New Clinical Collaborations for D-Data
Innovations
dkerr@sansum.org
Can Machines Think?
“Can machines help people think?”
“Can people help machines help people think?
http://smart.inf.ed.ac....
Diabetes Care Today
ADA recommendations
•<8.0% 6-12 years
•<7.5% 13-17 years
•<7.0% adults
“while avoiding recurrent hypog...
25,390 children and young people with diabetes
HbA1c <7.5% 17%
HbA1c 7.5 - 9.0% 56%
HbA1c >9.0% 27%
Personal Diabetes
Aged 46
•Type 1 diabetes 35 years
•HbA1c 6.1%
•Mother, athlete, researcher
•CSII, CGMS
•No complications...
Diabetes Care Today
• Unattractive technology
• consumer electronics
• Impersonal technology
• make it mine
• Inaccessible...
“65% of all time downloads from the diabetes app market
are generated by only 14 app publishers. The majority are
small ap...
The current approach to technology
for diabetes care is not working
Insourcing Innovation – 4 Stage Design
• Contextual enquiry
• Embed in the life of users
• Social component
• Problem defi...
Diabetes Care Today
People – process
information, creative,
abstract – high level
Machines – organize ,
analyze, process, ...
The Rise of Social Machines
http://sociam.org/
Smart Society
http://smart.inf.ed.ac.uk/
“Society is moving towards a socio-technical ecosystem in which the physical and ...
Natural Pancreas
βα
Glucose
Artificial Pancreas
Doyle F J et al. Dia Care 2014;37:1191-1197
Fully Automated Closed-Loop System
Kudva, Diabetes Care May 2014
Problem Solving
• Hypo unaware
• Metabolic memory
• New onset T1DM
• Pregnancy planning
Adaptive Diabetes Systems
Non-Adaptive Systems
Adaptive Diabetes Systems
Device
Data Interpreted
Shared
Elaborated
InformationExperienceConsumer
Data
Humans as Consumers...
Adaptive Diabetes Systems- Roadblocks
Human
Machine
Outcomes
Device Semantic
Gap
Evidenced
TrustworthyWorthwhile
Provenance
Smart Diabetes Society
• Devices (open, interoperable)
• Data (semantics)
• Architecture (cloud)
• Adaptation – (physiolog...
Creating a Smart Diabetes Society-
Contextual Enquiry and Problem Definition
Creating a Smart Diabetes Society-
Exploration of alternatives
Information Here and Now
$
$
$
Creating a Smart Diabetes Society-
Rapid Validation
Data
New DataNew ExperienceNew Outcomes
Glucocentric
Perfomance
Qualit...
Smart Diabetes Society - Travel
Smart Diabetes Society – Mass Gatherings
Smart Diabetes Society
Life
Moments
Human
Experience
Knowledge
Social
Interaction
Adaptive
Machines
Devices Data
Crowd
Ker...
UCSanta Barbara/Sansum Consortium
for Diabetes Innovation
• Chemical Engineering (Artificial Pancreas)
• Computer Science ...
Smart Diabetes Society
Open Data
Interoperability
Smart Diabetes Society
Smart Diabetes Society
dkerr@sansum.org
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"Insourcing Innovation - presentation by Sansum's David Kerr at the Summer 2014 DiabetesMine D-Data ExChange

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David Kerr, new director of research & innovation at the Sansum Diabetes Research Institute in Santa Barbara, discusses "insourcing innovation" at the June 2014 DiabetesMine D-Data event in San Francisco.

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  • Taxonomy of the AP design. A specific AP configuration is created by selecting options for each of the major elements shown in the figure. Solid lines demonstrate connections that are always present and dashed lines represent connections that may only be present in some configurations. The tuning, model, and desired glucose concentration are all part of the controller, as signified by the black arrows. Green color distinguishes physiological states or properties from measured or digital signals. Black lines are used to indicate predetermined features of a block, and blue lines indicate signals or actions conducted during closed-loop operation.
  • "Insourcing Innovation - presentation by Sansum's David Kerr at the Summer 2014 DiabetesMine D-Data ExChange

    1. 1. New Clinical Collaborations for D-Data Innovations dkerr@sansum.org
    2. 2. Can Machines Think? “Can machines help people think?” “Can people help machines help people think? http://smart.inf.ed.ac.uk/category/video/
    3. 3. Diabetes Care Today ADA recommendations •<8.0% 6-12 years •<7.5% 13-17 years •<7.0% adults “while avoiding recurrent hypoglycemia” “patients with type 1 diabetes are capable of achieving target HbA1c levels” N = 6229 13-18 year olds 8.8% N= 6862 6-13 year olds 8.4% Beck R et al J Clin Endocrinol Metab 2012; 12: 11
    4. 4. 25,390 children and young people with diabetes HbA1c <7.5% 17% HbA1c 7.5 - 9.0% 56% HbA1c >9.0% 27%
    5. 5. Personal Diabetes Aged 46 •Type 1 diabetes 35 years •HbA1c 6.1% •Mother, athlete, researcher •CSII, CGMS •No complications •No severe hypoglycemia 20 minutes
    6. 6. Diabetes Care Today • Unattractive technology • consumer electronics • Impersonal technology • make it mine • Inaccessible technology • visual, functional, cognitive • One-size-fits-all technology • reservoirs, tubing, strips • Unconnected technology • sync with phone, records, social • Unintelligent technology • Education, learning http://www.diabetesmine.com/2012/11/patients- call-for-innovation-diabetesmine-summit-2012.html
    7. 7. “65% of all time downloads from the diabetes app market are generated by only 14 app publishers. The majority are small app developers” “Mobile diabetes apps are currently used by only 1.2% of the target group”
    8. 8. The current approach to technology for diabetes care is not working
    9. 9. Insourcing Innovation – 4 Stage Design • Contextual enquiry • Embed in the life of users • Social component • Problem definition • What is the exact problem • Avoid expensive premature anchoring • Exploration of alternatives • Divergence in alternatives • Real-time user feedback • Rapid validation • Does it make a difference • Hours and days Asch D et al N Eng J Med May 8th 2014
    10. 10. Diabetes Care Today People – process information, creative, abstract – high level Machines – organize , analyze, process, present information – low level Social – supportive, alternative, non-hierarchical –high level
    11. 11. The Rise of Social Machines http://sociam.org/
    12. 12. Smart Society http://smart.inf.ed.ac.uk/ “Society is moving towards a socio-technical ecosystem in which the physical and virtual dimensions of life are more and more intertwined and where people interaction, more often than not, takes place with or is mediated by machines”
    13. 13. Natural Pancreas βα Glucose
    14. 14. Artificial Pancreas Doyle F J et al. Dia Care 2014;37:1191-1197
    15. 15. Fully Automated Closed-Loop System Kudva, Diabetes Care May 2014
    16. 16. Problem Solving • Hypo unaware • Metabolic memory • New onset T1DM • Pregnancy planning Adaptive Diabetes Systems
    17. 17. Non-Adaptive Systems
    18. 18. Adaptive Diabetes Systems Device Data Interpreted Shared Elaborated InformationExperienceConsumer Data Humans as Consumers Adaptive, Individual, Scalable, Societal
    19. 19. Adaptive Diabetes Systems- Roadblocks Human Machine Outcomes Device Semantic Gap Evidenced TrustworthyWorthwhile Provenance
    20. 20. Smart Diabetes Society • Devices (open, interoperable) • Data (semantics) • Architecture (cloud) • Adaptation – (physiological) • Adaptation – (learned) • Social (big data) • Effectiveness (evidence, trust) • Incentives (stickiness)
    21. 21. Creating a Smart Diabetes Society- Contextual Enquiry and Problem Definition
    22. 22. Creating a Smart Diabetes Society- Exploration of alternatives Information Here and Now $ $ $
    23. 23. Creating a Smart Diabetes Society- Rapid Validation Data New DataNew ExperienceNew Outcomes Glucocentric Perfomance Qualitative Share
    24. 24. Smart Diabetes Society - Travel
    25. 25. Smart Diabetes Society – Mass Gatherings
    26. 26. Smart Diabetes Society Life Moments Human Experience Knowledge Social Interaction Adaptive Machines Devices Data Crowd Kerr D, Deus Ex Machina Prim Care Diab 2011
    27. 27. UCSanta Barbara/Sansum Consortium for Diabetes Innovation • Chemical Engineering (Artificial Pancreas) • Computer Science (Cloud Computing) • Digital Games Research (Health Games)
    28. 28. Smart Diabetes Society Open Data Interoperability
    29. 29. Smart Diabetes Society
    30. 30. Smart Diabetes Society dkerr@sansum.org

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