The Biggest Healthcare Fix Might Not Come From a Vendor
Nobody has ever needed convincing on what to fix. What they've lacked is a way to act on it themselves, in something close to real time instead of on a vendor's roadmap.
Healthcare Is Deploying AI Tools — It’s Not Ready for AI Colleagues
Most healthcare organizations are experimenting with AI. Few are preparing to manage AI agents as participants in everyday healthcare workflows.
Where Will AI Deployment Benefit Payers the Most?
We are taking a look at how health insurers are using AI, defining success, and managing cybersecurity risks. Give us your opinions by completing our brief, anonymous survey.
Ambulatory Growth Demands a New Operating Model
While organizations have invested heavily in expanding ambulatory networks, many are discovering that operational workflows have not evolved at the same pace. The result is a growing gap between demand and the systems designed to manage it.
AI in Drug Development is Not a Data Problem — It’s An Evidence Problem
Drug development isn't limited by the amount of data we collect, but by our ability to preserve the context, meaning and relationships that transform data into evidence.
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From Blind Spots to Better Outcomes: How SNF Data Transparency Strengthens LTSS
What if health plans could identify member decline before an avoidable hospitalization occurs? Real-time clinical visibility into long-stay SNF members uncovers risk earlier and drives better outcomes.
Rethinking How Hospitals Measure Behavioral Health Success
Hospitals that see improved long-term results in behavioral health patients will be those that prioritize metrics aligned with their care pathways.
Why Pharma Companies Should Look To Social Media For Better Patient Insight
Healthcare decision-makers, especially at pharmaceutical companies, must prioritize patient input. Data collected from social media listening is a significant step in the right direction.
Have Healthcare Data Lakes Become “Data Swamps”?
They are vast, deep, and nearly impossible to navigate without the right clinical lens applied on top. The real challenge facing health data platform companies and health system data teams is what to do with all of it.
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.
What Data Delays Are Costing Healthcare — And Its Patients
Timely, usable data closes care gaps and improves operations, but healthcare organizations lack access.
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Closing the Quality-Affordability Gap in Workplace Mental Health
In an interview, Kyan Health Co-Founder and Chief Commercial Officer Konstantin Struck discussed how Kyan gives mid-market and enterprise employers access to premium workforce mental healthcare, at a price point that is affordable.
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.
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.