Will Comparative Effectiveness Research Lead to Healthcare Rationing?

Will Comparative Effectiveness Research Lead to Healthcare Rationing?

DOI: 10.4018/978-1-4666-2770-3.ch074
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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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The Affordable Healthcare for America Bill that was signed into law in March 2010 includes resources and operational support for activities that come under the heading of ‘comparative effectiveness’ research. The high level concepts of comparative effectiveness research include the idea that it is possible, through analysis of existing transactional healthcare data, to determine which particular medication or therapy is most effective in treating a particular disease. Advocates point to the work being done on a limited and local scale in organizations, like the Mayo and Cleveland Clinics, as evidence of the ability to improve treatment while lowering costs through identification and application of medical best practices. Aston (2010) points out, however that it is equally possible that this research may discover that the most effective form of treatment turns out to be the most expensive form of treatment, in which case the quality of care may be improved but with an associated increase in cost.

Concerned practitioners express reservations that the practice of medicine includes so many variables and is so situational in nature that they need to have available to them all of the tools at their disposal. It is common sense to assume that if a particular medication is deemed to be superior by payers, who begin to limit their payment only to that particular medication, then manufacturers of competitive medications are not as likely to continue producing that product. What then is the recourse for those patients that are not fortunate to experience any benefit from receiving that particular medication? Is implementation of comparative effectiveness a cost saving measure that, over time, will reduce the role of physician judgment and will restrict the current model that includes a wide, diverse, and expensive approach to therapeutic treatment in a competitive economy? Webster (2010) identifies an additional concern that the comparative effectiveness research proposal included in the Affordable Healthcare for America law includes formation of a governing board that includes a 20% representation by industry causing concern in some that this creates a conflict of interest that may influence the decisions they make. It is possible that if clinicians perceive undue influence by commercial interests they will be less inclined to embrace any potential recommendations that might come out of comparative effectiveness research.

The healthcare industry has been using technology for several decades in their medical specialty departments to support diagnosis and treatment activities. The result has been an eclectic mix of cardiology, radiology, clinical laboratory and other medical specialty systems that supplement a paper chart which, for many healthcare organizations, is still considered to be the official medical record. Many healthcare clinics and clinician offices, unless they are associated with a larger healthcare organization, still run their practices using completely paper based or a combination of paper and locally installed practice management systems.

The more diverse the services that are offered onsite by a particular provider, the more complicated the technologies that have been deployed to support these clinical practices. For example, some hospitals began, in the 1970’s and 1980’s to explore transactional systems known as ‘Admission, Discharge and Transfer’ (ADT) systems designed to store customer demographic and billing information. These systems did not communicate to the medical specialty systems and the medical specialty systems did not communicate with one another. Integration of this data was handled through reports generated by these systems that were then printed onto paper and filed in the paper chart which was then considered the official medical record. There has been an interim step in some organizations involving the use of interfaces to transmit data between systems in order to increase data quality and reduce the amount of duplicate entry of patient information. The use of interfaces creates the need to find and hire skilled staff so they are available to create and manage the interfaces. Freedman (2007) describes the limitations of using interface engines and why they do not constitute true ‘interoperability’ in the healthcare environment.

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