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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|

Risk Scores in Clinical Care- You’re Not From Around Here Are You?

These days, every care management / value-based care organization has a risk score to help target interventions. Unfortunately, these risk scores often frustrate clinicians by directing them to people who cannot benefit from an intervention – either the person is not actually headed for trouble, or the clinician already knew about that person. Why is that? It turns out most

By | August 28th, 2017|Analytics|

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|

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|

Cyft in the News: Healthcare Informatics Q&A Explores Analytics & Value-Based Care

With 30 percent of Medicare payments expected to be tied to alternative payment models and bundled payments by the end of 2016, the pressure is on for healthcare leaders still struggling to align their organizations with the idea of value-based care (VBC). This struggle is compounded further when one considers the push to utilize data analytics, machine learning, natural language

By | November 21st, 2016|Analytics, Company News|

Cyft CEO Speaks on Machine Learning At Big Data and Healthcare Analytics Forum

In case you missed it, Cyft CEO Len D’Avolio spoke yesterday at the HIMSS Big Data and Healthcare Analytics Forum in Boston. Healthcare IT News wrote a great piece encapsulating Len’s thoughts and those of his fellow presenters; all who sought to explain the potential impact of machine learning on healthcare. Consistent with our mission at Cyft, this technology could

By | October 26th, 2016|Company News|

WSJ Article Highlights EMR Frustrations: Analytics Companies To Answer the Value-Based Call

This week, a great op-ed published in The Wall Street Journal called “Turn Off the Computer and Listen to the Patient” brought a critical healthcare issue to the forefront of the national discussion. In 800 words, the authors (Dr. Caleb Gardner & Dr. John Levinson) succinctly describe the current frustrations physicians experience with electronic medical records (EMRs), in particular the

By | September 27th, 2016|Analytics|
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