Hatfull, Dr. Graham, 99–101
HealthKit app, 176, 209
Heart disease, 220–222
Helme, Kady, 73, 74
HER2/neu gene, 94
Herophilus, 6, 8, 14
Herrling, Paul, 6–7, 119
Heywood, Jamie, 26–28, 32
High blood pressure AI model to predict hypertension, 86
DeepHeart (algorithm) prediction of, 45
Hypertension study (2018), 31
Hippocrates, 3, 4
HITLAB (Healthcare Innovation and Technology Lab), 183
HIV, 111
Hodgkin's lymphoma, 147
Hodgkins, Michael, 223
Hook‐Barnard, India, 88
Hourani, Andrew, 177
Hypertension, 45, 86
Hypertension study (2018), 31
Hypothermia, 5
IBM's Watson failure, 44, 47, 133–134, 219
Icahn School of Medicine (Mount Sinai), 8
Idiopathic multicentric Castleman disease (iMCD), 106–109
IEEE Spectrum, 83
Ikeguchi, Dr. Edward, 137–138
Illness benefits of catching early, 81–82, 86–87
challenges of stopping spread through data, 87–89
See also Diseases
Immunotherapy treatments for Alzheimer's disease, 99
customized, 98–99
great potential of, 101–102
Kymriah, 98, 210, 211
Incyte, 87
Inside Signal Processing newsletter, 82
Institute for Next Generation Healthcare, 8
Insulin artificial pancreas‐type system to replace, 72–74
multi‐hormone closed loop system to supplement, 74
International Programme on Chemical Safety (WHO), 17
iPhones, 51, 195
IVF, 64
I‐SPY 2 breast cancer study, 157–162, 197
Janssen (Johnson & Johnson), 177
Jawbone fitness trackers, 195
Jenkins, Julian, 87, 88
Jobs, Steve, 51
Johnson & Johnson, 177
Journal of Chronic Diseases, 162
Journal of Medical Internet Research, 84
“Junk DNA,” 25
Jurassic Park (film), 234
Kachnowski, Dr. Stan, 183–185, 186–187
Kaiser Permanente (Oregon and Washington State), 85
Kennedy, Ted, 161
Kepler, Johannes, 130
Keytruda, 98–99, 145, 155
Koenig, Pascal, 61–62, 64–66
Kuelper, John, 207
Kymriah, 98, 210, 211
The Lancet, 45
Lassman, Andrew, 161
Latent autoimmune diabetes of adulthood (LADA), 76
Lee, David, 107, 136
Lee, Dr. Jerry, 94, 126–129, 130, 137
Lind, James, 8–9, 10, 154
Livongo, 223, 227, 228
Los Angeles Times, 135
Lumosity, 126
Lyme disease, 225, 232
Lymphoma, 111
L‐DOPA, 223
Machine learning systems, 85–86
Margolis, Jeff, 226–227
Mars Climate Orbiter (1998), 133, 134–136, 137, 140
McCain, John, 32, 161
Measurements discrete points vs. continuous, 39–40
how doctors can use improved digital, 219–222
low cost of better digital, 37–40
Nest Learning Thermostat, 53–54, 60
to predict fertility, 59–60
to predict heart disease, 220–222
See also Biomarkers; Steam tables
MedCityNews, 186, 223, 226
Medical devices Ava (ovulation‐tracking bracelet), 60–63, 64–67, 70, 71
Bloomlife, 66
Fitbit, 52, 66, 76, 153
Medtronic Minimed 670G, 73
See also Digital technologies; Smartphones; Wearables
Medical reimbursement. See Value‐based reimbursement
Medidata Solutions, Inc. ADAPTABLE trial involvement by, 150–151
advantages of data sharing by, 198–199
creating value from data, 136–141
demystifying clinical trials data, 135–136
the founding and focus of, 138–141
PARADE study role of, 209
purchased by Dassault Systèmes, 237
synthetic control model developed by, 165–168, 198
work with Castleman Disease Collaborative Network, 140
Medtronic Minimed 670G, 73, 77
Memorial Sloan Kettering Cancer Center (New York City), 130, 211
Mendel, Gregor, 4
Merad, Dr. Miriam, 98–99
mHealthIntelligence, 223
Migraine Alert app, 45, 46
Mindstrong, 36
Mind‐body connections, 34–36
Misra, Dr. Veena, 70–72
MIT Connection Science, 86, 87
MobiHealthNews website, 66, 88, 177
Mobile apps. See Apps
Molecular data, 6fig, 123
MoovCare app, 177
Morphogens, 12
Mount Sinai Health System, 7–8, 86
MRIs, 135–136
mTOR, 108
Muscular dystrophy, 38
Mycobacterium abscessus strains, 101
National Cancer Institute (NCI), 126, 127
National Institutes of Health (NIH), 145
National Patient‐Centered Clinical Research Network, 150
Nest Learning Thermostat, 53–54, 60
New Atlas, 76
New England Journal of Medicine, 31
Newsweek magazine, 95
New York Times on benefits of Ava bracelet, 61
criticism of clinical trial procedures by, 144
on doctors' use of data to predict disease, 232–233
on lack of treatment for rare cancers, 98
on proteins used to diagnose disease, 20
on social indicators of depression, 36
Nixon, Richard, 93, 94
Nokia Health (now Withings), 51–52
Noom app‐based diet and life coaching tool, 211
North Carolina State University's ASSIST program, 70
Northwestern University, 46
Norton, Larry, 130
Novartis, 6–7, 98, 119, 177, 178, 210
Novella Clinical, 98
Nuclear attack data story, 224
Omada Health, 211
OneDrop app, 75–77, 175, 180, 186
OneZero, 99
Open Artificial Pancreas System project (#OpenAPS), 74–75
Otsuka Pharmaceutical, 194
Outcomes‐based contracts (OBCs), 210–211
Ovulation‐tracking Ava bracelet for, 60–63, 64–67, 70, 71
differing methods used for, 59–60
p53 mutation, 20, 96–97
Pahwa, Dr. Rajesh, 223–224
PARADE study (Patient Rheumatoid Arthritis Data from the RealWorld), 209
Parkinson's disease, 223
Parsa, Ali, 45
Patient‐Centered Outcomes Research Institute (PCORI), 150
Patient equations building a steam table to represent, 115–119
a call to action and future use of, 235–238
cognitive dimension of, 34–36
Dr. Fajgenbaum's research on leveraging, 102, 106, 107–110, 140, 153, 170
moving from univariate to multivariate approaches to, 31–33, 43
progressing through Alzheimer's disease, 119–125
See also Data
Patients doctors partnering with, 222–224
empowering through new technologies, 224–228, 232–233
finding a marketing niche that benefits the, 66–67
how apps can help compliance through engagement strategy, 179–182
making clinical trials patient‐centric, 149–152
medical devices and changing role of, 63–64
quality of life over duration of life, 207–210, 2
15, 219
survival rates of, 204
PatientsLikeMe, 26–28
Patient territory data, 22–24
Pear Therapeutics, 177
Personalized medicine for Alzheimer's disease, 99
customized immunotherapy treatments as, 98–99
Kymriah, 98, 210
phage therapy used as, 93–94, 99–102, 106
Petrov, Stanislav, 224
Phage therapy, 93–94, 99–102, 106
Pharma “digital from the beginning” applications by, 180–185
drug development by, 105–106, 211
making the case for value‐based reimbursement, 203–216
Pharmaceutical Technology magazine, 107
Pharma Times, 152
PharmaVOICE, 98
Phase diagrams on transition from matter to liquid or gas, 116fig
for treatment choices, 118fig
Phenotypic scale, 5–6fig
Phenotype description and examples of, 4
genotype vs., 3–4, 11fig–13
multiscale view of health role of, 6fig
Physical therapy (PT), 205–206
Physicians. See Doctors
Physiological data combining genetic information with, 13–14
multiscale view of health including, 6fig
PLOS Medicine, 48
Pocock, Stuart J., 162, 165
Poon, Dr. Eric, 83, 85
“Powering Your Own Wellness” TEDx Talk (Misra), 72
PP2A cell regulator protein, 97
PPROM (preterm premature rupture of membranes), 61
Precision Immunology Institute (Mount Sinai School of Medicine), 99
Privacy, 234–235
Progesterone, 94
Project Baseline (Verily), 195–196
Prostate cancer, 18–19, 21, 115–118fig, 120
Proteins beta‐amyloid plaques, 119–120
cancer treatment using proteomics, 94
p53 tumor suppressor, 96–97
PP2A cell regulator, 97
Proteomics, 94
Proteus Digital Health, 177, 178, 179
PSA (prostate‐specific antigen), 18–19, 21, 115–118fig, 120–121, 155
PSA mRNA, 19
Quality of life (QOL), 207–210, 215, 219
Radical prostatectomy, 115–116fig
Razorfish, 75
Regulators, 204–205
Reimbursement. See Value‐based reimbursement
reSET app, 177
Rheumatoid arthritis (RA), 111, 209
“The Rise of Consumer Health Wearables: Promises and Barriers” (PLOS Medicine), 48
RNA, PSA, 19
Rosenthal, Arnon, 99
Rose, Sophia Miryam Schüssler‐Fiorenza, 232–233
Schizophrenia, 86
Science Friday (NPR), 86
Science Translational Medicine, 74
Scripps Translational Science Institute (San Diego), 36
Scurvy, 8–9
Sendak, Dr. Mark, 82, 83
Sepsis description and mortality rate of, 82
using data to catch it earlier, 82–85
Sepsis Watch system (Duke University Hospital), 82–85, 89
Shark Tank (TV show), 224
Sharpe, T. J., 144–145, 146, 149
Sherif, Tarek, 138, 152
6‐minute walk test, 38–39
Slate magazine, 48
Sleep apnea, 45
Smartphones Apple Watches, 30, 46, 76, 153, 195, 233
iPhones, 51, 195
as a medical device, 14, 15, 51
See also Apps; Medical devices
Smart toilets, 52
Snyder, Michael, 232
Social media Facebook, 195
tracking the flu using, 87–88
“The Stakes of Uncertainty: Developing and Integrating Machine Learning in Clinical Care” (Elish), 84
Staley, Alicia, 147–148, 149
Stanford's Center for Genomics and Personalized Medicine, 232
Statistics Bayesian methodology, 156–162, 168–170
frequentist methodology, 156, 157
See also Data
STAT (publication), 207
Steam tables for cancer, 126–128
example of a, 115–116fig
how to improve graphs and, 118–119
See also Measurements
Steinhubl, Dr. Steve, 36
Survival difference between quality of life and, 207–210, 215, 219
Synthetic control Bayesian adaptive model used with, 168–170
conducting clinical trials with, 162–165
Medidata's model for, 165–168
Tay‐Sachs disease, 11
T‐cell acute lymphoblastic leukemia (T‐ALL), 97
Thalassemia, 211
Theranos, 44, 47
Thermometer readings, 4–5
TP53 gene, 127
Trak (at‐home testing kit), 66
Treatment/clinical care application of data to cancer, 22–23
factors involved in cognitive impairment, 123–125
Keytruda used in cancer, 98–99, 145
phage therapy, 93–94, 99–102, 106
steam tables and phase diagram for choosing, 115–118fig
See also Cancer treatments; Diagnosis
Triple‐negative tumors, 94
Tuberculosis, 101
Tullman, Glen, 223, 227–228
23andMe, 36
UnitedHealth, 26
University of California, San Francisco, 88
University of Kansas Medical Center, 223
University of Pennsylvania, 106, 108
University of Pittsburgh, 99
University of Southern California, 94, 126
University of South Florida, 126
University of Texas M.D. Anderson Cancer Center, 156
Value‐based reimbursement doctors incentivized to prevent diseases, 206–207
incentives for delivery of therapeutic value, 211
making value‐based care the future, 212–216
van Leeuwenhock, Anton, 8, 14
Vator News, 211
Vector (Boston Children's Hospital blog), 97
Verily, 44, 47, 195–196
Washington Post, 224
Waterlogged app, 175, 176
Watson, James, 4, 10, 14
Wearables Apple Watch, 30, 46, 76, 153, 195, 233
asthma monitoring, 71
Ava ovulation‐tracking bracelet, 60–63, 64–67, 70, 71, 219
battery technology barrier to, 48, 51
Consumer Electronics Show (CES) exhibitors on, 46
criticism of, 175–176
Fitbit, 52, 66, 76, 153
potential and developments in, 46–48, 49, 52
Verily's work on, 44, 47, 195–196
See also Apps; Digital technologies; Medical devices
Weather Channel, 87
WebMD, 45
Wellness transition, 129–130
WellTok, 226
Whelan, Jack, vii–x, 225
Win probability added (WPA), 213
Wired magazine, 100, 134
Withings (was Nokia Health), 51–52
World Health Organization, 17, 123
Yadegar, Dr. Daniel, 216, 220–222
YO (at‐home semen analysis), 66
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