Cyft | Blog
End-to-End Healthcare AI Pipelining
The Cyft Supermarket Being able to build state-of-the-art models regularly is much harder and takes much longer if you don’t have a robust repeatable pipeline to process your data. This gets even more unwieldy if you’re trying to do that in a team, where one giant notebook doesn’t cut it. You also need a way to scale up to process
Progress and Barriers Bringing Palliative Care to Medicare Advantage Populations
Insights from the September 25th Medicare Advantage Learning Community As a nurse practitioner in palliative care, I am excited. The Center to Advance Palliative Care (CAPC) and End-of-Life Nursing Education Consortium (ELNEC) both celebrate their 20-year anniversaries this year. Today, almost three quarters of U.S. hospitals with fifty or more beds report having a palliative care team. ELNEC has trained over 24,000 nurses in train-the-trainer courses. Integrated health
The inconvenient truth about “The ‘inconvenient truth’ about AI in healthcare”
Drs. Panch, Mattie, and Celi recently published an article in Nature’s Partner Journal, Digital Medicine titled, “The ‘Inconvenient Truth’ About AI in Healthcare.” It’s a thought-provoking piece and I recommend taking the time to read it. They identify a huge problem in healthcare. But as brilliant as the three authors are, and as much as I hate to disagree with
Job opening: Vice President Engagement Management
When an organization's survival is dependent on keeping people healthy, they need to find ways to constantly improve their performance. Performance improvement has little to do with 'one size fits all' dashboards of mandated measures. That's why we formed Cyft. Our customers understand that improvement is a team sport, not something IT installs. It's facilitated by consultation
A Manager’s Guide to Making Machine Learning Work in the Real World
There’s plenty of coverage on what machine learning may do for healthcare and when. Painfully little has been written for non-technical healthcare leaders whose job it is to successfully execute in the real world with real returns. It’s time to address that gap for two reasons. First, if you are responsible for improving care, operations, and / or the bottom
It’s not really about “big data” in healthcare
The expression “big data” leads to some pretty reasonable assumptions: 1) you need huge volumes of data for machine learning and 2) more is more. Neither is particularly helpful in healthcare. We usually deal with smaller sets of rich but messy data (sample sizes in the hundreds or thousands). 10k rows vs 10M rows of claims data tend to