Health care risk adjustment and predictive modeling

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health care risk adjustment and predictive modeling

Healthcare Risk Adjustment and Predictive Modeling by Ian G. Duncan

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Published 15.01.2019

Risk Adjustment The Introduction

ISBN 13: 9781566987691

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Effective risk adjustment is an aspect that is more and more given weight on the background of competitive health insurance systems and vital healthcare systems. The objective of this review was to obtain an overview of existing models of risk adjustment as well as on crucial weights in risk adjustment. Moreover, the predictive performance of selected methods in international healthcare systems should be analysed. A comprehensive, systematic literature review on methods of risk adjustment was conducted in terms of an encompassing, interdisciplinary examination of the related disciplines. In general, several distinctions can be made: in terms of risk horizons, in terms of risk factors or in terms of the combination of indicators included.

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To effectively analyze healthcare outcomes and spending and appropriately manage clinical and financial risks, organizations need to be able to take into account the clinical complexity of each individual in a specific population. Risk adjustment is a statistical process that allows organizations to make fair comparisons of healthcare delivery and payment systems, and in so doing, identify opportunities for improvement. The gold standard in risk adjustment and predictive modeling, DxCG Intelligence analyzes and helps manage the clinical and financial risks associated with caring for populations, with specificity at the individual level.
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The training is role-based and uses case scenarios. No additional hardware or software are required for this course. Transformative health care delivery programs depend heavily on health information technology to improve and coordinate care, maintain patient registries, support patient engagement, develop and sustain data infrastructure necessary for multi-payer value-based payment, and enable analytical capacities to inform decision making and streamline reporting.

The book first introduces the topic with discussions of health risk, available data, clinical identification algorithms for diagnostic grouping and the use of grouper models. The second part of the book presents the concept of data mining and some of the common approaches used by modelers. The third and final section covers a number of predictive modeling and risk adjustment case-studies, with examples from Medicaid, Medicare Advantage, ACA Exchanges, ACOs disability, depression diagnosis and provider reimbursement, as well as the use of predictive modeling and risk adjustment outside the U. For readers who wish to experiment with their own models, the book also provides access to test datasets. Learn more.

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