Digital Health Frontier Blog

By John Halamka and Paul Cerrato — Mayo Clinic’s Tapestry Study has demonstrated that next generation genetic analysis can have a significant impact on patient care.

By John Halamka and Paul Cerrato — Imagine if you could create a digital clone of yourself that can be used to test various treatment options to determine which one is best for your real self. 

By John Halamka and Paul Cerrato — Data scientists use a variety of coding languages to create AI-driven models, but the real “secret sauce” that helps them identify the best algorithms are the weights the coding generates.

By John Halamka and Paul Cerrato — Several of these digital tools are supported by strong evidence and are worth considering, not to replace your clinical judgement, but to augment it.

By John Halamka and Paul Cerrato — All the good things in the world worth believing, and among those good things are the therapeutic power of kindness and the healing effects of music.

By John Halamka, Paul Cerrato, and Sonya Makhni — How do you construct a safe, effective algorithm? It’s not an easy question to answer, but with a well thought out roadmap, it’s doable.

By John Halamka and Paul Cerrato — Generative AI has limitations, but with each quarter, performance and adoption are growing at an unprecedented rate. 

By John Halamka and Paul Cerrato — Even the most accurate algorithm is useless if it can’t be seamlessly implemented into a hospital’s workflow. Here’s a way to make that happen.

By John Halamka, Paul Cerrato, and Teresa Atkinson — Many clinicians are well aware of the shortcomings of LLMs, but studies suggest that retrieval-augmented generation could help address these problems.

By John Halamka and Paul Cerrato — Large language models rely on complex technology, but a plain English tutorial makes it clear that they use math, not magic to render their impressive results.