Analytics

It is gathering real-world evidence to inform treatment guidelines for severe asthma.

Incorrect, outdated or incomplete utilization data may not accurately reflect the underlying health risks and needs of disadvantaged populations, according to new research from the Terry Group, which offers strategies to address the challenge.

A new analysis examines how artificial intelligence in medicine can impact clinical decisions and identifies the steps that could build more trust in machine learning models from doctors and patients.

When technology neglects the needs of nurses, it can cause more problems than solutions, says chief nursing informatics officer at Frimley Health NHS Foundation Trust Kevin Percival.

The index uses machine learning-based similarity modeling and a data visualization tool to help compare and benchmark more than 2,000 acute care hospitals in the U.S.

Researchers at IU and Regenstrief received a 5-year grant to develop a population-based surveillance system using state EHR data; they hope to support population health studies at the national level.

The precision medicine initiative will use real-world data from the Learning Health Network for clinical validation and refinement of cancer risk prediction models.

Only three to four in 10 leaders are able to harness data and use AI and predictive analytics.

What do providers need to do better to protect against cyberattacks? A former healthcare CISO, now leading security strategies at Google Cloud, gives some advice.

Once validated, machine learning-based labor risk scores could be used in clinical practice to monitor labor in real time and improve maternal care, new Mayo Clinic research shows.