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.
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.
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
Addressing the burnout demands integrated systems that lighten administrative burdens rather than compound them.
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.
Data is fueling the AI revolution, but it must be aligned with solving business challenges.
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.
How can AI benefit and improve patient outcome in the emergency department? Watch the latest episode to find out.
A universal medical coder, applied consistently across care settings, offers a practical solution to the enduring challenge of data integrity.
Network inaccuracy isn’t an inconvenience – it’s a public health crisis.
Data only drives change when the right people see the right numbers at the right time and someone owns the follow-through.
Medical AI developers who prioritize data early will be the ones crossing the regulatory finish line faster and more reliably.
Alan Murray on improving access for medical transportation.
In the shift from isolated expertise to collective insights, shared data are revolutionizing patient care in EMS and hospitals.
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.
Healthcare today requires more than the partial snapshot offered by legacy technology. By integrating diverse data sources such as wearables, claims, communications, and ADT feeds, clinicians gain a comprehensive, real-time view of their patients’ health.
AI-powered platforms can collect data sets from disparate sources and check them for quality, duplication, relevance, and more. These platforms can then create even more extensive, understandable findings to help healthcare professionals make decisions.
The way forward isn’t bigger models. It’s smaller, smarter ones. Small Language Models (SLMs) are designed to do what LLMs can’t: learn from enterprise data and focus on specific problems.