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

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

Rethinking How to Measure “Risk” in Healthcare

A more technical post for those evaluating risk scores for care management, coupled with a real-world example. As healthcare organizations move toward taking on greater financial risk for keeping people healthy, it is critical for organizations to match people to the interventions they’re most likely to benefit from. This has traditionally been done by applying risk scores that are based on

By | September 5th, 2017|Analytics, Real World Applications|

The Dangers of Claims Based on Claims

Healthcare is notorious for its lack of consistent and widely adopted data formats. The one consistent exception is the billing information exchanged between payors and providers. These files are often referred to as “claims.” Because of their ubiquity, many of today’s analytical approaches - from epidemiology to public health, actuarial sciences, business intelligence, and risk scores - rely heavily,

By | August 21st, 2017|Analytics, Real World Applications|

Leading Institutions to Focus on Improving Type 1 Diabetes Care with Machine Learning

Today, June 12, 2017, Children's Mercy Kansas City, Joslin Diabetes Center, Cyft Inc., and The Leona M. and Harry B. Helmsley Charitable Trust are proud to announce the creation of a new learning health system to improve the care of individuals diagnosed with type 1 diabetes (T1D). Starting in mid-2017, Children's Mercy and Joslin will deploy machine learning-enabled solutions to

By | June 12th, 2017|Company News, Press, Real World Applications|

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

Three Peer-Reviewed Publications in 2016 Highlight Efficacy of Cyft Solutions

Peer-reviewed articles show clear benefits of machine learning for healthcare applications Cambridge, Mass – February 8, 2017 – Cyft Inc., a leading provider of machine learning solutions for healthcare, is the result of a decade of research into how machine learning and natural language processing technology can improve healthcare. Cyft is pleased to recognize the publication of three peer-reviewed studies

By | February 11th, 2017|Analytics, Company News, Patient Safety, Press, Real World Applications|

Dartmouth Researchers Detect Falls Without the Data Scientists

We learned that a team of health services researchers at Dartmouth College recently published a study in the Journal of Patient Safety using an early research version of Cyft to detect falls in inpatient notes.  No one on their research team is a data scientist and using a relatively small sample size they outperformed previous efforts at this task by

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