Quality and Safety

The ability to actively manage data is the conduit to success in today's healthcare environment – maintaining engagement with frontline colleagues and surgeons is one key to success, says a physician admin at the East Coast health system.

Healthcare has too often been reactive. But a more forward-thinking approach, using tools and technologies for RPM, can help reduce readmissions, improve patient outcomes and enable success with value-based care models.

AEquity aims to improve training for healthcare machine-learning algorithms by assessing the accuracy and fairness of the data they're fed.

Educators collaborate with preceptors to identify knowledge gaps and create targeted content. Tangible benefits include reducing overtime costs and repeat in-person training sessions – nurses can now review materials when they wish.

Dr. Ethan Goh, executive director of Stanford ARISE, the AI Research and Science Evaluation Network, describes a new study to explore models' diagnostic and management reasoning capabilities – and what that could mean for clinicians and patients.

The AI models, paired with standardized care pathways, can be transformative in addressing readmissions and other high-impact quality metrics – but their success hinges on thoughtful design and implementation, cardiac quality doc says.

A legal settlement directs the U.S. Department of Health and Human Services to again make available data that healthcare stakeholders say is essential to designing interventions, tracking healthcare outcomes and allocating resources effectively.

Contracted ASX-listed Beamtree to help find solutions to improve health data quality across 20 hospitals.

It's eliminated two hours of daily admin time and three hours of daily manual work, a practice manager says. Patient no-show rates are now under 4.5%. Surgical procedures are also more efficient. The clinic PM tells the whole story – and offers tips others could learn from.

HIMSS Chief Scientific Research Officer Anne Snowdon says there are "thousands of reasons" AI tools are more readily adopted in other industries. But the biggest is that lives are at stake in healthcare. "We don't ever take that for granted."