Index
A
acquisition cost, 83
adaptations. See healthcare adaptations
administrative system modernization
in healthcare industry, 132
Adoption Decision Dashboard, 17-18
adoption factors. See Innovation Adoption Factors Model
adoption of innovations, 15-17
Innovation Adoption Factors Model, 16-17, 182-183
Innovation Pathway, 15, 179-181
customer focus, 53
Allen, Ray, 152
customer analytics in retail industry, 63-66
healthcare adaptations. See also innovations
peer-to-peer information sharing, 175-176
healthcare adaptations discovery process, 5-6
healthcare analytics
current and future state of, 40-41
high risk/high reward, 3
analytics sweet spots, 9-13. See also innovations
administrative system modernization, 130-132
multichannel messages, 132-133
transaction processing, 128-130
big data promises, 12
personal data collection, 11
in political campaigns, 92-111
predictive analytics, 10
privacy concerns, 13
customer lifetime value (CLV), 74-76
social media data collection, 79-81
sociology of, 13
technology game changers, 12
ATMs (automated teller machines), 128-129
auto racing, predictive modeling for, 161
Axelrod, David, 84
Ayer, Robert, 158
B
balance sheet improvements in banking industry, 123-124
“Banking for Success: Using Analytics to Grow Wallet Share” (IDC Financial Insights), 131
banking industry
analytics sweet spots, 127-139
administrative system modernization, 130-132
multichannel messages, 132-133
transaction processing, 128-130
creative adaptations from, 14
health insurance industry comparison, 116-117
hierarchy of needs, 127
logical adaptations from, 13-14
mission of, 23
predictive analytics, 10
regulations, 120
size of, 115
balance sheet improvements, 123-124
cost-cutting initiatives, 121-123
Barea, Jose, 159
on-base and slugging percentage (OPS), 150
on-base percentage (OBP), 150
baseball, predictive modeling. See also Sabermetrics
for pitching and fielding, 162-163
basketball, predictive modeling for, 158-159, 161-162
Battier, Shane, 159
Behavioral Risk Factor Surveillance System (BRFSS), 101, 171
Biden, Joe, 89
in banking industry
IT system modernization, 122
in political campaigns, 83-85, 90-91
BMI (body mass index), 50, 72, 107
Bradley, Elizabeth, 21
BRFSS (Behavioral Risk Factor Surveillance System), 101, 171
brick and mortar
repurposing
in banking industry, 121, 126-127
in healthcare industry, 121-122
Brock, Thomas, 182
Bureau of Consumer Financial Protection, 120
burning platform, 186
business drivers. See strengths
business needs
Bynum, Andrew, 151
C
Cameron, Gordon, 135
Canadian Index of Well-Being (CIW), 98
Capra, Frank, 115
cause and effect theory, 186
challenges
to healthcare adaptations discovery process, 5-8
case study, 193
customer engagement, 30
monetization of challenges, 31-32
mortality amenable to healthcare rate in U.S., 25-27
social factors in health outcomes, 27-29
hold the base, 87
economy, 59
Chandler, Tyson, 151
Chang, Yu-Han, 161
Cignifi, 136
CIW (Canadian Index of Well-Being), 98
clinical practice
converting scientific research to, 25-27
CLV (customer lifetime value), 74-76
Collaborative Planning, Forecasting, and Replenishment (CPFR) systems, 63
competitive advantages in healthcare marketplace, 38-39
confirmation bias, 4, 178, 185-186
CoreLogic, 137
“The Cost Conundrum” (Gawande), 29
Cost-cutting initiatives in banking industry, 121-123
costs. See pricing
cottage industry, healthcare industry as, 35-36
CPFR (Collaborative Planning, Forecasting, and Replenishment) systems, 63
creativity
in healthcare adaptations, 13-14
credit performance, improving, 123-124
CRM (customer relationship management) systems, 63
customer analytics
by healthcare industry, 30
monetization of, 31
customer experience, retail versus healthcare industries, 60-62
in healthcare adaptations, 177-178
personal data collection, 11
of retail industry, 58
customer lifetime value (CLV), 74-76
customer power, 7
customer relationship management (CRM) systems, 63
customer value, optimizing in retail industry, 62-63
customers
banking industry trends, 118
in healthcare industry, defined, 32
CVS/Caremark, customer engagement, 77-78
D
Dampier, Erick, 159
Danesis, Samantha, 161
Dantley, Adrian, 159
dashboard reporting, 45-46, 171-172
data collection. See also personal data collection
in banking industry
IT system modernization, 122
for predictive analytics, 133-139
data source optimization, 49-51
mobile communication and, 54-55
by political campaigns, 90-91, 105-106
privacy concerns, 13
technology game changers, 12
data democratization in political campaigns and healthcare industry, 107-111
data integration, challenges of, 42-44
data sources, optimizing, 49-51
data-driven management in sports industry, 147-148
decision stage (Innovation Pathway), 181
decision support in clinical practice, 45-48
democratization of data in political campaigns and healthcare industry, 107-111
design factors (Innovation Adoption Factors Model), 183-193
compatibility, 188
complexity, 188
individual’s acceptance, 189-190
innovation attributes, 187-189
organization capabilities, 189-191
program theory/model, 190
prototyping, 191
skills and competencies, 190-191
design stage (Innovation Pathway), 180
discovery process for healthcare adaptations, 5-6
Dodd-Frank Wall Street Reform and Consumer Protection Act, 120
E
economic challenges to retail industry, 59
education, performance metrics, 155-156
efficiency
of banking industry, improvements to, 121-123
Eggers, William, 182
EHRs (electronic health records), data integration with, 42-44
election campaigns. See political campaigns
Emanuel, Rahm, 117
employee salaries in sports industry, 145-146
execution stage (Innovation Pathway), 181
F
Federer, Roger, 144
Ferle, Ewan, 184
FICO score, 134
Fieldf/x, 163
finance industry, performance metrics, 153
Fitzgerald, Larry, 145
FiveThirtyEight Methodology (Silver), 102
Formula One auto racing, predictive modeling for, 161
fundraising in political campaigns, 87-89
G
Gandhi, Mahatma, 52
Ganeshapillai, Gartheeban, 159-160
Gasol, Pau, 159
Gawande, Atul, 29
GDP (Gross Domestic Product), 98
Gladwell, Malcolm, 182
Goethe, 181
Great Recession, 6
Green Dot, 119
Gross Domestic Product (GDP), 98
Gruman, Jesse, 53
H
Hadoop, 12
Hasenfeld, Yeheskel, 182
HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems) surveys, 94-95
behavior change and, 22, 27-29
defined, 20
Health Improvement Capability Score (HICS), 14, 134, 138-139, 174
health information exchange (HIE), 43
Health Information Technology for Economic and Clinical Health (HITECH) Act, 42
health insurance industry. See healthcare industry
healthcare adaptations. See also innovations
peer-to-peer information sharing, 175-176
healthcare analytics. See also analytics
business needs alignment, 53-54
data source optimization, 49-51
healthcare reform support, 44-45
population health improvements, 48
current and future state of, 40-41
administrative system modernization, 132
banking industry comparison, 116-117
care delivery statistics, 35
case study, 193
customer engagement, 30
monetization of challenges, 31-32
mortality amenable to healthcare rate in U.S., 25-27
social factors in health outcomes, 27-29
comparison with other industries
customer experience, retail industry versus, 60-62
market-oriented solutions for, 36-37
multichannel messages in, 133
repurposing physical space, 121-122
retail industry, comparison with, 60-62, 70-71
revenues in U.S., 35
sports industry comparison, 141-143
healthcare reform
customer focus, 53
health-risk-assessment (HRA) surveys, 72
Henehan, Aaron, 161
HICS (Health Improvement Capability Score), 14, 134, 138-139, 174
HIE (health information exchange), 43
Hill, Grant, 159
HITECH (Health Information Technology for Economic and Clinical Health) Act, 42
hold the base in political campaigns, 87
Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) surveys, 94-95
“How Companies Learn Your Secrets” (Pole), 68
Howard, Dwight, 151
HRA (health-risk-assessment) surveys, 72
hyper-competition, 7
I
IBM’s Watson technology, 47-48, 129
IDC Financial Insights, 131
idea generation factors (Innovation Adoption Factors Model), 183-187
burning platform, 186
cause and effect theory, 186
comfort with new ideas, 183-184
ideation maturation, 185
innovation receptivity, 183
norms and beliefs about change in general, 184-185
quality of idea, 185
survival pressures, 184
urgency and timing, 186
window of opportunity, 187
ideas
Innovation Adoption Factors Model, 16-17, 182-183
Innovation Pathway, 15, 179-181
Implementation (Pressman and Wildavsky), 181
inefficiency of healthcare industry, 29-31
Innovation Adoption Factors Model, 16-17, 182-183
compatibility, 188
complexity, 188
individual’s acceptance, 189-190
innovation attributes, 187-189
organization capabilities, 189-191
program theory/model, 190
prototyping, 191
skills and competencies, 190-191
idea generation factors, 183-187
burning platform, 186
cause and effect theory, 186
comfort with new ideas, 183-184
ideation maturation, 185
innovation receptivity, 183
norms and beliefs about change in general, 184-185
quality of idea, 185
survival pressures, 184
urgency and timing, 186
window of opportunity, 187
Innovation Pathway, 15, 179-181
innovations. See also analytics sweet spots
Innovation Adoption Factors Model, 16-17, 182-183
Innovation Pathway, 15, 179-181
high risk/high reward, 3
integration of data, challenges of, 42-44
IT system modernization in banking industry, 122
It’s a Wonderful Life (film), 115
J
James, Bill, 148
James, Brent, 4
Jobs, Steve, 3
K
Kennedy, John F., 180
key business drivers. See strengths
Kingdon, John, 182
Kissinger, Henry, 192
L
Lewis, Rashard, 152
life expectancy in U.S., 24-25
Lin, Jeremy, 144
M
M&A (mergers and acquisitions) in banking industry, 123
machine-enabled clinical support, 47-48
Maheswaran, Rajiv, 161
Majerle, Dan, 159
management approach in sports industry, 147-148
in banking industry, 125
in healthcare marketplace, 38-39
in political campaigns, 90
retail versus healthcare industries, 76-79
market-oriented solutions for healthcare industry, 36-37
Maslow, Abraham, 20
Maslow’s hierarchy of needs, 20, 40
math usage in political campaigns, 90-91
McGee, JaVale, 151
McGlynn, Elizabeth, 27
McGrady, Tracy, 159
McHale, Kevin, 159
McKee, Lorna, 184
media channels in political campaigns, 90
merchandise (optimizing customer value), 63
mergers and acquisitions (M&A) in banking industry, 123
Merrill, Douglas, 136
messaging in political campaigns, 89-90
microlistening, 105
in banking industry, 125
healthcare adaptation of, 172-173
in political campaigns, 90
military, personal data collection in, 11
MIT Annual Sports Analytics Conference, 158
in banking industry, 122-123, 132-133
healthcare analytics and, 54-55
in healthcare industry, 133
personal data collection, 79-81
mobilization of political campaigns, 91
modeling behavior change, 10, 51-52
money in political campaigns, 87-89
mortality amenable to healthcare rate in U.S., 25-27
motivation in political campaigns, 91
in banking industry, 122-123, 132-133
in healthcare industry, 133
in political campaigns, 90
N
Nash, Steve, 152
National Institutes of Health (NIH), 20
natural language processing, 129
NetSpend Holdings, 119
network power, 110
NIH (National Institutes of Health), 20
Nowitzki, Dirk, 152
O
Obama Campaign Dashboard, 108-109
Obamacare. See Affordable Care Act (ACA)
obesity, predictive analytics for, 72-73, 107, 168-169
OBP (on-base percentage), 150
O’Leary, John, 182
online sales, retail sales versus, 59-60
OPS (on-base and slugging percentage), 150
optimizing
customer value in retail industry, 62-63
outcomes
of healthcare expenditures, 24-25
monetization of, 31
P
patient engagement. See customer engagement
Paxson, John, 159
PayNearMe, 119
PECOTA (Pitcher Empirical Comparison and Optimization Test Algorithm), 157
peer-to-peer information sharing, 175-176
peer-to-peer lending, 120
Pekovic, Nikola, 151
performance improvement in sports industry, 146-147
in healthcare industry, 153-156
peer-to-peer information sharing, 175-176
personal behavior modification. See behavior change
personal data collection, 11
by political campaigns, 105-106
for predictive analytics, 133-139
privacy concerns, 13
personalization. See microsegmentation
Pettigrew, Andrew, 184
physical space, repurposing
in banking industry, 121, 126-127
in healthcare industry, 121-122
Pitcher Empirical Comparison and Optimization Test Algorithm (PECOTA), 157
PITCHf/x, 162
hold the base, 87
mission of, 23
performance metrics, 153
personal data collection, 11
predictive analytics, 10
media channels, 90
microsegmentation, 90
mobilization, 91
motivation, 91
Pollock, Jeffrey, 86
population health. See also health
improving, 48
Prager, Richie, 117
predictive analytics, 10, 51-52
healthcare adaptation of, 173-175
in political campaigns, 102-107
presidential election campaigns. See political campaigns
customer value optimization, 63
in healthcare industry, 33-34, 38
privacy, personal data collection and, 13
profit margin in retail industry, 59
promises of big data, 12
prototyping, 191
provider performance. See performance metrics
Q-R
“The Quality of Health Care Delivered to Adults in the United States” (McGlynn), 27
racing, predictive modeling for, 161
radical personalization. See microsegmentation
radio frequency identification (RFID), 63
range percentage, 152
ratings. See performance metrics
receptive context, 184
regulations
in healthcare industry, 34
reinvention stage (Innovation Pathway), 181
repurposing brick and mortars
in banking industry, 121, 126-127
in healthcare industry, 121-122
results stage (Innovation Pathway), 181
retail industry
customer lifetime value (CLV), 74-76
social media data collection, 79-81
economy, 59
healthcare industry, comparison with, 70-71
mission of, 22
personal data collection, 11
predictive analytics, 10
revenues in U.S., 57
customer value optimization, 62-63
“Retailing 2020: Winning in a Polarized World,” 63
return on equity (ROE), 118
RFID (radio frequency identification), 63
risk measurement. See predictive analytics
Rodriguez, Alex, 145
ROE (return on equity), 118
Rogers, Everett, 182
Rollins, Tree, 159
Ronaldo, Cristiano, 145
Rowles, Sean, 127
rugby, predictive modeling in, 163
S
salaries in sports industry, 145-146
Schroeder, Steve, 27
scientific research, converting to clinical practice, 25-27
security breaches with personal data in retail industry, 66
segmentation. See market segmentation
sentiment analysis, 110
service factors (optimizing customer value), 62
“shadow banking” system, 118-119, 124
Shaping Strategic Change: Making Change in Large Organizations (Pettigrew, Ferle, McKee), 184
The Signal and the Noise (Silver), 84
SLG (slugging percentage), 150
slugging percentage (SLG), 150
social media usage
in banking industry, 122-123, 132-133
in healthcare industry, 133
peer-to-peer information sharing, 175-176
personal data collection, 79-81
societal needs, business needs versus, 37-38
sociology. See also behavior change
of analytics sweet spots, 13
sports industry
analytics sweet spots, 148-164
healthcare industry comparison, 141-143
mission of, 23
predictive analytics, 10
data-driven management, 147-148
performance improvement, 146-147
spread percentage, 152
store factors (optimizing customer value), 62
Strawberry, Darryl, 149
balance sheet improvements, 123-124
cost-cutting initiatives, 121-123
of healthcare adaptations discovery process, 8-9
media channels, 90
microsegmentation, 90
mobilization, 91
motivation, 91
customer value optimization, 62-63
data-driven management, 147-148
performance improvement, 146-147
supply chain (optimizing customer value), 63
sweet spots. See analytics sweet spots
swing votes in political campaigns, 85-86
T
Target, predictive analytics at, 68-69
Taylor, Lauren, 21
technology game changers, 12, 63
Tesco, 136
Tolstoy Trap, 185
transaction processing in banking industry, 128-130
transformation, need for, 8
transparency in healthcare industry, 33
V
“The Value of Building Sustainable Health Systems” (IBM), 132
value over replacement player (VORP), 150
VORP (value over replacement player), 150
W-Z
Wade, Dwayne, 159
Wall Street (film), 114
WARP (Wins Above Replacement Player), 157
Watson technology (IBM), 47-48, 129
Webber, Chris, 159
Weinberg, Allen, 117
well-being indexes, 97-102, 170-171
window of opportunity, 187
Wins Above Replacement Player (WARP), 157
Wonga, 136
Wooding, Julie, 120
Woods, Tiger, 144
ZestCash, 136