"Getting the data in" has often been cited  by authorities in clinical informatics as being among the most difficult challenges in successfully deploying clinical information systems. In particular, the costs of acquiring detailed and structured data from the clinical care process have been daunting. Voice and handwriting recognition information systems have not been broadly adopted for a variety of performance and usability issues. These cost and practicality issues will continue to present an obstacle to clinical information system utility and deployment until better solutions are arrived at. In contrast, the Human Genome Project has managed to achieve significant economies of scale in data acquisition in sequencing and expression measurement. Gene microarrays alone have dropped in cost by a factor of 10 in just the last 2 years.
Here again, once genomic investigators attempt to bridge the gulf from purely genomic data sets to phenotypically (i.e., clinically) annotated data sets, they will be confronted with the same challenges of clinically oriented, codified data acquisition. The questions of which user interfaces are the most cost-efficient, reliable, and generalizable to multiple clinical domains are among the implementation and design challenges that they will face. Although they have yet to arrive at definitively successful answers, clinical informaticians have already completed several decades worth of engineering and ethnographic studies [49, 50] addressing the very same questions.
We have personally witnessed the difficulties of some genomic studies in which the genotyping portion of the study was readily completed but the phenotyping was costly, slow, and ultimately rife with errors. This experience is likely to be increasingly common and therefore the need to solve the clinical annotation challenge is pressing.
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