Complimentary from the publishers of Predictive Modeling News March, 2016 | |||||||||||
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“Using predictive
analytics, we can identify members who are likely to be non-adherent
with their medication regimens. The predictive models that include risk
factors beyond the traditional variables (i.e., age, gender, disease
prevalence, prescribed medications) and include medical events, disease
progression, socioeconomic factors and the member’s use of mail order
versus retail for his or her medications increase the strength of the
predictive model. In addition to including more refined risk factors,
classification models (e.g., clustering, support vector machine) also
open the door for improved analytics.” - Swati Abbott, Chief Executive Officer, Blue Health Intelligence. |
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"In fact,
automation can lead to cost reduction of 30% or more with a typical
implementation time of three months, according to NelsonHall, a
healthcare industry outsourcing analyst firm." Excerpted from: Predictive Modeling News, Volume 9 Number Number 3, March 2016, "Seven Steps to Making Your RPA Investment Work" |
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A Practical Approach to Controlling the Cost of Health Care Leavitt Group, February 29, 2016 |
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What is the
Relationship Between Precision Medicine & Predictive Analytics? HIT Consultant, February 22, 2016 |
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Bosses Harness Big Data to Predict Which Workers Might Get Sick Nasdaq, February 16, 2016 |
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Predicting the Economic
Impact of Changes to Population Health The Evidence Base, USC Schaeffer Center, February 16, 2016 |
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Big Data Analytics to Bring Billions in Healthcare Market Growth Health IT Analytics, February 12, 2016 |
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Race against time: Can analytics intervene in patient care? Health Data Management, February 12, 2016 |
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Delivering on the Promise of Healthcare Analytics: Troubleshooting,
Intelligent Design and Predictive Modeling Electronic Health Reporter, February 11, 2016 |
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