Keeping Honest in Healthcare: Engineering Accountability into AI
When AI is honest and acts as a connector in healthcare workflows, clinician time is freed up, accuracy is ensured, and revenue is protected.
The Illusion of Diversion Data: Why Confirmed Diversion Counts Misrepresent True Risk
Confirmed diversion cases do not equal true prevalence - they are directly dependent on investigative proficiency, tooling, and the bandwidth of the teams doing the work.
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RCM Outsourcing: Balancing Human Expertise & Smart Technology
Greenway Health blends smart technology with human expertise to elevate revenue cycle.
We Have a Problem: Even Doctors Struggle to Navigate our Healthcare System
I lived for nine years with ALS while navigating a healthcare system that routinely obscured my own medical reality. But I know that pain is only amplified for individuals without medical expertise like mine.
Five Collaboration Trends That Shaped Healthcare AI in 2025
Relationships between health systems, non profits, universities and public agencies are not new. The difference here is a nuance, the structure of the relationships are what stands out in these collaborations.
Beyond the Buzzword: Why Semantic Interoperability is the Holy Grail for Digital Health
True interoperability is a prerequisite for patient-centered, efficient and scalable digital health solutions. And among the different types of interoperability, semantic interoperability stands out as the goal many are striving toward.
To Reduce Therapist Burnout and Improve Care, Turn to Longitudinal Data
Addressing the burnout demands integrated systems that lighten administrative burdens rather than compound them.
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Esperion Sets Its Sights on New Areas While Maintaining Commitment to Cardiovascular Health
In a recent interview, Esperion president and CEO, Sheldon Koenig talks about the reality of statin intolerance and his company’s expansion into new diseases
How AI Enables Unified Patient Journeys for Faster, More Accurate Rare Disease Diagnoses
Every year shaved off the diagnostic odyssey means fewer irreversible complications, fewer misdiagnoses, and fewer clinicians and families left wondering what they missed. A unified, longitudinal approach doesn’t just speed up diagnosis — it changes, and in some cases saves, lives.
Trends in Health IT – What Should Organizations Prioritize in 2026?
Data is fueling the AI revolution, but it must be aligned with solving business challenges.
Beyond the Numbers: Every Data Point Tells a Different Story
While data and cost are important elements in recognizing issues, the measure of success isn’t just about the data or the numbers; it’s about ensuring that older adults can manage and maintain their expectations about health and aging in place.
The Case for a Universal Medical Coder in Achieving True Interoperability
A universal medical coder, applied consistently across care settings, offers a practical solution to the enduring challenge of data integrity.
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MedCity News Pivot Podcast: How Emergency Departments Are Using Real-Time Data to Improve Outcomes
How can AI benefit and improve patient outcome in the emergency department? Watch the latest episode to find out.
The Hidden Data Errors Delay Healthcare for Millions of Americans
Network inaccuracy isn’t an inconvenience – it’s a public health crisis.
Bridging the Gap Between Data and Quality Improvement for Hospitals
Data only drives change when the right people see the right numbers at the right time and someone owns the follow-through.
AI Companies That Invest in Better Data Pipelines are Winning Faster Regulatory Approvals — Here’s Why
Medical AI developers who prioritize data early will be the ones crossing the regulatory finish line faster and more reliably.
From Silos to Synthesis: How the Information Renaissance is Transforming Emergency Medicine
In the shift from isolated expertise to collective insights, shared data are revolutionizing patient care in EMS and hospitals.
Bias Doesn’t End at the Dataset: It Lives in the Deployment
Equity has to be built into the deployment strategy from day one, not treated as a future retrofit. That means prioritizing inclusion not only in the data but in the delivery, and recognizing that inclusive deployment is the foundation for inclusive datasets.