"Insourcing Innovation - presentation by Sansum's David Kerr at the Summer 2014...

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.uk/category/video/

Diabetes Care TodayADA 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

25,390 children and young people with diabetes

HbA1c <7.5% 17%

HbA1c 7.5 - 9.0% 56%

HbA1c >9.0% 27%

Personal DiabetesAged 46•Type 1 diabetes 35 years•HbA1c 6.1%•Mother, athlete, researcher•CSII, CGMS•No complications•No severe hypoglycemia

20 minutes

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

“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”

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 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

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

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 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”

Natural Pancreas

Glucose

Artificial Pancreas

Doyle F J et al. Dia Care 2014;37:1191-1197

Fully Automated Closed-Loop SystemKudva, 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, Individual, Scalable, Societal

Adaptive Diabetes Systems- Roadblocks

Human

Machine

Outcomes

Device Semantic Gap

Evidenced

TrustworthyWorthwhile

Provenance

Smart Diabetes Society

• Devices (open, interoperable)• Data (semantics)• Architecture (cloud)• Adaptation – (physiological)• Adaptation – (learned)• Social (big data)• Effectiveness (evidence, trust)• Incentives (stickiness)

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 OutcomesGlucocentricPerfomanceQualitative

Share

Smart Diabetes Society - Travel

Smart Diabetes Society – Mass Gatherings

Smart Diabetes Society

Life Moments

Human Experience

Knowledge

Social Interaction

Adaptive Machines

Devices Data

Crowd

Kerr D, Deus Ex Machina Prim Care Diab 2011

UCSanta Barbara/Sansum Consortium for Diabetes Innovation

• Chemical Engineering (Artificial Pancreas)• Computer Science (Cloud Computing)• Digital Games Research (Health Games)

Smart Diabetes Society

Open DataInteroperability

Smart Diabetes Society

Smart Diabetes Society

dkerr@sansum.org