Predictive Analytics Detect Heart Failure Patients

A new predictive analytic model will help identify patients at high-risk for hospitalization due to congestive heart failure (CHF).

The Humedica Predictive Analytics model is designed to enable providers to identify high-risk CHF patients before they have been hospitalized, helping to reduce projected hospital admissions. The model is more accurate and more timely than other predictive analytic tools that are based upon claims-based risk predictors, since it is based on clinical data pulled directly from the electronic medical record (EMR). The Software-as-a-Service-based (SaaS) analytics portfolio uses data collected on the Humedica MindShare platform and Humedica MindStream predictive clinical surveillance system, all products and services provided by Humedica (Boston, MA, USA).

“Predictive analytics powered by clinically-rich data is the future of health care and we are very excited to be on the leading edge of this innovation,” said Michael Weintraub, President and CEO of Humedica. “As health care providers move squarely into risk-based population health management, they need cutting edge systems that will help them improve patient care and lower cost. As one of the most costly and most preventable outcomes, CHF hospitalization is a great place to start.”

“We are leveraging the power of millions of patient experiences in our data warehouse, and are providing highly predictive capabilities that identify CHF patients who would likely end up hospitalized without prior medical intervention,” said Paul Bleicher, MD, Chief Medical Officer at Humedica. “Humedica is empowering clinicians to target specific groups of high-risk CHF patients and intervene before their condition worsens.”

Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, data mining, and game theory that analyze current and historical facts to make predictions about future events. The models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making. Predictive analytics is used in actuarial science, financial services, insurance, telecommunications, retail, travel, healthcare, pharmaceuticals, and other fields.

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