Where Healthcare Data Systems Fail and How to Build Them Better
Once data starts moving across multiple systems, it becomes hard to track unless you’ve been very intentional about it from the start.
Why Traditional CQI Breaks Down as EMS Agencies Grow
The problem isn’t that CQI no longer matters. It’s that traditional approaches were never built for the scale and complexity many agencies are now dealing with.
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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!
What Data Delays Are Costing Healthcare — And Its Patients
Timely, usable data closes care gaps and improves operations, but healthcare organizations lack access.
The Illusion of Visibility: Why More Data Doesn’t Mean Better Decisions
When every metric is treated as important, nothing actually stands out. Leaders spend more time interpreting data than acting on it. And in a system as complex as healthcare, delays in decision-making can have real operational and financial consequences.
Why Quality Shareback is the Missing Fuel for the Healthcare Interoperability Engine
The question is no longer whether shareback matters. It is whether the healthcare ecosystem is willing to prioritize it in practice by recognizing it in policy measurement, monitoring, and enforcement, as well as investing in the technical and governance structures needed to make shareback routine, high-quality, measurable, and reportable.
Wearable Noise Is Exploding — Trust Is the Filter
The challenge is determining whether wearable data is reliable enough to relieve the review burden, guide care, support reimbursement, or reassure a patient who is worried about their heart rhythm at two o’clock in the morning.
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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.
What Will Separate Healthcare AI Winners From Losers?
Uma (Veerappan) Nuggehalli of Flare Capital Partners thinks the healthcare AI startups that will come out on top will be companies that integrate seamlessly into workflows, build proprietary datasets and quickly determine how to sell their technology.
Artificial Intelligence and the Data Quality Problem No One Can Ignore
Healthcare is pouring money into AI, but poor data quality is quietly sabotaging results by scaling bias, errors, and mistrust instead of value. Until organizations fix historical data, set accuracy baselines, and keep humans in the loop, AI will multiply problems faster than it improves outcomes.
Privacy Expectations in Consumer AI Tools: How Patient Use of ChatGPT Health and Claude Differs From HIPAA-Regulated Care
If leading hospitals are using these AI tools, and the companies mention HIPAA compliance on their websites, are the consumer AI health tools also regulated by HIPAA? Do consumers share a similar relationship with these companies as healthcare organizations do?
Where the Patient Experience Is Heading – And 3 Ways Healthcare Providers Can Meet That Moment
Healthcare providers need to be thinking not only about the experience that patients need and want now, but the experience they’re going to need and want down the road.
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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.
Imaging Data Liquidity: The Foundation of Multimodal Medical Intelligence
Imaging has become a core input into how health systems understand disease, evaluate outcomes, plan capacity, and increasingly, how they learn.
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.
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.