Information Theory and Translational Medicine: Maximizing the ‘bit rate’ of medical knowledge translation

Cabell Maddux


In information theory there exists a beautifully simple theorem - the Shannon-Hartley theorem - to explain the maximum rate at which error-free information can be transmitted over a particular communication channel. In short, the Shannon-Hartley theorem states that the capacity (i.e. what your cable/internet provider gal/guy might call ‘max bit rate’) of a given communication channel is proportional to the channel’s bandwidth and quality; a ratio of signal to noise on the channel. This theorem is vitally important to electrical engineers and networking specialists, not to mention the kind engineers at Twitter who want to ensure that the 1s and 0s representing each of our vitally important tweets reaches its destination even when we insist on simultaneously Facebook Live-ing ourselves binge-watching our favorite Netflix show.

At first glance, the Shannon-Hartley theorem appears to have very little direct consequence for medical researchers, front-line healthcare workers, and patients in need of quality care. However, a step back brings a clear medical knowledge translation channel from bench to bedside into view. The fundamental properties that determine the rate at which data is transferred from all over the world through copper wire and fiber optic cable into our homes, also apply to the communication channels that translate innovative and complex medical knowledge from researchers, through front-line health care workers, to patients at the point of care.

In this talk, we will consider the transfer of medical knowledge in the form of seminal research papers, detailed guidelines, and thoughtful commentaries from the medical research community, through the communication channel of physicians, nurses, residents, and medical students, to its destination with patients at the point of care. The system as a whole will be analyzed through the lens of the Shannon-Hartley theorem, and we will consider analogs for bandwidth, signal, and noise, in an effort to better understand what factors might support or restrict the flow of information through this knowledge translation pipeline. Finally, we will consider potential means for adjusting these variables to change the capacity of a particular medical knowledge translation channel in order to improve quality at the point of care.

The Shannon-Hartley theorem provides an interesting lens through which to analyze knowledge translation in medicine and inform potential changes to medical knowledge translation channels. While implementations in technology will continue to drive change in healthcare, the frameworks used by technologists to understand complex technological systems may be similarly applicable to difficult systems in medicine. More importantly, considering complex issues in medicine from the multiple perspectives of communities with differing expertise and experience, provides a fantastic opportunity to find creative solutions to difficult problems and promote better quality at the point of care. 
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