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Predictive Modeling in Disease Management by Inc. HCPro

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Published by Opus Communications .
Written in English


  • General,
  • Internal Medicine,
  • Medical,
  • Medical / Nursing,
  • Computer simulation,
  • Disease management

Book details:

The Physical Object
Number of Pages150
ID Numbers
Open LibraryOL12245117M
ISBN 101578399750
ISBN 109781578399758

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Predictive modeling tools are used by disease management programs to risk-stratify members in order to optimize the utilization of available clinical resources. This paper provides an introduction Cited by: •Predictive Modeling is the process of estimating, predicting or stratifying members according to their relative risk. • Prediction can be performed separately for Frequency (probability) and Severity (loss). •Risk adjustment is a concept closely related to Predictive Size: 1MB. Solution-A Predictive Modeling Tool that: • Easy to use • Robust predictive modeling • Transparent and modifiable rules • Predictive modeling identifies – Those with opportunities for improved care based on evidence based care profiles – Those populations predicted to incur the greatest costFile Size: KB. Predictive modeling is a key technology to prospectively identify high-risk asthmatic patients and enroll them in care management for preventive care to reduce future hospital encounters.

To get a grip on the predictive healthcare revolution, one must begin with this book's comprehensive 26 chapters and 33 hands-on tutorials." --Eric Siegel, Ph.D., founder of Predictive Analytics World and author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or DieCited by:   Summary: Here’s an easy to understand example of how predictive analytics can reduce cost while increasing efficacy of disease management programs. Healthcare providers have made major breakthroughs over the last two decades by creating and implementing increasingly sophisticated disease management programs (DMPs). Machine learning can be applied in various areas like: search engine, web page ranking, email filtering, face tagging and recognizing, related advertisements, character recognition, gaming, robotics, disease prediction and traffic management,,. The essential learning process to develop a predictive model Cited by: 8. Predictive Modeling to Prioritize Patient Outreach Benefits of Predictive Modeling – Provide clinicians, healthcare managers, and hospice care management with an effective method to identify patients and families who may benefit from timely discussion of advance planning – Timely discussions leading to appropriate care near the endFile Size: KB.

or developing a particular disease or outcome (prognos-tic model). In clinical practice, these models are used to impact phase the ability of a prediction model to actu-ally guide patient management is evaluated. Whereas in This enhances applicability and predictive stability across Diagnostic and prognostic prediction models Cited by: Healthcare Risk Adjustment and Predictive Modeling Second Edition Learn Today. Lead Tomorrow. ACT it would become to transform massive data sets into predictive patterns and models. This book makes it even easier, laying out the analytical techniques that underlie the models and and how to. Healthcare Risk Adjustment and Predictive File Size: KB. predictive modeling, or health forecasting. In a population heath management context, these algorithmic tools predict which people are likely to get sick or sicker in the near term. This is crucially important information to provider organizations and health plans that take financialFile Size: KB.   Clinical predictive modeling involves two challenging tasks: model development and model deployment. In this paper we demonstrate a software architecture for developing and deploying clinical predictive models using web services via the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) by: