March 20th 2019

Case study: How clinical analytics helps with meaningful use

Whether a healthcare organization is attesting to Stage 1 or Stage 2 of meaningful use this year, the process may not be entirely intuitive for physicians and other clinicians responsible for documenting patient care.  At Massachusetts General Hospital (MGH), physicians have a little bit of help from the hospital’s Queriable Patient Information Dossier (QPID), a clinical analytics engine developed at the hospital that provides actionable insights drawn from patient information stored in EHRs and data repositories, makingmeaningful use attestation just a little bit easier for harried clinicians.

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12 predictive analytics screw-ups

Make these mistakes and you won't need an algorithm to predict the outcome. Whether you're new to predictive analytics or have a few projects under your belt, it's all too easy to make gaffes. "The vast majority of analytic projects are riddled with mistakes," says John Elder, CEO at data mining firm Elder Research.

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Cable exec: See the big picture in evaluating big data opportunities

Mike Lurye has evaluated many of the software tools that have become nearly synonymous with big data. But the Time Warner Cable executive feels that some of the big data opportunities and technologies getting the most attention today have relatively little business value, at least for his organization's current requirements.

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Case study: How big data powers the eBay customer journey

With 50TB of machine-generated data produced daily and the need to process 100PB of data all together, eBay's data challenge is truly astronomical.

This deluge of data is helping eBay to emulate the know-how that customers used to get from a local shop owner; the only difference is, it is trying to achieve this across its global auction sites.

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Fighting fraud in near real time

Hungary’s OTP Bank gains competitive advantage via data visualization, fraud detection and social network analysis from SAS®.

When a Hungarian banking group began experiencing an uptick in fraudulent activity, it knew it needed a better way of spotting potential fraud before the credit was issued. “More sophisticated criminal techniques are making fraud detection and prevention much harder,” says Zoltan Zsolt Nagy, Head of the Analysis and Modeling Department, Credit Approval and Risk Management Division of OTP Bank. “We needed a solution that could return quick analysis on customer behavior reliably across multiple accounts and systems.”

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