Biomedical Engineering Reference
In-Depth Information
business partners would be appropriate. However, pharmaceutical companies
often send their compounds to many laboratories to be tested in numerous
assays, and all of that data must be imported into the assay database of the
client, and the data must be interpreted by chemists and biologists who are not
the operators of those assays. The further downstream the assay if the assay
was an in vivo assay, as opposed to an in vitro assay—the more complicated
the experimental design, and thus the harder it is for scientists to interpret the
data without being proximal to the biologist who performed the assay.
Both the chemistry and biology examples above highlight the cost and
complexity of exchanging data between business partners, and the activities
of data exchange and data harmonization are not value-added work for fi nding
drugs. These data tasks are a cost of doing business in life sciences, and as such
the industry is looking for ways to reduce these costs without impacting the
science. In fact, it could be argued that resources poured into the data activities
are actually diverting funds away from doing science. So, reducing these costs
will actually free up resources to do more science. The challenge of reducing
these data-curation costs is that no single entity, neither a pharmaceutical
company nor a contract laboratory nor a biotech, can accomplish what is
needed to be done, namely to harmonize across the industry. Point-to-point
optimizations of data exchange are helpful but only marginally cost effective.
For a paradigm shift to occur that would dramatically improve the effi ciency
of external science, the industry must come together to agree on common
methods of exchanging data, delivering services, defi ning entities, and so on.
Thus, a precompetitive collaboration among informatics groups is a natural
evolution in our industry. This evolution has already occurred in numerous
other industries, from apartments [7] to banking [8] to retail [9].
The nature of every industrywide data standardization effort revolves
around defi ning the terminology, semantics, metadata, entity attributes, and
services or functions of the data exchanged between business partners. These
defi nitions and attributes are collaboratively defi ned by IT or informatics
peers who together determine how to harmonize data between disparate
systems and processes.
1.3 OVERVIEW OF VALUE OF PRECOMPETITIVE ALLIANCES IN
OTHER INDUSTRIES
Other industries have realized the need for precompetitive alliances for some
time and have established them over the last two decades. This drive for col-
laborative alliances has been driven by the same pressures that the life science
industry faces today, that of increased pressures on effi ciency and the need to
divert funding to innovative activities rather than to commodity services. The
maturity of the business model for these other industries (telecoms, insurance,
automotive, and aerospace) has meant that they have existed prior to work
within the early stages of life science and informatics. These other industries
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