Last week I ended up at a fancy venue hired by a hopeful medtech company trying to impress potential investors with delicious food and an emotional story about their life-saving device. Pitching has become performance art. Founders deliver sharp decks, polished demos, bold promises of “AI-powered transformation,” impressive market sizes, and superior outcomes. What all these startups usually have in common is a near-fanatical belief in the importance, capabilities, and value of their product -
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The Womenʼs Health Blackout: How Defunding Gender Research Puts Patients at Risk Across Every Life Stage
Women experience mental health disorders at significantly higher rates than men. In primary care settings, 43% of women have at least one mental disorder compared to 33% of men, with particularly elevated rates of mood and anxiety disorders. Despite this higher prevalence, only 7% of healthcare research focuses on conditions that exclusively affect women.
This research gap leaves clinicians without clear guidance when treating women's mental health across different life stages. How does
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Preparing Healthcare Data for AI: Why Health Systems Must Fix Legacy Systems
Artificial intelligence (AI) promises to transform healthcare operations and decision-making. Healthcare alone now captures nearly half of all vertical AI spend – approximately $1.5 billion in 2025, more than tripling from $450 million the year prior and exceeding the next four verticals combined. Yet many organizations discover that their AI initiatives stall before they deliver value. The problem often isn’t the AI models themselves, but rather the data behind them.
For
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Why Hospital Dashboards Tell the Future But Operations Remain Stuck in the Past
Over the past decade, health systems have poured sustained capital and attention into data infrastructure. Enterprise warehouses consolidated fragmented reporting environments. Interoperability initiatives linked EHR instances that had operated in parallel for years.
Population health platforms brought predictive modeling into conversations about utilization and risk. Dashboards became ubiquitous, appearing in service line reviews, access meetings, finance updates, and clinical
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The Clinical Resolution Gap: Why AI Can’t Fix Broken MSK Care Platforms
For years, clinicians have complained about the same thing: they became doctors to care for patients, not to stare at screens.
The electronic health record inserted a keyboard into the most important relationship in medicine. Now, for the first time in decades, artificial intelligence (AI) is beginning to remove it. Ambient listening technology is giving providers back the focused attention that documentation demands have quietly eroded. That is not a minor workflow improvement. It is a
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Finding the Right Five Percent: How Machine Learning Is Reshaping Care Management
Population Health Has a Precision Problem
Population health programs continue to rely on blunt tools. Many risk stratification approaches emphasize historical utilization—basic risk scores or vendor-generated models that explain who was expensive—rather than identifying emerging clinical risk. These methods struggle to detect deterioration early enough to influence outcomes.
At the same time, care management teams face persistent resource constraints. Organizations cannot provide intensive
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The Perioperative AI Reality Check: Why Hospital Tech Fails Without Clinician Co-Design
When hospital administrators started talking about "AI-powered solutions" for perioperative care a few years ago, I was deeply skeptical. I'd watched too many technology promises fail to deliver. EHR modules that were supposed to streamline workflows but actually made them more cumbersome. "Intelligent" scheduling systems that didn't account for clinical realities. Patient portals that patients didn't use.
The problem wasn't the technology, it was that most solutions were designed by people
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The Invisible Implementation: Why Healthcare IT Needs to Shift from Vendors to Partners
In healthcare, implementation has a reputation problem.
Too often, onboarding a new vendor, transitioning a program, or launching a platform is associated with disruption: extra meetings, unclear timelines, competing priorities, and last-minute data requests that strain already stretched teams.
But the best healthcare implementations rarely feel disruptive at all — because the most effective teams design simplicity into the process long before kickoff begins.
Smooth implementations
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Voice Scams: When AI Calls Your Patients, Who’s Responsible?
Thirty-eight percent of Americans received a scam call in 2025 where someone was impersonating one of their healthcare providers. This is an eye-opening data point for healthcare executives, security leaders, and compliance officers challenged to stay ahead of increasingly sophisticated AI scams that even the most inexperienced bad actors can now rapidly and cost-effectively deploy.
Hospitals, health systems, and clinics were already on high alert when the American Hospital Association (AHA)
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Why Healthcare’s Safety Strategy Must Be Layered, Data-Driven, and Lead With Trust
Healthcare leaders no longer debate whether workplace violence is a crisis. The data is clear.
According to the U.S. Bureau of Labor Statistics, healthcare and social service workers account for nearly 73% of all nonfatal workplace injuries and illnesses due to violence. OSHA has repeatedly identified healthcare as one of the most high-risk sectors for workplace violence. Meanwhile, incidents of violence – including verbal abuse, threats, and physical assaults – are significantly
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