Thank you to my parents, Madeline, Alan, Judy, and Ian, whose inspiration, influence, and voices all exist herein.
Finally, to my friends, and to my fiercely loyal sisters, who have tolerated and encouraged my attention to science, technology, and Medidata for all these years: Katie Sue, Jesse, Uri, Mike, Adam, Steve, Andy, Michael, Yukiyo, Seijiro, Valéry, Maria, Padma, Katie, and Lizzy (and again to Tarek, who I have now shared an office and a business with for over 20 years): I love you guys!
About the Authors
Glen de Vries
Glen is the co‐CEO and co‐founder of Medidata Solutions, a Dassault Systèmes brand, the leading cloud platform for life sciences research. He has been driving Medidata's mission of powering smarter treatments and healthier people since the company's inception in 1999. He received his undergraduate degree in molecular biology and genetics from Carnegie Mellon University, worked as a research scientist at the Columbia Presbyterian Medical Center, and studied computer science at New York University's Courant Institute of Mathematics. Glen's publications have appeared in Applied Clinical Trials, Cancer, The Journal of Urology, Molecular Diagnostics, STAT, Urologic Clinics of North America, and TechCrunch. Glen is a trustee of Carnegie Mellon University, a Columbia HITLAB Fellow, and a member of the Healthcare Businesswomen's Association European Advisory Board. Follow Glen on social media at @CaptainClinical.
Jeremy Blachman
Jeremy Blachman is a writer who works with leaders across industries on getting their ideas out to the world. A graduate of Princeton University and Harvard Law School, he is also a twice‐published novelist—Anonymous Lawyer (Henry Holt) and The Curve (Ankerwycke, co‐authored with Cameron Stracher)—and screenwriter, having developed both of his novels as television pilots for NBC. His writing has appeared in the New York Times, the Wall Street Journal, and many other publications. Visit his website at jeremyblachman.com.
Index
Page references followed by fig indicate an illustrated figure.
A/B testing, 35
Acute lymphoblastic leukemia (ALL), 210
ADAPTABLE clinical trial, 150–151
“Alarm fatigue,” 84
Alector, 99
Alphabet, 233
ALS (Lou Gehrig's disease), 108
Alzheimer's disease apps trying to distinguish memory issues from, 125
biomarkers' potential to diagnose, 20, 52, 155, 212–213
building patient equation for, 119–125
drug development to fight off, 99
gathering data on, 40–41
offering interventions for, 61
theoretical paths for neurodegenerative disease, 121–123
See also Cognitive impairment; Dementia; Diseases
Amazon, 35, 195
Amazon Alexa, 54
American Healthcare Leader, 83, 85
American Heart Association, 170
American Medical Association, 223
American Society of Clinical Oncology, 176
Apple Apple HealthKit app, 176, 209
Apple HomeKit app, 54
Apple Watch, 30, 46, 76, 153, 195, 233
health care research by, 195
iPhones, 51, 195
Applied Health Signals (Livongo), 228
Apps Apple HealthKit, 176, 209
Apple HomeKit, 54
Babylon Diagnostic and Triage System, 45
BlueStar, 177
brain training, 126
Cardiogram, 45
debate over medical value of, 45–46
to distinguish memory issues from Alzheimer's‐like dementia, 125
Flumoji, 88
Migraine Alert, 45, 46
MoovCare, 177
Noom app‐based diet and life coaching tool, 211
OneDrop, 75–77, 175, 180, 186
reSET, 177
Trak, 66
Waterlogged, 175, 176
See also Digital technologies; Smartphones; Wearables
App Store, 195
Artificial intelligence (AI), 85–86, 219, 226
Artificial pancreas FDA warning on hacking, 74
as solution to diabetes, 72–75
Aspen Ideas: Health, 99
ASSIST (Advanced Self‐Powered Systems of Integrated Sensors and Technologies) [North Carolina State University], 70
Asthma comparing diabetes to, 69–70
wearable aimed at eliminating attacks, 71
The Atlantic, 211
Automated Decision Support system (OneDrop), 75–77, 175, 180, 186
Ava ovulation‐tracking bracelet, 60–63, 64–67, 70, 71, 219
Babylon Diagnostic and Triage System app, 45
Babylon (UK), 45
Bach, Dr. Peter, 211, 212
Basal body temperature, 63
Battery technology, 48, 51
Bayesian methodologies collaborative Bayesian adaptive trials, 158fig
combined with synthetic control, 168–170, 198
description of, 156
I‐SPY 2 breast cancer study, 157–162, 197
Bayes, Thomas, 156
B‐cell acute lymphoblastic leukemia, 98
B‐cell lymphomas, 210
BCR‐ABL fusion, 127
Becker's Hospital Review, 134
Behavioral data combining genetic information with, 13–14
multiscale view of health including, 6fig
Bernard, Charlès, 237
Berry, Dr. Don, 156–159, 197
Beta‐amyloid plaques, 119–120
Biden, Joe, 94
Big Data, 76
Bill & Melinda Gates Foundation, 101
Biomarkers collaborative Bayesian adaptive trials on, 158fig
continuous vs. discrete points measuring of, 39–40
description of, 17, 18
digital technologies measuring medical, 29–31
PSA (prostate‐specific antigen), 18–19, 115–118fig, 120–121
testing for, 18–20
See also Measurements
Biospecimens, 17–18, 20–21
Blood pressure AI model to predict hypertension, 86
DeepHeart (algorithm) prediction of high, 45
Bloomberg terminal, 31
Bloomlife, 66
Bluebird Bio, 211
BlueStar app, 177
BMC Infectious Diseases, 87
BrainHQ app, 126
“Brain training” memory game (Lumosity), 126
Breast cancer study (I‐SPY 2 model), 157–162, 197
Cambridge Cognition, 125
Cancer Moonshot project (NCI), 94, 126
Cancers application of data to treatment of, 22–23
changing the way we look at, 94–96
colon cancer screening, 85
complexity of genes and, 13
complexity of the disease, 93
glioblastoma (brain cancer), 161
interaction between other diseases and, 129–130
p53 mutation and susceptibility to, 20, 96–97
prostate, 18–19, 21, 115–118fig, 120
TP53 gene in DNA causing, 127
triple‐negative tumors, 94
See also Diseases
Cancer treatments for B‐cell acute lymphoblastic leukemia, 98
IBM's Watson failure, 44, 47, 133–134, 219
I‐SPY 2 breast cancer study on, 157–162, 197
Keytruda, 98–99, 155
Kymriah, 98, 210, 211
phage therapy applied to, 93–94, 99
proteomics approach to, 94
value of collaboration to develop, 194
See also Treatment/clinical care
Cardiogram app, 45
CardioNet, 184
Carson, Joy, 98
Carson, William, 194
Castleman disease data management role in treating, 107–108
description and traditional treatment of, 106
Dr. Fajgenbaum's work fighting, 102, 106, 107–110, 140, 153, 170
idiopathic multicentric Castl
eman disease (iMCD) form of, 106–109
implications for other diseases, 111
Castleman Disease Collaborative Network, 106, 153
Center for Genomics and Personalized Medicine (Standard University), 232
Centers for Disease Control and Prevention (CDC) “Flu View” report, 86, 86–87
Chasing My Cure: A Doctor's Race to Turn Hope into Action (Fajgenbaum), 107
Cheek, Julia, 224–225
Chronic myeloid leukemia, 127
Clinical care. See Treatment/clinical care
ClinicalTrials.gov, 145, 146
Clinical trials accepting new kinds of data, 152–154
ADAPTABLE, 150–151
advantages of Bayesian methodology for, 156–170
expanding access to, 144–147
GBM AGILE, 161–162
insights into adaptive designs for, 170–172, 196
I‐SPY 2 breast cancer study, 157–162, 197
making them truly patient‐centric, 149–152
synthetic control, 162–168, 198
typical phase III, 149–150
See also Drug development
Cognitive data building patient equations using, 119–125
multiscale view of health including, 6fig
Cognitive factors, 34–36
Cognitive impairment building patient equations for, 119–125
factors involved in treating, 123–125
false “brain training” memory game to slow, 126
See also Alzheimer's disease; Dementia
Collaborative data to accelerate the value of research, 197–201
to become part of larger digital ecosystem, 195–196
Colon cancer screening, 85
ColonFlag system, 85, 89
Columbia Business School, 214
Columbia Presbyterian Medical Center, 19
Columbia University, 119, 120, 137, 161
Consumer Electronics Show (CES), 46
Costello, Anthony, 150
Cowen, Tyler, 43
Crick, Francis, 4, 10, 14
Crowdsourcing to track flu, 86–87
Cue Health, 233
CURE magazine, 145
Cyrcadia Health, 94
Cystic fibrosis, 99, 100
Dachis, Jeffrey, 75–77
Dassault Systèmes, 237, 238
Data application to treatment of cancer, 22–23
biomarkers, 17–20
biospecimens, 17–18, 20–21
clinical trials accepting new kinds of, 152–154
Flumoji (crowdsourcing tracking engine), 86–87
layers of, 24–25
molecular, 6fig, 123
partnering with doctors, 83–85
PatientsLikeMe's self‐reported, 26–28
patient territory, 22–24
privacy and transparency issues of, 234–235
tracking the flu, 81–89
traditional biomarkers, 29–31
See also Patient equations; Statistics
Data collaboration accelerating the value of research, 197–201
Data & Society Research Institute, 84
Davi, Ruthanna, 166
DeepHeart (algorithm), 45
Dementia apps trying to distinguish memory issues from, 125
beta‐amyloid plaques linked to, 119–120
factors involved in treating, 123–125
financial and social costs of, 123
theoretical paths for neurodegenerative disease, 121–123
See also Alzheimer's disease; Cognitive impairment
Dennis, Kara, 153, 176
Department of Defense, 128, 130
Department of Veterans Affairs, 128, 130
Diabetes artificial intelligence model to predict, 86, 219
artificial pancreas‐type solution to, 72–74
DeepHeart (algorithm) prediction of, 45
doctors incentivized to prevent, 206
latent autoimmune diabetes of adulthood (LADA), 76
multi‐hormone closed loop system treatment for, 74
Noom app‐based diet and life coaching tool, 211
OneDrop system for, 75–77, 175, 180, 186
Open Artificial Pancreas System project (#OpenAPS), 74–75
Diabetes journal, 76
Diagnosis biomarkers used for, 17–20, 29–31
data used for early intervention and, 88–89, 220–222, 232–233
heart disease, 220–222
high‐frequency feedback used for, 29–31
low cost of better digital measurements, 37–40
See also Treatment/clinical care
Digital technologies artificial intelligence (AI), 85–86, 219, 226
complexity of digital images, 135–136
empowering patients through, 224–228, 232–233
importance of doctors to revolutionary use of, 217–222
low cost of measurements using, 37–40
machine learning and AI, 85–86
need for increased application to clinical trials, 154–156
as part of a larger ecosystem, 195–197
privacy and transparency issues of, 234–235
See also Apps; Medical devices; Wearables
Diseases asthma, 69–71
Castleman disease, 102, 106
cystic fibrosis, 99, 100
data used for early intervention, 88–89, 220–222, 232–233
diabetes, 45, 69–77, 86, 206, 211, 219
heart disease, 220–222
interaction between cancer and other, 129–130
Lyme disease, 225, 232
“omnigenic” nature of, 13
proteins used to diagnose, 20
rheumatoid arthritis (RA), 111, 209
transitioning to wellness following treatment for, 129–130
See also Alzheimer's disease; Cancers; Illness
DNA biospecimen to search for specific sequences of, 18
Gattaca's illustration on limitations of, 10–11, 235
“junk,” 25
TP53 cancer‐causing gene in the, 127
Watson and Crick's breakthrough on, 4, 10, 14
See also Genotype
Doctors partnering with patients, 222–224
their importance to the data revolution, 217–222
Drug development how precision medicine impacts, 105–106
incentives for delivery of therapeutic value of, 211
including engagement strategy as part of, 180–182
See also Clinical trials
Duchenne muscular dystrophy, 38
Dudley, Dr. Joel, 7–8
Duke University Hospital's Sepsis Watch system, 82–85, 89
ECG CardioNet, 184
livestream and continuous, 14, 33
The Economist, 195
Elashoff, Barbara, 28–29, 165–166
Elashoff, Mike, 29, 166
El Camino Hospital (California), 85
Elemental publication, 224
Elish, Madeleine Clare, 84
Engagement strategy challenges to implementing, 182–185
pre‐ versus post‐regulatory approval, 181fig
EverlyWell, 224–225
Facebook, 195
Fajgenbaum, Dr. David, 102, 106, 107–110, 140, 153, 170
Fantastic Voyage (film), 236
Farmanfarmaian, Robin, 216, 226
FDA (Food and Drug Administration) artificial pancreas‐style system approved by, 73
Ava approval by, 60
clinical trial responsibilities by, 149
concerns over 23andMe genetic diagnostics by, 36
Keytruda cancer treatment approved by, 98
mTOR inhibitor sirolimus approved by, 108
6‐minute walk test used for submission to, 38
support of Bayesian trial design, 160
Fernandez, Clara Rodriguez, 73–74
Fertility Ava ovulation‐tracking bracelet, 60–63, 64–67, 70, 71
Trak's at‐home testing kit, 66
YO's at‐home semen analysis by smartphone, 66
Financial Times, 45
Fitbit, 52, 66, 76, 153
Flatiron Health, 168
Flu benefits of catching early, 81–82
CDC's “Flu View” report on the, 86–87
data tracking the, 86–89
Flumoji (crowdsourced flu‐tracking app), 86–87, 88
Flu Near You, 87
Flu shots, 87
Forbes Healthcare Summit, 73
Forbes magazine, 223
Frequentist methodology, 156, 157
Gartner, 153
Gastric bypass surgery, 205
Gates Foundation, 101
Gattaca (film), 10–11, 235
Gawande, Dr. Atul, 43, 175
GBM AGILE (Glioblastoma Adaptive Global Innovative Learning Environment), 161–162
Genes: cancer and, 13; HER2/neu gene, 94
Genetic panels, 153
Genome a future of predicting disease using, 232
importance in determining our health, 4
sequencing the, 3, 10
Genotype the false promise of, 10–14
phenotype vs., 3–4, 11fig–13
See also DNA
GlaxoSmithKline, 86, 87, 209
Glioblastoma (brain cancer), 161
Glucose‐sensing contact lens, 44, 47
Goldner, Dan, 77
Google attempts to track the flu by, 87
Gmail, 195
Google Home, 54
health care research and products by, 195
Nest Learning Thermostat, 53–54, 60
Verily, 47
Groove Health, 177
The Patient Equation Page 26