Transforming Healthcare: The Quantification of Everything

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Presented by Dr Steven Tucker, Founder, Tucker Medical at ISS Seminar: How Analytics is Transforming Healthcare on 31 Oct 2014.

Healthcare Data & Analytics

TRANSFORMING HEALTHCARE
THE
QUANTIFICATION OF
EVERYTHING
STEVEN TUCKER, MD, FACP, FAMS
Global Health Advisor, SingTel
Founder, Tucker Medical

WARNING !
We tend to overestimate
the effect of a technology
in the short run and
underestimate the effect
in the long run.
Roy Amara
Co-founder, Institute for the Future

1974 1984 1994 2004 2014 EXPONENTIALS
4E+09
3E+09
2E+09
1E+09
4E+03

4E+09
3E+09
2E+09
1E+09
4E+03
COMPUTING
POWER
1974 1984 1994 2004 2014 2024

4E+09
3E+09
2E+09
1E+09
4E+03
MOORE’S
LAW
1974 1984 1994 2004 2014 2024

Computers
4E+09
3E+09
2E+09
1E+09
4E+03
MOORE’S
LAW
1974 1984 1994 2004 2014 2024

Computers
4E+09
3E+09
Sensors
2E+09
1E+09
4E+03
MOORE’S
LAW
1974 1984 1994 2004 2014 2024

Computers
4E+09
3E+09
Sensors
2E+09
1E+09
4E+03
MOORE’S
LAW
Biology
1974 1984 1994 2004 2014 2024

Computers
4E+09
3E+09
Sensors
2E+09
1E+09
4E+03
MOORE’S
LAW
Biology
Pan-omics
1974 1984 1994 2004 2014 2024

Healthcare is not
a science problem,
it’s an information
problem.
-Thomas Goetz

DIABETES
382 Million
Source: International Diabetes Federation

OBESITY
500 Million
Source: World Health Organisation

HEART DISEASE
17.3 Million
Source: World Health Organisation

CANCER
14.1 Million
Source: World Health Organisation

If you cannot
measure it, you
cannot improve it.
-Lord Kelvin

SENSOR
EVOLUTION
Sensor 1.0
Sensor 2.0
Sensor 3.0
Ability to measure
+ record signal
Sensor web
integration; share
+ access data
Passive data
gathering +
aggregating +
meaningful insight

GENOME MATH
3.2
Base pairs
per genome
billion

GENOME MATH
3.2billion 40xCoverage BP 128billion
data points

SENSORS
Gyroscope
Proximity
Gesture
Fingerprint

SENSORS
Gyroscope
Proximity
Gesture
Compass
Fingerprint
Hall cover

SENSORS
Gyroscope
Gesture
Heart rate
Proximity
Compass
Barometer
Fingerprint
Hall cover

SENSORS
Gyroscope
Gesture
Ambient light
Heart rate
Accelerometer
Proximity
Compass
Barometer
Fingerprint
Hall cover

Exposome
33
48
Cd
80
Hg
28
Ni
81
82
Ti
Pb
As

Digital Health as
Google Map
HEALTH AS A
GOOGLE MAP
SENSOR
ACTIVITY
DIET
GENOME
MICROBIOME
LAB
PHENOME
UNIQUE
PERSON

P4 MEDICINE
1. Predictive
2. Preventive
3. Personalized
4. Participatory

100k WELLNESS
PROJECT
1. Predictive
2. Preventive
3. Personalized
4. Participatory

P4 MEDICINE
Predictive
You will track your health.
It’s your data.
Identify transition states.

P4 MEDICINE
Preventive
Wellness networks.
Illness networks.
A systems-based approach
to repair and maintenance.

P4 MEDICINE
Personalized
You are your own control.
Your health data cloud.
Molecular + Environmental.

P4 MEDICINE
Participatory
Transformative for health.
Societal change - enormous.
Patients will adapt.
Doctors may adapt?

THE PIVOT
Health Status 20% of patients =
80% of costs
Healthy
Low Risk At Risk High
Risk
Chronic
Disease
Early
Active
Disease
End of Life
Care
Chronic
Disease
Progression
VALUE COST

Life Expectancy at birth
85
82
79
76
73
70
1990 2000 2008 1990 2000 2008 1990 2000 2008 1990 2000 2008
< 12 12 13-‐15 > 16
Years of Educa>on

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More often than not, people’s perception of Product Management is usually centred around the definition, management and prioritisation of software features and functionality. While that is largely true, it is also one of many things that a Product Manager needs to focus on, given limited time and resources. This session aims to provide an unfiltered view of how Product Management looks like in the context of Enterprise Cloud Applications development, the challenges confronting Product Managers, and the tradeoff decisions to be made in order to overcome these challenges. All this, while shipping a working product with each release that will surprise and delight the end user.

Product Management in The Trenches for a Cloud Service

NUS-ISS

As companies increasingly integrate data across functions, the boundaries between marketing, sales and operations have been blurring. This allows them to find new opportunities that arise by aligning and integrating the activities of supply and demand to improve commercial effectiveness. Instead of conducting post-hoc analyses that allow them to correct future actions, companies generate and analyze data in near real-time and adjust their operations processes dynamically. Transitioning from static analytics outputs to more dynamic contextualized insights means analytics can be delivered with increased relevance closer to the point of decision. This talk will cover the analytics journey from descriptive, predictive and prescriptive analytics to derive actionable and timely insights to improve customer experience to drive marketing, salesforce and operations excellence.

Overview of Data and Analytics Essentials and Foundations

NUS-ISS

With the use of Predictive Analytics, companies are able to predict future trends based on existing available data. The actionable business predictions can help companies achieve cost savings, higher revenue, better resource allocation and efficiency. Predictive analytics has been used in various sectors such as banking & finance, sales & marketing, logistics, retail, healthcare, F&B, etc. for various purposes. Get set to learn more about the different stages of predictive analytics modelling such as data collection & preparation, model development & evaluation metrics, and model deployment considerations will be discussed.

Predictive Analytics

NUS-ISS

In this digital transformation era, we have seen the rise of digital platforms and increased usages of devices particularly in the area of wearables and the Internet of Things (IoT). Given the fast pace change to the IoT landscape and devices, data has become one of the important source of truth for analytics and continuous streaming of data from sensors have also emerged as one of the fuel that revolutionise the emergence of IoT. These includes health telematics, vehicle telematics, predictive maintenance of equipment, manufacturing quality management, consumer behaviour, and more. With this, we will give you an introduction on how to leverage the power of data science and machine learning to understand and explore feature engineering of IoT and sensor data.

Feature Engineering for IoT

NUS-ISS

Master of Technology in Software Engineering

NUS-ISS

Master of Technology in Enterprise Business Analytics

NUS-ISS

In today’s VUCA world, we are faced with problems coming in fast and furious. In order to resolve such problems quickly, we need to first understand the problems. One of the techniques to understand complex problem is through the use of system archetypes. System archetypes are patterns of behaviour of a system. Let’s us explore some of the system archetypes in this session as well as tips on how to resolve them.

Diagnosing Complex Problems Using System Archetypes

NUS-ISS

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Site Reliability Engineer (SRE), We Keep The Lights On 24/7

Product Management in The Trenches for a Cloud Service

Overview of Data and Analytics Essentials and Foundations

Predictive Analytics

Feature Engineering for IoT

Master of Technology in Software Engineering

Master of Technology in Enterprise Business Analytics

Diagnosing Complex Problems Using System Archetypes

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Transforming Healthcare: The Quantification of Everything

1. TRANSFORMING HEALTHCARE THE QUANTIFICATION OF EVERYTHING STEVEN TUCKER, MD, FACP, FAMS Global Health Advisor, SingTel Founder, Tucker Medical

2. WARNING ! We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run. Roy Amara Co-founder, Institute for the Future

3. 1 EXPONENTIALS

4. 1974 1984 1994 2004 2014 EXPONENTIALS 4E+09 3E+09 2E+09 1E+09 4E+03

5. 4E+09 3E+09 2E+09 1E+09 4E+03 COMPUTING POWER 1974 1984 1994 2004 2014 2024

6. 4E+09 3E+09 2E+09 1E+09 4E+03 MOORE’S LAW 1974 1984 1994 2004 2014 2024

7. Computers 4E+09 3E+09 2E+09 1E+09 4E+03 MOORE’S LAW 1974 1984 1994 2004 2014 2024

8. Computers 4E+09 3E+09 Sensors 2E+09 1E+09 4E+03 MOORE’S LAW 1974 1984 1994 2004 2014 2024

9. Computers 4E+09 3E+09 Sensors 2E+09 1E+09 4E+03 MOORE’S LAW Biology 1974 1984 1994 2004 2014 2024

10. Computers 4E+09 3E+09 Sensors 2E+09 1E+09 4E+03 MOORE’S LAW Biology Pan-omics 1974 1984 1994 2004 2014 2024

11. 2 PROBLEMS

12. Healthcare is not a science problem, it’s an information problem. -Thomas Goetz

13. DIABETES 382 Million Source: International Diabetes Federation

14. OBESITY 500 Million Source: World Health Organisation

15. HEART DISEASE 17.3 Million Source: World Health Organisation

16. CANCER 14.1 Million Source: World Health Organisation

17. 3 SENSORS

18. If you cannot measure it, you cannot improve it. -Lord Kelvin

19. SENSOR EVOLUTION Sensor 1.0 Sensor 2.0 Sensor 3.0 Ability to measure + record signal Sensor web integration; share + access data Passive data gathering + aggregating + meaningful insight

20. ACTIVITY

21. HEALTH

22. MEDICAL

23. THE TRICORDER

24. VITAL SIGNS

25. EMERGENCY

26.

27.

28.

29.

30.

31.

32.

33.

34.

35.

36.

37.

38.

39.

40.

41. BIRTH CONTROL

42. WIRELESS BIRTH CONTROL

43. 4 BIOLOGY

44.

45.

46. THERANOS

47. RETAIL LABS

48. HOME TRACKING

49. HOME DIAGNOSIS

50. HOME DIAGNOSIS

51. LAB ON A CHIP

52. CELL CAPTURE

53. LIQUID BIOPSY

54.

55. GENOME MATH 23Pairs of chromosomes

56. GENOME MATH 20k Genes

57. GENOME MATH 4Bases A - T G - C

58. GENOME MATH 3.2 Base pairs per genome billion

59. GENOME MATH 3.2billion 40xCoverage BP 128billion data points

60. SEQUENCING $

61. GENOMICS 2001

62. GENOMICS 2007

63. GENOMICS 2013

64. GENOMICS 2014

65. GENOMICS 2014

66. GENOMICS 2016

67. GENOMICS 2018

68.

69. NEXT GEN SEQUENCING

70. NEXT NEXT GEN SEQUENCING ?

71.

72.

73. 5 PLATFORMS

74.

75. $2.4 Billion

76. $26 Billion

77. SENSORS

78. SENSORS Gyroscope Proximity

79. SENSORS Gyroscope Proximity Gesture Fingerprint

80. SENSORS Gyroscope Proximity Gesture Compass Fingerprint Hall cover

81. SENSORS Gyroscope Gesture Heart rate Proximity Compass Barometer Fingerprint Hall cover

82. SENSORS Gyroscope Gesture Ambient light Heart rate Accelerometer Proximity Compass Barometer Fingerprint Hall cover

83.

84.

85.

86. 6 PAN-OMICS

87.

88.

89.

90.

91. Exposome 33 48 Cd 80 Hg 28 Ni 81 82 Ti Pb As

92. EPphigeennoommee

93. ! TRANSFORM

94. Digital Health as Google Map HEALTH AS A GOOGLE MAP SENSOR ACTIVITY DIET GENOME MICROBIOME LAB PHENOME UNIQUE PERSON

95. Digital Health as Google Map HEALTH AS A GOOGLE MAP SENSOR ACTIVITY DIET GENOME MICROBIOME LAB PHENOME UNIQUE PERSON

96. 6P4 MEDICINE

97. P4 MEDICINE 1. Predictive 2. Preventive 3. Personalized 4. Participatory

98. 100k WELLNESS PROJECT 1. Predictive 2. Preventive 3. Personalized 4. Participatory

99. P4 MEDICINE Predictive You will track your health. It’s your data. Identify transition states.

100. P4 MEDICINE Preventive Wellness networks. Illness networks. A systems-based approach to repair and maintenance.

101. P4 MEDICINE Personalized You are your own control. Your health data cloud. Molecular + Environmental.

102. P4 MEDICINE Participatory Transformative for health. Societal change - enormous. Patients will adapt. Doctors may adapt?

103. THE PIVOT Health Status 20% of patients = 80% of costs Healthy Low Risk At Risk High Risk Chronic Disease Early Active Disease End of Life Care Chronic Disease Progression VALUE COST

104. 6 LONGEVITY

105.

106. Life Expectancy at birth 85 82 79 76 73 70 1990 2000 2008 1990 2000 2008 1990 2000 2008 1990 2000 2008 < 12 12 13-‐15 > 16 Years of Educa>on

107. TECHNOLOGY

108. BIOLOGY

109.

110. CALICO

111. 7 AND…

112.

113.

114.

115.

116.

117.

118.

119.

120. THANK YOU For your 9me & a>en9on

Transforming Healthcare: The Quantification of Everything

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