Digital Health Frontier

By John Halamka and Paul Cerrato - Cognitive errors challenge clinicians and technologists alike. Being aware of the types of mistakes that can occur is the first step toward fixing them.

By John Halamka and Paul Cerrato - The technology, which has been successful used in other industries, is slowly emerging as an innovative way to “clone” organ systems and genomic data to personalize patient care.

By John Halamka and Paul Cerrato - Measuring a long list of biomarkers with the help of wearables, lab testing, medical imaging, and genomic sequencing is the future of precision medicine. Creating these integrative personal omics profiles (iPOPs) has the potential to transform health care.

By John Halamka and Paul Cerrato - The pursuit of innovative digital tools is hollow if it ignores the core values that drive good patient care.

By John Halamka and Paul Cerrato - Software as a medical device continues to evolve with advances in machine learning. So do the federal regulations governing these digital tools.

By Paul Cerrato and John Halamka - One of the goals of our weekly blog is to explain, in plain English, how digital tools can improve patient care. This week we unravel convolutional neural networks and random forest analysis.

By John Halamka and Paul Cerrato - The evidence indicates it won’t solve this national epidemic but can serve as a valuable adjunct to professional therapy.

The agency’s draft guidelines cover a wide range of complex issues, including patients’ ability to handle remote monitoring devices and correctly record data, how to[...]

By John Halamka and Paul Cerrato- While many health care startups begin as daydreams in the minds of creative geniuses, entrepreneurs with the right blend of passion, observational skills, and imagination are the future of digital medicine.

By John Halamka and Paul Cerrato - In the second installment in the series, we explain how gradient boosting can help make patient care more precise and cost effective.

By John Halamka and Paul Cerrato - Many physicians ignore the recommendations provided by machine learning algorithms because they don’t trust them. A few imaginative software enhancements may give them more confidence in an algorithm’s diagnostic and therapeutic suggestions.

By John Halamka and Paul Cerrato - The potential benefits of AI-enabled algorithms have to be weighed against the risk of dataset shift, which can compromise their accuracy in ways that developers never anticipated.