their marketing spend based on which channels actually produce patients, not which channels produce calls.
Patient financing is a clinical access problem disguised as a billing problem
When 70% of patients cannot pay their medical bills in full, the downstream effects extend far beyond the revenue cycle. Patients delay or avoid care because of financial anxiety. They leave the ED before treatment. They don’t fill prescriptions. They don’t follow up after a procedure. The financial problem is a clinical problem.
AI-powered payment plans that assess a patient’s ability to pay and offer personalized, affordable installment options — without requiring manual negotiation or specialist billing staff — address the problem at the point of service rather than in collections. Collection rates improve, but more importantly, patient access to care improves. The organizations deploying this approach are finding that patient satisfaction scores move alongside collection metrics.
Voices from the conversations
“We started with a single hypothesis: patients are calling in, and half of them aren’t scheduling an appointment. The receptionist may be fumbling the ball. Marketing may be driving the wrong patient. Or the practice may not have capacity. After ten years and millions of analyzed calls, the answer is: all three. And most organizations have no idea which problem they’re actually solving, because they don’t have data.”
Founder and CEO of an AI-powered patient call analytics company
“Seventy percent of patients cannot pay their medical bills. That is not a collections problem. That is a healthcare access problem that presents as a billing problem. When we build payment plans that patients can actually afford — personalized to their financial situation, offered at the point of service — collection rates go up by 40%. But more importantly, patients don’t avoid care because they’re afraid of the bill.”
Co-founder and CEO of an AI patient financing platform
The mindset, decisions, and cultural shifts that separate organizations building the future from those managing the present
Threading across all 44 conversations is a portrait of what healthcare leadership demands in this moment: the ability to manage extreme operational pressure while investing in transformation, to maintain physician trust while introducing AI and automation, to navigate an uncertain payment environment while building toward value-based models that require years to mature. The leaders who stood out were distinguished not by certainty, but by a particular quality of honest, curious engagement with the complexity.
The best healthcare leaders share one trait: they lead with questions, not answers
A former auto engineer who became CEO of one of the country’s largest health insurers described his entire career philosophy as rooted in curiosity — spending more time with people asking questions than delivering answers. This approach led him, at Aetna and then at Oscar, to see connections that pure industry insiders missed: the relevance of social determinants, the potential of virtual care, the insufficiency of break-fix medicine. The price of expertise in healthcare can be a narrowing of the question space.
The leaders making the most progress with AI, value-based care, and workforce transformation share this quality: they ask “what is the actual problem here?” before selecting solutions. This sounds obvious. It is persistently rare.
Collaboration is not a soft preference — it is an operational necessity
Hospital Clínic de Barcelona operates four distinct strategic partnerships — with primary care networks, a regional hospital alliance, a pediatric partner, and international CAR-T licensing partners — and describes each as essential to delivering the level of care their patients need. The chief insight: “If we want to take good care of our patients, one hospital is not enough.” This is equally true for health systems, payers, physician groups, and technology companies.
In the US context, the organizations building sustainable value-based care models are those that have constructed genuine partnerships — not just contractual relationships — with primary care networks, behavioral health providers, and community organizations addressing social determinants. The organizations still operating as self-sufficient acute care entities are increasingly disadvantaged in population health performance.
Culture outlasts strategy — and innovation culture is built over decades, not quarters
The most durable examples of healthcare innovation culture described in these conversations — UCSF’s approach to clinical AI, Hospital Clínic’s 120-year research culture, Stanford Health Care’s partnership strategy — share a common trait: they were built through long-term consistent investment in research culture, clinical talent, and a willingness to fail at innovation in order to learn from it.
The organizations launching innovation labs, appointing Chief Innovation Officers, and accelerating AI initiatives in the current environment are making necessary investments. But the leaders who have built lasting innovation capability emphasized that the cultural work — creating psychological safety for clinical staff to identify and solve problems, connecting research to operational reality, celebrating learning from failure — takes years of consistent leadership behavior to develop.
Voices from the conversations
“I found a box of my old overhead projector slides from 1991. Virtual healthcare. Social determinants of health. The human genome project. I looked at them and thought: my God, we have come so little in so long. These things seemed radical when I gave those presentations. And we are still struggling with the very same issues thirty years later. The question isn’t what we know. The question is why we cannot act on it.”
Current CEO of a national health insurance company, former CEO of one of America’s largest health insurers
“You cannot mandate innovation. You can only facilitate it. What we have is a research culture — a hundred and twenty years of asking clinical questions and trying to answer them rigorously. That is the foundation. The innovation program, the awards, the startup partnerships — those are the superstructure. Without the culture, they produce nothing.”
Chief Innovation Officer of a major European academic hospital
“Digital transformation is 70% culture change and 30% technology. I have seen health systems with inferior technology platforms outperform those with best-in-class tools, because they had physician trust and clinical leadership aligned. And I have seen transformations fail with every advantage in the world except that one.”
Chief Digital Officer at a large integrated health system
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