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.
If hospitals are going to stay ahead, revenue cycle leaders must shift from reaction to strategy. Here are some key areas of focus.
By reducing administrative burden and redesigning workflows around human needs, it creates space for what matters most: connection between clinicians and patients.
AI can speed up drug discovery and decrease attrition rates in the clinic, but it is important to recognize that both are tall orders. The companies that will benefit most are those that stay grounded, set realistic expectations, and keep experienced scientists at the center of decisions that require genuine creativity and judgment.
A checklist for AI readiness from Nordic aims to help healthcare organizations avoid failure.
Healthcare startup exits are improving but remain highly selective. Jack Euston, general partner Fountain Health Partners, expects the next exit cycle to reward AI-enabled, workflow-embedded companies while sidelining undifferentiated point solutions and growth-at-all-costs models.
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.
Veradigm examines key clinical trends, comorbidity profiles, and treatment trends across adolescence, reproductive years, and peri-/post-menopause. Download it today!
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.
Signos raised $20 million to expand its glucose monitoring platform for weight loss, which pairs Dexcom’s over-the-counter CGM with an app that translates glucose data into personalized diet and lifestyle recommendations.
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 evolution in digital health is layering intelligent orchestration on top of the EHR to activate care in real time. It’s a platform plus orchestration layer, powered by AI and designed for action.
Enterprise EHR boosts scalability, interoperability, and governance for large healthcare systems.
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.
AI doesn't just demand better processes. It requires a complete redesign of traditional healthcare operations for a new level of precision, traceability, and governance. A new operating model that the vast majority of healthcare organizations simply aren't built around today.
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.
Bayesian Health became the first company to receive FDA clearance for a continuous AI-powered sepsis monitoring system. The clearance follows years of clinical validation studies and real-world deployments across multiple health systems.
As the industry continues to pour money into AI and other emerging technologies, a more disciplined era of healthcare technology investment is taking shape.