Smart Society George Kampis & Stuart Anderson: Edinburgh University, Paul Lukowicz: DFK Presentation from ECAL 2013

1. Human‐machine Coexistence in
Groups
George Kampis Paul Lukowicz Stuart Anderson
DFKI (German Research Institute for Artificial Intelligence)
DFKI (German Research Institute for Artificial Intelligence),
Kaiserslautern, Germany
Edinburgh University, Edinburgh, United Kingdom
26/09/2013
www.smart‐society‐project.eu

2. “classical” telemonitoring
“l
l” l

3. Scoring scheme
h

4. Results
l
 Patients like the system –
Patients like the system
 Scoring is de‐personalised
Scoring is de personalised
primarily because it gave them  Contextual factors are
privileged access to doctors...
excluded
 Doctors orried abo t
Doctors worried about:
 Difficult to prioritize patients
 High false positive rate
 Brittle response even at
 Over diagnosis
small scale
small scale
 Over treatment
 “In participants with a history
of admission for exacerbations
of admission for exacerbations
of COPD, Telemonitoring was
not effective in postponing
hospital admissions or
hospital admissions or
reducing healthcare costs.”

5. Diagnosis
 Capturing Context
Capturing Context
 Shifting Context
Shifting Context
 Pervasive and mobile
data capture
data capture
 Formal monitoring
activity
 Open‐ended
 Social
 Negotiation of key
features
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 Individual
 Global systemic
Global, systemic
 Individualised
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6. Diagnosis
 Cultures
 Learning
 Domestic: concerned,
inexpert
 Call Centre: risk averse,
inexpert
 General Practitioner:
risk averse, expert,
resource constrained
 Acute Care: uninvolved
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 More or less open loop
 Easy to create
Easy to create
“revolving door” cases.
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7. “Social Sensemaking”
“
l
k ”
 Individual family and carers “curate” the monitoring time
Individual, family and carers curate the monitoring time
series.
 Capture rich context – environment patient condition
Capture rich context – environment, patient condition, …
 Learning algorithm
 Interactive supports negotiation
Interactive, supports negotiation
 Linking cultures supporting the transfer of context
 Individualised
 Learning community
H i
Horizontal links
t l li k
 Transferring experience
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8. Settings
 Cultures
 Context
 Long‐lived
 Value rich (empirical
Value rich (empirical
 Open ended
Open ended
work)
 Range of scale
g
 Shifting and overlapping
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 Rich
 Unexpected
 Capture is inherently
Capture is inherently
hybrid
 Sensors always miss
y
things
 Negotiated
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9. Mechanisms
h
 Semantics
 Mediation
 Tightly linked to
culture/context
 “Good enough” to
understand now
 Open‐ended
 Structured by cultures
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 Value systems differ
 Boundary practices
Boundary practices
enable inter‐working
without agreement
 Key element in crossing
cultures
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10. Mechanisms
h
 Incentives
 Hybridity
 Easy to get wrong
 E g quality of care in
E.g. quality of care in
 In particular the
In particular the
the telemonitoring case
 “Keeping people out of
p gp p
hospital” is probably
better
 Heterogeneous
 Privacy harming?
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 All computation is social
evolution process
 Develop more
p
participatory
approaches to
evolution.
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11. Effects
ff
 Learning
 Alignment
 Opportunity to reflect
 Temporary, requires
 Incorporate reflection
Incorporate reflection
repeated repair
repeated repair
 Incentives encourage
alignment
 Linkage between values
and incentives
 Drives design and
evolution
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12. Conclusions
l
 Focus on Hybridity and Diversity
Focus on Hybridity and Diversity
 Hybridity drives the programming model…
 Di
Diversity drives the data model…
it d i
th d t
d l
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