Everybody wants to be “AI-first” because the market is telling them they need to be. But what is AI-first? Is it AI-native? Is it software that has successfully threaded AI across its business? I’m not sure anyone can confidently answer the question, and it’s creating a lot of confusion in the market among founders, buyers, and investors.
The money is still flowing regardless – especially in healthcare. Rock Health reported that AI-enabled companies took 54% of digital health funding in
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Health IT & Digital Health-Opinion | Op-Eds | Guest Columns | Analysis, Insights - HIT Consultant
From “Maybe” to Baltic Gods: The Stories Behind Virus Names
Hundreds of new RNA viruses are discovered every year, but every discovery brings an unexpected challenge: what should the new virus be called? While some names describe where a virus was found or which species it infects, others draw inspiration from mythology, language, biology and even heavy metal music.
Among the viruses officially recorded in international genetic sequence databases are Galbūt ("Maybe"), Ūsinis, Barstukas, Pikulas and Patulas, all named by Lithuanian virologist Dr Gytis
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Beyond Pack-Years: Why Lung Cancer Detection Demands a Shift to AI-Driven Case-Finding
Most conversations about closing the lung cancer screening gap focus on uptake, and for good reason. Just 18.7 percent of eligible Americans were up to date with low-dose CT screening in 2024, roughly one in five, according to the American Cancer Society. The standard remedies follow: better outreach, more reminders, smarter scheduling, friction removal in the EHR. All of that is worth doing.
But it treats the eligibility criteria themselves as fixed, as the outer boundary of the
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The Silent Breakdown of Revenue Cycle Management
Healthcare organizations often already possess the information they need. The gap lies not in visibility, but in execution, in how quickly, consistently, and at scale they can turn that information into action.
Over the past decade, providers have invested heavily in dashboards, denial analytics, and performance monitoring. These tools have delivered on their promise. Revenue cycle leaders can now identify which payers have the highest denial rates, which claim categories pose the greatest
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Three Essential Principles for Evaluating Healthcare AI Vendors
Healthcare organizations are confronting an overwhelming number of AI vendors lately. Most products appear similar in presentations and slide decks, making it difficult for buyers to differentiate between product A and B. The real challenge — more than finding AI solutions — is building a selection process that scales as vendor volume increases.
The fastest path to a good AI decision is an intentional, structured evaluation process. Here are three things every healthcare executive
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Controlling AI Hallucinations: Building Evidence- First Trust in Medical Information and Medical Writing Workflows
Trust Is the Real Question
Can AI outputs be trusted? This is one of the most important questions healthcare professionals ask about generative AI. This question is especially important in medical information and medical writing because the value of a response depends on clarity and whether every statement is grounded in verifiable evidence and can withstand expert scrutiny. This matters because these workflows impact clinical judgment, scientific exchange, and regulated healthcare
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The Growing Disconnect Between Clinical Documentation and Reimbursement
Most documentation problems I have reviewed over the years were never really about documentation itself. They were workflow problems that showed up later as denials, CDI overruns, and revenue that quietly disappeared.
That is the honest truth the industry keeps avoiding. We keep buying tools to fix a problem that is not about tools. The gap between clinical documentation and reimbursement is not growing because technology is failing. It is growing because we keep ignoring how misaligned our
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Beyond AI Scribes: Why Ambient Clinical Intelligence Is Health IT’s Greatest Governance Test
Two years ago, ambient AI scribes were a mere curiosity. A handful of health systems ran the pilots. Conference demos drew crowds but not purchase orders. That phase is over.
Nearly two-thirds of U.S hospitals running Epic had deployed an ambient AI documentation tool by mid-2025 – 1,744 of them – according to a study published in the American Journal of Managed Care. Among the individual physicians, ambient documentation is the fastest-growing AI use case. Doximity’s 2026 survey of more than
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CMS’ Proposed Changes to Remote Monitoring Could Reshape Digital Healthcare Delivery
Over the past decade, remote patient monitoring (RPM) and remote therapeutic monitoring (RTM) have transformed from emerging technologies into integral components of chronic disease management. Fueled by advances in connected medical devices, wearable sensors, digital therapeutics, artificial intelligence (AI), and virtual care platforms, remote monitoring has expanded providers' ability to manage patients beyond the traditional clinical setting while supporting earlier intervention and improved
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Your Healthcare AI Strategy Has a Data Problem. And It’s an Interoperability Problem.
There is a familiar arc to healthcare AI initiatives. A promising tool is selected. A pilot launches with real enthusiasm. The early demo impresses everyone in the room. And then the project quietly fails to scale — and the post-mortem keeps surfacing the same culprit. It is rarely the model. It is the data feeding the model.
This is the uncomfortable gap at the center of healthcare AI in 2026. By some industry estimates, provider adoption of AI is on track to roughly double in just two
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