For years, healthcare organizations have pursued IT cost optimization through familiar tactics: renegotiating contracts, extending hardware refresh cycles, consolidating vendors, and managing cloud spending. Those efforts can produce incremental savings, yet many organizations find themselves confronting the same financial pressures a few years later. The larger challenge is often architectural.
Increasingly, healthcare organizations are discovering that the design of their IT operating
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Health IT & Digital Health-Opinion | Op-Eds | Guest Columns | Analysis, Insights - HIT Consultant
Most Healthcare Is Local: Connecting Epic with Regional Health Information Exchanges
Imagine a patient who arrives at the hospital for a transplant evaluation. She’s been treated at three different health systems over the past two years, seen specialists across the region, and filled prescriptions at pharmacies in two states. Her oncology navigator needs a complete picture of her care history before tomorrow morning’s appointment. Some of that data lives in our EHR. A lot of it doesn’t.
This is the reality at a tertiary and quaternary referral center. Patients arrive from
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The Bad Information Doom Loop: AI-Enabled Marketing Is Only as Strong as Its Data Foundation
In the AI era, "garbage in, garbage out" is no longer a technical warning. It is a business reality.
AI can optimize campaigns, identify audiences and recommend where to invest the next marketing dollar faster and more effectively than ever before. What it cannot do is recognize when the underlying data is incomplete, outdated or inaccurate. It simply builds on the information it is given.
As AI becomes increasingly commoditized, most brands now have access to similar models, platforms and
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From Members to Ecosystems: How Medical Societies Can Steward Specialty Data in the Age of AI
Medical societies hold one of the most valuable and underappreciated assets in healthcare today: specialty clinical data.
Through years of quality reporting, education, and clinical collaboration, many have developed longitudinal, clinically grounded registries that reflect real-world practice. As demand for real-world evidence grows and new analytical capabilities emerge, these datasets are becoming more valuable and increasingly sought after.
Medical societies now face a clear and
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How Virtual Care and AI Unlock Outcomes-Based Autism Therapy
For years, autism therapy has forced healthcare purchasers into a false choice: expand access or control costs.
But as diagnoses in the U.S. have climbed to one in every 31 children, demand for care has outpaced the supply of Board Certified Behavior Analysts (BCBAs), the profession's most highly trained clinicians. Providers are responding by prescribing more therapy hours delivered by larger teams of lesser-trained clinicians, while payors continue to reimburse them for every additional
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America’s Elder-Care Crisis Has Arrived. We’re Not Ready.
For years, America has talked about the aging population as a problem we will need to solve somewhere down the road. That framing is outdated. The crisis is already here.
The population of older adults grew 13% between 2020 and 2024, far faster than the working-age population. There are over 61 million older adults in the United States. By 2030, every Baby Boomer will be at least 65 years old.
After decades in healthcare, I have seen how quickly demographic trends become realities
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Healthcare AI Governance: Moving from Data-Sensitivity Tiers to Reversibility Controls in Agentic Systems
Ashok Benial is right. His argument in these pages last week — that most hospitals are buying AI faster than they can govern it, that "the tools go live while the guardrails are still on a slide deck" — is the most useful thing anyone has said about healthcare AI governance this quarter. His three controls are the correct ones: validate locally against your own population, monitor for drift after go-live, and fund the human review layer as a control system rather than overhead. Every health
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Healthcare Doesn’t Need Forward Deployed Engineers. It Needs Forward Deployed Operators.
The forward deployed engineer model, embraced across enterprise technology, is built on a seductive assumption: that the gap between a technology's potential and its adoption is primarily a technical gap. A missing integration. A misconfigured workflow. A feature not yet built. Fix the code, and the organization will follow.
In most industries, that assumption is partially right. In healthcare, it is almost entirely wrong.
In part, healthcare organizations contribute to the problem
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Healthcare AI ROI Should Be Measured by Work Completed, Not Tasks Automated
Healthcare executives are being asked to approve AI investments through dashboards that emphasize activity: messages drafted, calls summarized, records reviewed and minutes saved. Those numbers show that a system is being used. They do not show whether the work reached a useful conclusion.
That distinction is easy to miss because healthcare workflows are divided into small steps. A patient message can be drafted while the request remains unanswered. A clinical note can be generated but still
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Healthcare AI’s Decision Intelligence Mandate: Turning Predictive Analytics into Timely Clinical and Operational Action
For years, healthcare leaders have asked if artificial intelligence can predict what will happen next, such as who might be readmitted, which patients could get worse, where resources are needed, and which interventions could help. Today, we can answer many of these questions, but a tougher question is what a healthcare organization should actually do with these predictions.
This is where much of healthcare AI still falls short.
The industry has invested heavily in electronic health
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