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Will Comparative Effectiveness Research Lead to Healthcare Rationing?

Will Comparative Effectiveness Research Lead to Healthcare Rationing?
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Author(s): Mary Brown (Capella University, USA)
Copyright: 2013
Pages: 21
Source title: User-Driven Healthcare: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-4666-2770-3.ch074

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Abstract

The Affordable Healthcare for America Bill that was signed into law in March 2010 includes support for activities that come under the heading of ‘comparative effectiveness’ research. The bill attempts to accelerate the conversion to electronic health records by all payers and providers who participate in the healthcare payment data stream. Conversion to electronic health data collection and storage solutions will create a large amount of treatment and payment data that is increasingly standardized by health standards organizations which reduces integration issues between technologies. There are federal advisory committees at work on designing the infrastructure needed to support a National Health Information Network (NHIN) that will support the healthcare data exchange required for comparative effectiveness research. The theory behind this work is that the availability of a large portion of existing health data will make it possible for researchers to identify therapies that lead to superior patient outcomes. It is assumed that the superior therapy would become the ‘best practice’ approach to treating a particular ailment. Supporters of comparative effectiveness see this as a strategy for making the system more effective both in terms of good medicine and also in terms of decreased cost. Opponents of comparative effectiveness see it as healthcare rationing and an inappropriate injection of government into the healthcare decision making process. Supporters and opponents have identified both positive and negative consequences to comparative effectiveness and this chapter will analyze the impact and propose some ways to optimize the results of this work.

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