Bridging Movement and Machine Learning: How Clinicians Can Harness AI in Practice
The true promise of AI lies not in autonomy of either the clinician or machine, but in a balanced collaboration that bolsters the strengths of each.
How AI is Unlocking Smarter Clinical Trial Protocols
By fine-tuning domain-specific models with real clinical operations data — such as historical performance, feasibility outcomes, enrollment patterns, and resource utilization — hidden information can be translated into structured intelligence.
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Top 7 Modern AI-Powered EAP Providers for Global Workforces in 2026
Discover the top AI-powered EAP providers for 2026. Compare platforms like Kyan Health and Spring Health on triage speed, global reach, and clinical quality to transform workforce wellbeing.
It’s Time for Technology to Lift Maternal Care
Improving maternal safety will not come from adding more data or more devices. It will come from building systems that help clinicians recognize risk earlier and communicate and act with confidence in the moment.
When AI Becomes a Payer Advantage, Hospitals Cannot Afford to Stay Reactive
If hospitals are going to stay ahead, revenue cycle leaders must shift from reaction to strategy. Here are some key areas of focus.
Is Your Healthcare Organization Ready to Implement AI?
A checklist for AI readiness from Nordic aims to help healthcare organizations avoid failure.
New Technology is Revolutionizing Clinical Trial Data Management — Here’s How Research Hospitals Can Seize the Moment
Options abound for hospitals looking to capitalize on these new data transfer technologies, but so do potential pitfalls. For any hospital considering adopting these new solutions, here are some of the key questions and considerations that should be top of mind.
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Inside an Automated Healthcare Practice: Redesigning Care Around People, Not Paperwork
By reducing administrative burden and redesigning workflows around human needs, it creates space for what matters most: connection between clinicians and patients.
AI for MSK Will Bend the Healthcare Cost Curve
Access to AI-powered digital MSK care options reduces wait times and allows patients to move directly into the right care instead of stair-stepping from hospital inpatient to primary care physicians to physical therapy.
Scaling AI in Health Systems: From Innovation to Sustainable Implementation
By adopting interpretation-driven, clinically intelligent technologies, revenue cycle teams can ensure that every nuance of care is accurately represented. This safeguards revenue integrity while maintaining the highest standards of compliance.
Medical Education Is at a Crossroads. AI Isn’t the Problem — It’s the Mirror
The real question is whether we're being honest with ourselves about what medical education was designed to produce, and whether our current system is still doing that job.
The Next Useful Layer in Radiology AI is Patient Comprehension
The first truly scalable and transformative use of AI in medical imaging may not be autonomous diagnosis. Instead, it may be the creation of a "translation layer" designed to help patients actually understand the complex information they are already receiving.
The Power of Real World Data to Study Women’s Health at Scale
Veradigm examines key clinical trends, comorbidity profiles, and treatment trends across adolescence, reproductive years, and peri-/post-menopause. Download it today!
The Hidden Million-Dollar Problem Lurking in Hospital Operations
As the industry continues to pour money into AI and other emerging technologies, a more disciplined era of healthcare technology investment is taking shape.
Employers are Scaling AI Use in Health Benefits, but Face Challenges
A new WTW survey found that employers are rapidly adopting AI in health benefits despite concerns about governance, resources, privacy and compliance.
AI Health Check: No Governance, No Trust
Will doctors or patients who are burned by one AI solution trust the next one they’re given? Probably not. That’s why every provider rolling out AI tools has to understand this risk and build governance into its development process.
What the Failure of Wearables Can Teach Us About AI
The takeaway from the current failure of wearables is that signal without synthesis doesn't change outcomes. If healthcare treats AI as just another way to collect or repackage data, it will repeat the same mistake.
AI Made Healthcare Smarter – The Next Step is Making It Simpler
A newly diagnosed person experiences healthcare as a complicated maze of physicians, specialists, pharmacies, insurers, deductibles, formularies, prior authorizations, benefit explanations and coverage rules that rarely speak to one another and often contradict each other. Better AI can help.