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From the Data Drought to the Data Deluge
1. Big Data “as a solution” is a myth
2. Sharing difficult - G.I.G.O
3. Meaning what does it mean to us?
4. Purpose orientation: politics, economics,…
“The Web” will evolve(probably no matter what we say …)
Can we influence it ? – how ?
1. Ends-based thinking: Utilitarian approach (Betham, Mill, Sidgwick)
• best for greatest number of people in terms of ends
2. Rule-based thinking: Universal law (Kant)• follow the principle that you want everyone else to follow.
3. Care-based thinking: Golden rule (most religions) • Do to others what you would like them to do to you.
4. Empirical thinking: (empiricists –scientist)• let us see what can be done and develop indicators
5. Chaotic thinking: (all the rest …)• Let diversity get its own course
The Gartner HYPE CYCLE: Where are WE ?
http://en.wikipedia.org/wiki/Hype_cycle
Viewing as a solution • Combining data from all different sources
• with different structures
ABSENT the Common Trust – Networks of Owners -Functions – Meaning
• Humongous Data Information Knowledge
Intelligence Wisdom
• Insight• Decision analysis• Prediction• Intervention
is only a myth
Shepherdingsimple requirements
1. Count your sheep• How many born ? • How many dead ?
2. Don’t cry wolf !
Reporting of Mortality in the World
Source WHO 2014
Information Paradox
0
100000000
200000000
300000000
400000000
500000000
600000000
700000000
800000000
1 2 3 4
YL
Ls
VR countries vs No VR
Burden of Mortality
Carpet burnt
Millenium Development Goals
from Information Technology Report 2013, by the World Economic Forum
Worldwide distribution of real-time behavious sensing systems(aka cell phones)
Data Collected But Not Used
the information YOU -
₋ have is not what you want
₋ want is not what you need
₋ need is not what you can have
Finagle's Law of Information have
want
need
In other words there is always a gap between what you have, need or want
Sharing “Health Information”
ComputationalProcessing
Knowledge
INPUTS
Analytical processOUTPUT
• Mechanisms
• Interventions
• Policies
• Statistics
• Aggregation
• Ontologies
• Data
• Big Data
in health
Reinventing the Meaning
Meaning of Global Health
• is NOT ALONE control of communicable diseases
• Can not be reduced to syndromic surveillance
• Can not be reduced to Health Security
is about LIFEaccess to water and foodImmunity Environment
Paranoia of Big Brother
• How can the selfish meme be altruistic ?
• Humans are NOT digital
• Emotions about unknowns
• Emotions about knowns
MAYBE WE SHOULD WATCH BIG BROTHER
It is not about size, it is what you do with it !
•Size• Is small still beautiful ?
•Relevance• Maps vs territory
•Big Data overload• information overload
Global Data - opportunities
• Direct generation /feedback from citizens
• Real-time health monitoring
• Creating NETWORKS
• Connecting data – hubs
• NETWORKING networks
• Availability/Mobility of services
• Confederating the Global Data
Sharing Data - requirements
• Capturing• Processing• Filtering• Calibrating • Putting in a scale • Comparing - Interpreting• Merging – Sorting - Integrating • Standardizing• Using - Re-using • Putting in perspective
Linked data Linked people
• Open data
• With meta data
• Transparency
• Citizens
• Enterprises
• Academia
• Governments
Trust B
Trust A
what data elements could be linkedShared with whom?
“Chemistry” of data binding
Smart Analysis of Big Data
Eco-systems of Health Information
Can we build the Big Intelligence ?
GIGO: Garbage In
Out ?
Value of Information
• the amount a decision maker would be willing to pay for information prior to making a decision• How much will you pay for weather forecast ?
• How much will you pay for maternal mortality information ?
• If you were to sell it what would be the price?
Is Health less valuable than Stock Exchange?
Personalised Healthcare = targeted therapy ?
… BUILDING BLOCKS OF HEALTH INFORMATION …
Avoiding an e-tower of Babel
Grade 3 hypertension
Grade 2 hypertension
Grade 1 hypertension
High normal
normal
optimal
120 130 140 150 160 170 180
Systolic pressure
Dia
sto
lic p
ress
ure
172
102
110
105
100
95
90
85
80
Knowledge Representation
41
Beyond
• Search using Concepts above Words• How many patients do have diabetes mellitus type II?
• Extraction of Concepts from Health Records• Automated extraction of HbA1c results of selected patients with DM type II from lab
reports within last year
• Statistical Index on Community Collections• Calculation of coverage gap for treatment need for diabetes mellitus
• Concept Navigation across Collections• Comparison of region A with region B etc
42
What do we do with our “time” ?
Trends From To
• Centralized Data
• Restrictions
• Fragmented
• Shared data
• Enablers
• Linked
Global Village … It takes a village…
It takes the whole globeto share data
