The Patient Equation Read online

  Table of Contents

  Cover

  Introduction Notes

  SECTION 1: From Hippocrates to Epocrates 1 Before We Cured Scurvy Nanometers to Megameters

  Scurvy

  The False Promise of Genotype

  Your Very Own High‐frequency Medical Device

  Notes

  2 Inside the Equations Like Lions of the Serengeti

  The Layer Cake

  Patients Like You, Patients Like Me

  Changing the Frequency

  Reverse‐engineering the Critical Layers

  The Cognitive Dimension

  Better Measurements at Virtually No Cost

  From Hypothesis Confirmation to Hypothesis Generation

  Notes

  3 Fitbits, Smart Toilets, and a Bluetooth‐enabled Self‐driving ECG Are Apps the New Snake Oil?

  Wearables for Panicked Dogs

  It's the Equations, Not the Devices

  Let's Start with the Thermostat

  Notes

  SECTION 2: Applying Data to Disease 4 Ava—Tracking Fertility, on the Road Toward Understanding All of Women's Health Enter Ava

  The Changing Role of the Patient

  The Motivation to Comply

  A Man Who Just Can't Ovulate

  Finding a Niche in a Crowded Field

  Notes

  5 One Breath, One Drop—Asthma and Diabetes, Chronic Conditions Being Conquered with Technology Out of the Danger Zone

  Lowering Barriers to Zero

  A Perfectly Artificial Pancreas

  Hacking One's Own Device

  One Drop at a Time

  Notes

  6 Flumoji and Sepsis Watch—Two Approaches to Predicting and Preventing Acute, Life‐threatening Conditions Through Smarter Data Catching Sepsis Earlier

  Partnering with Doctors, Not Replacing Them

  Looking Beyond Sepsis

  Using Crowdsourcing to Track the Flu

  Stopping the Spread of Illness with Data Is Hard

  Notes

  7 Cancer and Phage Therapy—Crafting Custom Treatments Just for You Changing the Way We Look at Cancer

  p53‐ologists of the Future

  Or Perhaps Car‐t‐ographers of the Present

  Personalized Immunotherapy Beyond Cancer

  Notes

  8 Castleman Disease—Not One Rare Disease with No Treatments, But Three Rare Diseases…with Hope, Thanks to Data Finding Clusters in a Random World

  Dr. David Fajgenbaum's Quest for a Cure

  Rare Diseases, Common Problems

  Notes

  SECTION 3: Building Your Own Patient Equations 9 The Steam Table Progressing Toward Alzheimer's Disease…or Maybe Not

  When the Measurement and the Therapy Are One and the Same

  Steam Tables for Cancer

  The Data Problem

  From Wellness to Illness—and Back Again

  Notes

  10 Good Data The Failure of Watson

  The Mars Climate Orbiter

  The Progression to Value

  Notes

  11 Changing Clinical Trials Expanding Access to Trials

  Pharma's Lack of Connection to Clinical Care

  Truly Patient‐centric Trials

  Accepting New Kinds of Data

  Unshackling the Clinical Trial

  Enter Thomas Bayes

  Breaking the Barrier

  Synthetic Control Arms

  Our Synthetic Control Model

  Making Every Trial an Adaptive Trial

  A Stroke of Insight

  Notes

  12 Disease Management Platforms The Promise of Mobile Apps

  Digital from the Beginning

  But It's Not That Easy

  Where That Leaves Us

  Notes

  SECTION 4: Scaling Progress to the World 13 The Importance of Collaboration A Tiny Island or a Larger Ecosystem

  How Data Collaboration Can Change the Game

  Notes

  14 Value‐based Reimbursement Beyond Survival

  The (Mathematical) Fountain of Youth

  Money‐back Guarantee

  Making Value‐based Care the Future

  Notes

  15 Aligning Incentives Human Doctors, Digital Doctors

  Respecting the Unquantifiable

  Empowered Patients

  Notes

  Conclusion Privacy and Transparency

  So What's Next?

  Notes

  Acknowledgments

  About the Authors Glen de Vries

  Jeremy Blachman

  Index

  End User License Agreement

  List of Illustrations

  Chapter 1 Figure 1.1 A multiscale view of health

  Figure 1.2 Data about you

  Chapter 2 Figure 2.1 A month in the life of a patient

  Chapter 9 Figure 9.1 A steam table

  Figure 9.2 Phase diagram

  Figure 9.3 A phase diagram for treatment choices

  Figure 9.4 Theoretical paths for neurodegenerative disease

  Figure 9.5 How I think about myself and cognitive impairment

  Chapter 10 Figure 10.1 Creating value from data

  Chapter 11 Figure 11.1 Collaborative Bayesian adaptive trials

  Figure 11.2 The virtuous cycle of synthetic controls, standard of care, and ...

  Chapter 12 Figure 12.1 Pre‐ versus post‐regulatory approval engagement strategies...

  Chapter 14 Figure 14.1 Quality of life over duration of life

  GLEN DE VRIES

  WITH JEREMY BLACHMAN

  THE PATIENT EQUATION

  THE DATA-DRIVEN FUTURE OF PRECISION MEDICINE AND THE BUSINESS OF HEALTH CARE

  Copyright © 2020 by Glen de Vries. All rights reserved.

  Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

  Published simultaneously in Canada.

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  Library of Congress Cataloging-in-Publication Data

  Names: De Vries, Glen, 1972‐ author. | Blachman, Jeremy, author.

  Title: The patient equation : the data‐driven future of precision medicine and the business of health care / Glen de Vries with Jeremy Blachman.

  Description: Hoboken, New Jersey : Wiley, [2020] | Includes bibliographical references and index.

  Identifiers: LCCN 2020000657 (print) | LCCN 2020000658 (ebook) | ISBN 9781119622147 (hardback) | ISBN 9781119622178 (adobe pdf) | ISBN 9781119622277 (epub)

  Subjects: MESH: Precision Medicine | Data Collection | Biomedical Technology | Monitoring, Ambulatory | Data Science

  Classification: LCC R856 (print) | LCC R856 (ebook) | NLM WB 102.7 | DDC 610.28—dc23

  LC record available at https://lccn.loc.gov/2020000657

  LC ebook record available at https://lccn.loc.gov/2020000658

  Cover Design: Wiley

  Cover Image: © teekid/Getty Images

  Introduction

  About a decade ago, I met Jack Whelan. An investment researcher working in the world of finance, Jack power‐walked from the train to his office every day for years…until he noticed that walk getting more and more difficult, along with occasional nosebleeds that prompted him to see his doctor. He was diagnosed with a rare blood cancer—Waldenstrom macroglobulinemia (WM)—and his world changed completely. WM was (and is) incurable, with no FDA‐approved treatments and an expected survival of just five to seven years. Wanting to extend his life, Jack sought out clinical trial after clinical trial. The first three trials failed, and then his fourth finally got him on a drug that stopped the cancer's progression for years.

  Throughout his experience, Jack became an expert—and, more important for our story here, a tracker. Jack demanded weekly blood tests and charted a range of biomarkers—hematocrit, immunoglobulin, and others—hoping to find answers in the numbers, to be able to know if he was responding to treatment even before the doctors did. From physician to physician, from trial to trial, he brought these numbers with him in Excel spreadsheets. He hoped this growing collection of data about his body would uncover new and valuable information that could keep him alive.

  Jack's diligence and initiative is rare, but he's not alone. Ray Finucane, a 75‐year‐old mechanical engineer with Parkinson's disease, built an app to track his symptoms and to try to optimize the dosing of his levodopa medication.1 Dr. David Fajgenbaum used his own blood samples and software to find a new plausible biological explanation for Castleman disease that led him to try a drug never before applied to the condition—which has led him into remission for the past six years. Millions of people around the world—sick or not—wear fitness trackers or carry smartphones that are able to track massive amounts of data at a far more granular level than we could have ever conceived of just a few years ago.

  Imagine a world where this 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, something to extend longevity and enhance the quality of life.

  Imagine if a patient like Jack didn't need to track his own body in an Excel spreadsheet because there were systems and devices that were doing it for him. What if he could do this while harnessing the collective wisdom of all of the research and real‐world experience of the scientists, physicians, and patients who came before him to produce recommendations for the most effective treatments, the most critical behaviors, and the most valuable things he needed to know to beat his cancer and optimize his health?

  Imagine a world where as soon as something useful could be detected, be it a data point we can track with conventional medical measurements or a misbehaving molecule or behavioral pattern that we may not even fully appreciate today (something about our sleep, our cognition, what we eat or drink, or an aspect of our environment, as just a few examples), we would be prompted to take action, use a medical device or drug, or change some aspect of how we live.

  Instead of being limited to waiting for a scan to show an increased tumor volume or a blood value concentration rising to a level detectable by a relatively crude chemical medical test, imagine real‐time, population‐tested, scientifically‐valid, difference‐making, actionable recommendations—whether you're fighting cancer or just trying to maintain a healthy, high‐quality life.

  This is the future, and the analysis behind the scenes that will produce—and is already producing—these types of information are the “patient equations” that inspired the title of this book. Jack was ahead of his time because he knew that the numbers and his careful tracking of them mattered. His engineering intuition told him that within those numbers were the mathematical keys to unlocking extended life, and making sure he got the right treatments at the right time.

  The world may not have been quite ready to put those numbers to use, but Jack was truly a trailblazer in realizing that many factors were relevant to his diagnosis and treatment, from his behavioral patterns (such as how tired he was when power‐walking) to otherwise insignificant medical events (like a nosebleed). He understood that tracking his biology more proactively and frequently than would be done in standard‐of‐care medicine could make a difference, and that his own patient equation encompassed far more variables and inputs than many of us would suspect.

  Jack passed away in late 2017, almost 10 years after his initial diagnosis, and spent the last years of his life as a speaker, a research advocate, and a fighter pushing for greater patient involvement in clinical trials and greater collaboration between the life sciences industry, the doctors on the front lines of treatment, and the patients ultimately receiving care. He knew that collaboration would be critical for achieving the future state of patient care that I'm describing, and for uncovering the business models that would make it all possible. This book is about explaining that future, how it's not as far away as it seems, and how everyone involved in the mission and business of life sciences, in providing health care—or, like Jack, merely hoping to live a longer, healthier life—needs to understand the power and promise of the revolution in data and analytics that we're currently experiencing.

  We are in a race to the holy grail of precision medicine—bringing the right treatments to the right patients at the right time. Progress is being made on so many fronts—life sciences companies are developing cell therapies in cancer, artificial pancreas device systems in diabetes, apps that help battle neurodegenerative diseases and optimize nutrition, and wearables that can track everything from heart disease to fertility. Technology companies are creating algorithms to select cancer treatments. Hospital systems are implementing decision support systems to help physicians and patients evaluate options for therapy. But it's a disjointed landscape, and so much of what we're aiming for is still in a black box.

  We know intuitively—like Jack—that the answers are there, and, more and more, we are amassing the data and developing the analyses to fill in the gaps of our knowledge and make that black box transparent. It's in many different places (from the phones in our pockets to the medical records at hospitals and the clinical trial data used to approve drugs and devices by the FDA). It's not always well‐organized, standardized, or easy to work with. But it exists. And for the first time in history, we're organizing it, making it accessible, learning how to analyze it, and creating new benefits from it every day.

  The magic is in the algorithms behind the scenes, and how they translate all of those inputs and all of that data into actionable information. These are the equations that will impact us all, mapping every condition that affects or could affect our lives (and every therapy that exists along with those yet to be invented), with unprecedented accuracy.

  When the smartes
t and most well‐informed patients get sick, they look for experts—doctors who have seen their condition before, and who have vast stores of wisdom and experience to apply. They put together care teams, hoping that someone's intuition will combine with the particulars of their disease, an understanding of the treatments out there, and, perhaps, a bit of luck, to lead them to the best path forward. Patient equations are going to turn that intuition into mathematically‐reliable insights—and bring those insights from the halls of major medical centers and top life sciences companies to patients of all demographics around the world.

  We are at the intersection of biological and technological revolution, at a point where the digitization of health and medicine is becoming a reality at the same time that medical innovation is catching up with—and possibly even exceeding the speed of—growth in computational capacity. Moore's Law, famously stating how the computational capacity of computer microchips doubles every two years, is being outpaced by how quickly we can sequence genes.2 The convergence of these digital and biological revolutions means that 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.

  Pretty soon, if you're a life sciences executive, you won't just be launching your clinical trials to develop your next drug or device, but you'll be tapping into a set of data never before available to help you ensure that what you're developing is best equipped to help patients and improve your bottom line. If you're a health care provider, you'll be relying on more than just broadly applied standards of care to find the best treatments for individual patients. And if you're a patient yourself, you'll have so much more insight into your health, now and into the future, than ever before.