Radiologists Need AI That Works Where They Work, Not Standalone Software
Current AI tools often increase the disruption that radiologists already experience from using non-integrated platforms.
Don’t Celebrate the Wrong Win: What CMS-0053-F Actually Changes
We aren't killing the problem that fax created. We are migrating it into a format that carries a higher expectation of automated processing — without yet having solved the underlying challenge that made automation hard in the first place.
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Top 7 Modern AI-Powered EAP Providers for Global Workforces in 2026
Discover the top AI-powered EAP providers for 2026. Compare platforms like Kyan Health and Spring Health on triage speed, global reach, and clinical quality to transform workforce wellbeing.
Healthcare’s Most Avoided Question: “How Much Exactly Do You Need, and What Will America Get in Return?”
Before policymakers, employers, insurers, or taxpayers commit ever-larger sums to the system, an uncomfortable question deserves attention: How much additional capacity does healthcare actually need and what measurable outcomes should society expect in return?
Why Generative AI Isn’t Enough: The Case for Causal Reasoning in Medicine
AI built on transparent, causal reasoning, systems that ground every output in validated biological mechanisms, show the pathway behind each recommendation, and cite their sources is what makes the difference between a tool that erodes clinical trust and one that rebuilds it.
The Hidden Cost of “Good Enough” in Healthcare Experiences
It’s not low effort or underinvestment. It’s the steady-state output of an organization succeeding at the wrong thing.
Have Healthcare Data Lakes Become “Data Swamps”?
They are vast, deep, and nearly impossible to navigate without the right clinical lens applied on top. The real challenge facing health data platform companies and health system data teams is what to do with all of it.
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The Power of Real World Data to Study Women’s Health at Scale
Veradigm examines key clinical trends, comorbidity profiles, and treatment trends across adolescence, reproductive years, and peri-/post-menopause. Download it today!
Why Most Healthcare AI Fails After the Pilot Phase
AI isn’t failing because it lacks capability. It’s failing because it isn’t consistently reaching the moments where decisions are made.
Keeping Up With Clinicians
AI adoption in healthcare isn't being led by innovation teams, it's the clinicians
Why Healthcare AI is Missing the Point
Organizations that are experiencing significant improvements related to AI are not necessarily using more advanced tools. They are just using the tools they have with more intention.
Why So Few Appeals Happen and What That Reveals About Health Plans
Appeals and grievances reflect how well a health plan functions under pressure. They also show how effectively your organization can surface, prioritize, and resolve the cases that matter most.
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Preparing Healthcare Networks For The Vulnerability Flood
We’re entering uncharted territory if bad actors can both launch zero-days and exploit unaddressed backdoors with new efficiency. This is why teams need to unify their network structure and better forecast what they’re up against.
Is Bias in Clinical AI Good or Bad? It’s More Complicated Than That
We do not need models that pretend every patient has equal or equitable access to care. We need models capable of recognizing disparities and responding to them.
Beyond the Pilot Trap: How Healthcare Can Scale AI Without Losing Trust
The future of healthcare depends on enterprises moving decisively beyond the pilot trap by treating enterprise AI as a platform capable of sustaining hundreds of dynamic models.
AI Won’t Fix Physician Burnout — Giving Them More Autonomy Will
AI documentation tools are genuinely useful. But they are treating a symptom, not the disease. The disease is that physicians have lost control over how they work.
The Human Behind the Machine: Why Healthcare Must Resist the Rush To Automate
The healthcare organizations getting this right are not defined by how much they’ve automated. They are defined by how deliberately they’ve protected the human touchpoints that drive trust, engagement, and outcomes.