Wayne R. Kubick
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The ongoing din of debate about health care reform and the associated transition to electronic health care records continues
to influence clinical research and regulatory strategies. It's hard to dispute the belief that these two worlds should converge—the
typical processes in clinical data collection are antiquated and redundant, and unnaturally separated from the process of
data collection within health care itself.
To some extent, recent demonstrations of health care data connecting with research cast a positive light on the use of data
standards in general—since standards are so fundamental to the harmonization of these parallel worlds of health care and clinical
research. On the other hand, it's a little premature to bet the ranch on a future utopia at the expense of other, albeit lesser
alternatives that may provide benefits that are already available today.
The vision for fully realizing the ultimate potential of health care standards centers on the nirvana-like goal of semantic
interoperability (or, more accurately, computer semantic interoperability—the ability for computer systems to unambiguously
exchange information and meaning). Wikipedia calls this "the ability of computer systems to communicate information and have
that information properly interpreted by the receiving system in the same sense as intended by the transmitting system." Too
many existing standards in common use are primarily syntactic in nature—they provide a way to represent the structure and
format of data, but do not sufficiently convey context or meaning. Thus, data sent in such a standard may consist of perfectly
well-formed sentences that don't make any sense to the person who receives them.
The lack of semantic interoperability has been a criticism sometimes made of CDISC standards. The CDISC Study Data Tabulation
Model (SDTM), for example, provides a way to represent the vast majority of commonly collected clinical trial data. But it
breaks down around the edges for questions not explicitly described in the SDTM Implementation Guide and especially for data
unique to a specific therapeutic area, leaving lots of room for creativity among individual implementers. A recent research
project that is seeking to repurpose and pool clinical trials data from a group of different sponsors in the hope of achieving
treatment breakthroughs for two catastrophic diseases illustrated the consequences of such creativity. Everyone was using
the same CDISC SDTM domain model, but somehow, none of the data quite looked the same or fit together in the same way. We can help close such gaps with experience and with increased effort on the content standards that would be used to populate
the SDTM—what some call the common data elements. CDISC is directly pursuing a project, CSHARE, to build a repository to collect
the metadata associated with the thousands of individual questions that can be asked on Case Report Forms—and, hopefully,
eventually find a way to harmonize the many different expressions of similar concepts.
And as such information is made available, reviewed, mapped, and put in use, and we begin to adopt the same set of questions
with the same terminologies within the same syntax, we can move in the desired direction toward semantic interoperability.
