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Strategies in Biomedical Data Science by Ken Buetow, Jay A. Etchings

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Introduction

Never let the future disturb you.

You will meet it, if you have to, with the same weapons of reason which today arm you against the present.

—Marcus Aurelius

Some time ago, while I was engaged as a consultant, it became painfully obvious that the approaches to healthcare data management and overall infrastructure architecture were stuck in the Stone Age. While data and information technology (IT) professionals sprinted to remain on the cutting edge of top tech trends, much of the healthcare system remained a technical backwater. The many explanations for this include compliance controls, challenges associated with the rapid proliferation of data, and reliance on old systems with proprietary code where porting was more painful than the day-to-day operations. This state of affairs has been frustrating for all involved. But beyond the very real frustrations, there are far more important negative impacts. Technical inefficiencies increase costs, lead to a loss of research productivity, and hurt clinical outcomes. In other words, everyone suffers. When I talk to people about data management and IT support within the healthcare field, a recurring theme is that much is “lost in translation” between the various stakeholders: IT professionals, researchers, doctors, clinicians, and administrators.

Over the past 20 years, much of my time has been spent in medical and technical fields. I have held positions with two large insurance payer providers and have worked with the ...

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