Medical care and biomedical research are in the midst of a data revolution. Put together, networked systems, electronic health records, electronic insurance claims databases, social media, patient registries, and personal devices comprise an immense new set of sources for data about health and healthcare. In addition, these “real-world” sources can provide data about patients in the setting of their environments—whether at home or at work—and in the social context of their lives.
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Many researchers are eager to tap into these streams to provide more accurate and nuanced answers to questions about patient health and the safety and effectiveness of medical products—and to do so quickly, efficiently, and at a lower cost than has previously been possible.
But before we can realize the dramatic potential of the healthcare data revolution, a number of practical, logistical, and scientific challenges must be overcome. One of the first that must be tackled is the issue of terminology.
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Data
Data (raw measurements), add critical context, becomes information to be analyzed and combined to yield evidence.
Unfortunately, there are two very weak links in this process. What is "critical context"? It may be valuable input but, unfortunately it can also be preconceived bias. Likewise, "analyze and combine" can be arbitrary in application... if the data does not support (or refute, as the case may be) the theory, find the "mistake" and correct it!
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