Cyft | Blog
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
Why IBM’s Layoffs Are Bad for Your Health
IBM sort of seemed like they were asking for it. From their claims to revolutionize cancer care in two years to their multi-billion $ growth projections, they worked real hard to project as healthcare's knight in shining armor. Of course, those inside of healthcare have seen many such knights burned to a crisp by the healthcare dragon -
Where Will AI Increase & Decrease Costs in Healthcare?
I asked LinkedIn friends to submit their questions related to AI in healthcare in preparation for an upcoming keynote at this year's HIMSS in Vegas. I promised to try to answer the questions they submitted. This one is courtesy of my friend, the great Aman Bhandari and it garnered the most "likes." Here's his question: "I have asked for this several times and haven't
Thoughts on JAMA’s “Adapting to Artificial Intelligence” by Jha and Topol
When journalists write about the disruptive power of artificial intelligence in healthcare they tend to zero in on radiology and pathology and for good reason. Both trades involve the interpretation of patterns from quantifiable image data - a thing that AI has proven highly capable of in several studies and commercial applications from facial recognition to the classification of hotdogs.