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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Health IT & Digital Health-Opinion | Op-Eds | Guest Columns | Analysis, Insights - HIT Consultant
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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Healthcare Payers Don’t Have an AI Problem. They Have an Infrastructure Problem.
Every day, payers deploy artificial intelligence (AI) into claims and prior authorization with the hope of eliminating delays, rework, and provider friction. Yet, too many are still seeing clean claims sent back for missing context, prior authorizations stalled over data that already exists, and a need for continued manual reviews because systems can’t agree. These disappointments stem from one root cause: payers are deploying AI as tools, not as infrastructure.
Without integration across
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The Missing Discipline in Healthcare Modernization
Modernization in healthcare is usually measured by what gets implemented next: an ERP platform replaced, EHR systems consolidated, a database moved to the cloud. But a successful go-live does not equal successful modernization. The full value of a new investment is only captured when the legacy system it replaces is permanently shut down.
The “shutting off” is where the work often breaks down. The team responsible for the new implementation is understandably focused on getting the replacement
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Why AI Finally Changes the Math on Fraud, Waste, & Abuse in Healthcare
The U.S. health care system loses more than $100 billion a year to fraud, waste, and abuse, and by some estimates several times that. We have known this for decades. What is new is that the technology to catch it has finally caught up. The question is no longer whether the tools work; it is whether the institutions processing and paying the faulty claims will finally use them.
What the system is actually losing
Three different problems get lumped together, so it is worth
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The Real Truth About Zero Data Retention: What AI Coding Tools Actually Promise in a HIPAA-Compliant World
A developer on your team is debugging a scheduling algorithm. They paste a function into an AI coding tool that references a patient's Medicaid eligibility window. Did that data just get retained? Logged? Used for training? For most engineering leaders, the honest answer is: they don't know.
That knowledge gap has a cost. IBM's Cost of a Data Breach Report found that 63% of organizations lack formal AI governance policies, and as AI tooling becomes embedded in everyday development
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