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

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

By |2020-03-18T14:56:02-04:00September 25th, 2019|Machine Learning, Value-Based Care|

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

By |2018-08-01T12:13:00-04:00August 1st, 2018|Analytics, Machine Learning, Real World Applications|

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 -

By |2018-06-28T13:35:29-04:00June 19th, 2018|Analytics, Machine Learning|

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.

By |2017-11-22T10:14:32-05:00November 22nd, 2017|Analytics, Machine Learning|

7 Ways We’re Screwing Up AI in Healthcare

The healthcare AI space is frothy.  Billions in venture capital are flowing, nearly every writer on the healthcare beat has at least an article or two on the topic, and there isn’t a medical conference that doesn’t at least have a panel if not a dedicated day to discuss. The promise and potential is very real.And yet, we seem to

What Data Scientists Need to Learn to Work in Healthcare

Data scientists like Sid Henriksen, a Ph.D. student nearing graduation, often ask me how they can succeed in healthcare. With Sid's permission, here are a few questions and insights for aspiring healthcare data scientists. How applicable is generic data science in healthcare? The core data science skillset of machine learning, data visualization, and statistics is the foundation of working with all data, healthcare

By |2017-06-23T14:27:45-04:00June 23rd, 2017|Analytics, Machine Learning|

Hey Machine Learning…If That’s Even Your Real Name

Hey Machine Learning, I heard what Forbes said about your “setback” at MD Anderson. I also heard rumors going around HIMSS that maybe it’s “too soon” for you to be in healthcare. At first I thought, “serves you right.” There was so much hype that I could barely recognize you. Then I realized that, in a way, we’re all to