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George Kampis Paul Lukowicz Stuart Anderson
DFKI (German Research Institute for Artificial Intelligence),
Kaiserslautern, Germany
Edinburgh University, Edinburgh, United Kingdom
Human-machine Coexistence in
Groups
18/09/2013 www.smart-society-project.eu
“classical” telemonitoring
Scoring scheme
Results
 Patients like the system –
primarily because it gave them
privileged access to doctors...
 Doctors worried about:
 High false positive rate
 Over diagnosis
 Over treatment
 “In participants with a history
of admission for exacerbations
of COPD, Telemonitoring was
not effective in postponing
hospital admissions or
reducing healthcare costs.”
 Scoring is de-personalised
 Contextual factors are
excluded
 Difficult to prioritize patients
 Brittle response even at
small scale
Diagnosis
18/09/2013 www.smart-society-project.eu 5
 Capturing Context
 Pervasive and mobile
data capture
 Formal monitoring
activity
 Open-ended
 Social
 Negotiation of key
features
 Shifting Context
 Individual
 Global, systemic
 Individualised
Diagnosis
18/09/2013 www.smart-society-project.eu 6
 Cultures
 Domestic: concerned,
inexpert
 Call Centre: risk averse,
inexpert
 General Practitioner:
risk averse, expert,
resource constrained
 Acute Care: uninvolved
 Learning
 More or less open loop
 Easy to create
“revolving door” cases.
“Social Sensemaking”
18/09/2013 www.smart-society-project.eu 7
 Individual, family and carers “curate” the monitoring time
series.
 Capture rich context – environment, patient condition, …
 Learning algorithm
 Interactive, supports negotiation
 Linking cultures supporting the transfer of context
 Individualised
 Learning community
 Horizontal links
 Transferring experience
Settings
18/09/2013 www.smart-society-project.eu 8
 Cultures
 Long-lived
 Value rich (empirical
work)
 Range of scale
 Shifting and overlapping
 Context
 Rich
 Open ended
 Unexpected
 Capture is inherently
hybrid
 Sensors always miss
things
 Negotiated
Mechanisms
18/09/2013 www.smart-society-project.eu 9
 Semantics
 Tightly linked to
culture/context
 “Good enough” to
understand now
 Open-ended
 Structured by cultures
 Mediation
 Value systems differ
 Boundary practices
enable inter-working
without agreement
 Key element in crossing
cultures
Mechanisms
18/09/2013 www.smart-society-project.eu 10
 Incentives
 Easy to get wrong
 E.g. quality of care in
the telemonitoring case
 “Keeping people out of
hospital” is probably
better
 Heterogeneous
 Privacy harming?
 Hybridity
 All computation is social
 In particular the
evolution process
 Develop more
participatory
approaches to
evolution.
Effects
18/09/2013 www.smart-society-project.eu 11
 Learning
 Opportunity to reflect
 Incorporate reflection
 Drives design and
evolution
 Alignment
 Temporary, requires
repeated repair
 Incentives encourage
alignment
 Linkage between values
and incentives
Conclusions
18/09/2013 www.smart-society-project.eu 12
 Focus on Hybridity and Diversity
 Hybridity drives the programming model…
 Diversity drives the data model…

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Human-machine Coexistence in Groups

  • 1. George Kampis Paul Lukowicz Stuart Anderson DFKI (German Research Institute for Artificial Intelligence), Kaiserslautern, Germany Edinburgh University, Edinburgh, United Kingdom Human-machine Coexistence in Groups 18/09/2013 www.smart-society-project.eu
  • 4. Results  Patients like the system – primarily because it gave them privileged access to doctors...  Doctors worried about:  High false positive rate  Over diagnosis  Over treatment  “In participants with a history of admission for exacerbations of COPD, Telemonitoring was not effective in postponing hospital admissions or reducing healthcare costs.”  Scoring is de-personalised  Contextual factors are excluded  Difficult to prioritize patients  Brittle response even at small scale
  • 5. Diagnosis 18/09/2013 www.smart-society-project.eu 5  Capturing Context  Pervasive and mobile data capture  Formal monitoring activity  Open-ended  Social  Negotiation of key features  Shifting Context  Individual  Global, systemic  Individualised
  • 6. Diagnosis 18/09/2013 www.smart-society-project.eu 6  Cultures  Domestic: concerned, inexpert  Call Centre: risk averse, inexpert  General Practitioner: risk averse, expert, resource constrained  Acute Care: uninvolved  Learning  More or less open loop  Easy to create “revolving door” cases.
  • 7. “Social Sensemaking” 18/09/2013 www.smart-society-project.eu 7  Individual, family and carers “curate” the monitoring time series.  Capture rich context – environment, patient condition, …  Learning algorithm  Interactive, supports negotiation  Linking cultures supporting the transfer of context  Individualised  Learning community  Horizontal links  Transferring experience
  • 8. Settings 18/09/2013 www.smart-society-project.eu 8  Cultures  Long-lived  Value rich (empirical work)  Range of scale  Shifting and overlapping  Context  Rich  Open ended  Unexpected  Capture is inherently hybrid  Sensors always miss things  Negotiated
  • 9. Mechanisms 18/09/2013 www.smart-society-project.eu 9  Semantics  Tightly linked to culture/context  “Good enough” to understand now  Open-ended  Structured by cultures  Mediation  Value systems differ  Boundary practices enable inter-working without agreement  Key element in crossing cultures
  • 10. Mechanisms 18/09/2013 www.smart-society-project.eu 10  Incentives  Easy to get wrong  E.g. quality of care in the telemonitoring case  “Keeping people out of hospital” is probably better  Heterogeneous  Privacy harming?  Hybridity  All computation is social  In particular the evolution process  Develop more participatory approaches to evolution.
  • 11. Effects 18/09/2013 www.smart-society-project.eu 11  Learning  Opportunity to reflect  Incorporate reflection  Drives design and evolution  Alignment  Temporary, requires repeated repair  Incentives encourage alignment  Linkage between values and incentives
  • 12. Conclusions 18/09/2013 www.smart-society-project.eu 12  Focus on Hybridity and Diversity  Hybridity drives the programming model…  Diversity drives the data model…