Decision Support

Researchers sought to assess and benchmark how physicians view electronic health record usability, as defined by a standardized metric, and use that to evaluate how poor UX correlates with feelings of job frustration and burnout.

Philips’ strong patient monitoring portfolio in critical care provides continuous monitoring for every care setting – from the point-of-care, to the unit central station, to a virtualised care provider or team.

A team of scientists trained a neural network to evaluate electrocardiograms to predict which patients were likely to develop an irregular heartbeat.

The companies say their approach to remote patient monitoring is uniquely modular and customizable, enabling provider organizations to implement pieces of an end-to-end system that best meet their particular needs.

In addition to gathering opinions on vendors including Health Catalyst, IBM Watson and Jvion, the research clears up some misconceptions about artificial intelligence systems and offers some best practices for safe and effective deployment.

At the HIMSS Asia Pac 19 Conference in Bangkok, Thailand in early October, Omar Sunna, Director, Global Product Management, GE Healthcare, shared his insights on some of the data and workflow-related challenges currently faced by healthcare providers and how some of GE’s solutions and applications can help tackle or address some of these issues.

Data from the accelerometers allowed researchers to correctly rank the mortality risk using 30-40 percent more accuracy than when using data about smoking status or a patient's stroke or cancer history.

Partnering with two firms focused on lab analytics and building a High-Value Care program, the health system within 15 months reduced unnecessary potassium tests by 63%, unnecessary magnesium tests by 40%, and unnecessary lipid panels by 40%.

Real-world evidence drawn from unstructured clinical notes was more accurate in algorithmic prediction of coronary artery disease than structured data, a new study in JAMIA shows.

Genomic medicine researchers at the laboratory have been using artificial intelligence, developed as part of Microsoft's Project Hanover, to help manage the vast amount of research data needed to power its precision oncology initiatives.