Multiple facets of a project need to be coordinated.

Activities should be synchronized with the objectives. This starts with adequately defining the objective. An objective defined as “collect clinical trial data” leaves the questions of what data and toward what end, which can result in collecting unusable data and repeating the expensive data collection process. An adequately defined objective will allow the creation of a data pipeline from collection to archive with statistical summaries, incorporating and directing all related activities.

It is important to synchronize definitions, whether within or between teams of people. One example is the equation used for Reduced Ejection Fraction, defined as either “EF < 50%” or “male EF<52% and female EF<54%”. Both are used in practice, but unless one definition is selected and communicated to everyone processing the data, results will not match. Worse than having to track down this problem is having to find the problem multiple times as staff changes. (See also Data Management.)

Clinical data collection often includes data from a new medical device and data from a gold standard, which needs to be matched for post processing and modeling. This matching will likely start with some kind of patient ID. The matching gets more complicated if each patient has multiple scans or recordings from the device as well as multiple gold standard data sets. Post processing will need to have these hierarchical data sets aligned.

In signal processing, there may be multiple signal paths within the system that need to be time-aligned. Each path may be optimized for a particular function. Farther down the pipeline, these separate signals may be combined or compared. If different processing delays are not taken into consideration or corrected for the application, unexpected or suboptimal results may occur.

We can identify areas of disconnect, then propose and implement methods to synchronize those facets of your project.

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