A general overview of Big Data in Healthcare with 3 slides explaining adoption problems in Taiwan

1. Geber Brand Consulting
格博品牌行銷顧問公司

2. 2
About Us
關於我們

3. 3
2008
Affinity-Asia Brand
Consulting established.
Richard deVries成立
愛芬尼品牌顧問公司
2010 2011 2012
Film department
established.
成立設計及影片部門
Awarded as a Preferred
Vendor for Marketing
Excellence in the
Semiconductor Industry
榮獲半導體產業行
銷首選顧問公司
Selected as one of the
top brand consulting
companies by TAITRA
獲選為TAITRA優
質品牌顧問公司
Our History - Timeline

4. 4
2013 2014 2016
Shanghai office
成立上海辦公室
Selected by TIER as a key
branding consultancy for
"Branding Taiwan 2.0”
獲選為TIER「台灣品
牌2.0計畫」重點品牌
顧問公司
Rebranded as "Geber
Brand Consulting” to
reflect our growing
global presence
改名為格博品牌顧問，
並重建品牌，呈現更
國際化的服務
Dallas, Texas Leipzig,
Germany
成立德州、德國辦公室
Cooperated with
Microsoft Taiwan for
internal training and
branding
台灣微軟內部培訓首選
顧問公司

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Your ‘virtual marketing team’
您的“虛擬營銷團隊”
Full-Solution Brand Consultancy
全方位解決方案的品牌顧問
Offices in Taipei and Shanghai, and
a list of affiliates in major markets.
於台北、上海擁有辦公據點並在每個
主要市場都有子公司

6. North America
 New York (6)
 Toronto (6)
 Los Angeles (5)
 San Francisco (4)
 Vancouver (3)
Europe
• United Kingdom (5)
• Germany (3)
• Denmark (2)
• Italy (2)
• Austria (2)
• Netherland (2)
• Finland (1)
• France (1)
Other
• Sydney (2)
• Buenos Aires (1)
Asia
• Hong Kong (4)
• Kuala Lumpur (2)
• Singapore (2)
• Tokyo (2)
• Mumbai (1)
Global Network of PR Contacts

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8. WHERE DOES THE DATA COME FROM?
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Blood pressure
Glucose
Temp
Calories
Steps
Skin
etc.
SENSORS

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11. Blood pressure
Glucose
Fitness etc. sensors
DOCTOR
DATA
CENTER
COMPARE IMPROVE

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Predict impending
heart attacks?

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Fitness data
Patient data
Imaging technology
Insurance data
Outbreak data
Genetic data
Social media

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Passive sensors = no electricity Nanosensors?

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Patient data
Compare and analyze
Develop models
Preventative medicine

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Cost down
due to
efficient
data use
Cost down
due to
prevention

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Staff
Patient
Insurance
Government
Time
Care
Money
Votes

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WATSON

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168000 doctors free to do more important work

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Teleradiology
AI Radiology
Faster?
More efficient?
More correct?
Cheaper?

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Robotic
medicine
Can we create
blueprints for
automated
surgeries?

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DOCTORS TODAY DOCTORS TOMORROW

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Example: ITU (United Nations)
Official data from national Mobile Network Operators (MNOs)
Combine with social network data
All data is anonymized
Data is then stored locally or in cloud
Processed and turned into a dynamic map by ITU
=> Better outbreak management

29. CLINICAL TRIALS
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RANDOM SELECTION ANALYSIS-BASED SELECTIOn

30. OPTIMIZED CANDIDATE SELECTION
FASTER ORGAN DONOR MATCHING

32. ANONYMOUS COMPARISON OF PATIENT DATA
BETTER, FASTER TREATMENT AND …

33. AI SOLUTIONS
“DOCTOR
IN
THE
MACHINE”

34. EXAMPLE: DERMATOLOGY
34
80% involve simple visual diagnosis
Upload a picture using cellphone
AI system quickly compares picture to database of millions
Suggests right course of treatment

35. Doctors heed the computer’s advice about 2/3 of the time
Current algorithms are already very good
90% of doctors say “It was smarter than my intuition.”
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85% of all procedures
in a hospital are routine
and can be automated

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90% of patients would trust
AI if it’s faster and cheaper
免掛號，免付費，免等候
阿公阿嬤 真歡喜

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Big data based
automation can
theoretically free up 50%
doctors’ and 70% of
nurses’ time to care better
for patients

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US will lead in AI medicine
Other countries can develop
- Indigenous systems
- Consultative systems
- Asia = Asian American?

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If you don’t do it …
Startups and AI
will steal your patients

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Medicine without borders

43. EXAMPLE: GENERAL CHECKUP
43
Blood, urine, tests for STIs, glucose, liver and kidney
function etc.
+ age, ethnicity, health status, habits
=> compare to millions of other patients
= Early warning system

44. EXAMPLE: CANCER MARKERS
44
No we are testing for 40-100 specific cancer markers
We can soon scan for 20000+
Combined with overall genetic information, we will detect
cancer before onset

45. PATIENTS LIE
DATA DOES NOT
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54. BIGGEST TRENDS 2016
54
• Data visualization
• Cloud-based solutions (pay as you go)
• Privacy
• Data collection (IoT)
• Digitalization of patient records
• Interdisciplinary data structures

55. 28% OF CANCERS ARE MISDIAGNOSED

56. THE PROBLEMS
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CURRENT HOSPITAL IT
SQL based
Relational
Structured
Outdated

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FUTURE HOSPITAL IT
Multi-query
Unrelational
Unstructured
Interdisciplinary
National and global
standards

61. INDIVIDUAL HEALTHCARE PROVIDERS
WILL HAVE TO …
61
Invest in hardware and software
Hire data analysis experts
Hire computer security experts
Develop privacy protection systems
ALL ON A MASSIVE SCALE

62. Healthcare transition in Taiwan:
realities and
future trends

63. Global Healthcare: Dramatic reforms
Fee for
Service
Physician
Turf warsSilos
Outcome
Based
Patient
Centric
Collaborati
on
Team-
work
Old Environment New Ecosystem

64. Taiwan’s unique problems
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• High-performance universal health system with no vision
• No understanding of cost-down advantage of big data
• Every hospital a kingdom
• Overworked doctors and nurses have no time for patients
• Up to 40% cost wasted on unnecessary doctor’s visits
• Ageing population averse to technology
• Rise of NCDs

66. Taiwan’s NCD paradox
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NCDs accounts for 79.3 % of all deaths in Taiwan
Cancer 29 percent of all deaths in 2013
Three major NCDs—cancer, cardio- and cerebral-vascular disease 45%
These are the areas where big data brings the biggest benefits
These are the areas where Taiwan invests the least

67. Catastrophic illness = highest cost
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Exempts all copayments and coinsurance to patients with one or more
of 30 catastrophic diseases, residents of remote mountainous areas
and offshore islands, pregnant women and child delivery,
children under three, veterans and their dependents,
and low-income households.
Chronic renal failure and cancer have the highest outpatient costs
(45% and 33.5%) of NHI outpatient and 30% of inpatient (incl. LT ventilation)
Both renal failure and cancer can be greatly reduced by investing in
big-data solutions, i.e. monitoring and preventive models

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