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Medidata Institute: The Patient Equation

The Patient Equation:

The Precision Medicine Revolution in the Age of COVID-19 and Beyond

A Book by Glen de Vries

A Book by Glen de Vries

The Medidata Institute serves as a thought leadership forum to redefine how collaborative data, technology, and expertise can improve patient lives and inspire the next generation of treatments. Glen de Vries, co-founder and co-CEO of Medidata, expands on this vision in his new book The Patient Equation: The Precision Medicine Revolution in the Age of COVID-19 and Beyond.

Imagine a world where … data was harvested, analyzed, and combined with all of the medical records that are collected over the course of our lives and assembled into something useful, even to help alter the arc of a pandemic. This is the future, and the analyses behind the scenes producing these types of information are the ‘patient equations’ that inspired the title of this book.

We are at the intersection of biological and technological revolution, at a point where the digitization of health and medicine is becoming a reality. The next breakthrough cure or treatment that turns a fatal condition into a chronic disease—will come from computers and algorithms working in concert with patients, physicians, and scientists. And the COVID-19 pandemic will catalyze this change at an even faster rate.

The Patient Equation is built around examples of the latest devices taking clinical trials to the next level, arming doctors with the tools to transform how they treat their patients, discovering, testing, and marketing new breakthrough drugs more quickly and at lower cost, and how we can thrive in a world changed by pandemic threat.

Take a deeper look at some of the examples highlighted within The Patient Equation here.

Visit the Medidata Institute site to see more of Medidata’s groundbreaking research.

The Patient Equation is available for purchase on Amazon. Royalties from the sale of the book will be donated to Conquer Cancer, the ASCO Foundation.

Examples highlighted within The Patient Equation

Castleman Disease

(Chapter 8, p. 107)  Analysis of omic data with machine learning technology identified previously-unknown patient subsets and their therapeutic response to this rare disease’s only approved drug


(Chapter 11, p. 150) – An approach to a patient-centric trial assessing benefits and long-term effectiveness of aspirin dosing

Synthetic Control Arms

(Chapter 11, p. 162) – Leveraging advanced analytics and patient-level data from multiple historical clinical trials can mimic the results of a traditional randomized control, reducing patient burden associated with randomized controls

COVID-19 and the Impact of Clinical Trials

(Chapter 16, p.244) – Sharing data analyses and industry insights, like the Medidata Perspective, are key in understanding COVID-19’s impact on a global level in order to fuel public health action