E health opportunities monguet oct 2014 vilnius

EHealthopportunities
Josep Mª Monguet / Vilnius, Oct 28th 2014.
jm.monguet@upc.edu http://thepracticeofinnovation.net
thepracticeofinnovation.net eHealth Opportunities

EHealthopportunities
Data/Time/Space
Management
Augmented
Learning services
communities
Accelerated
Collective Knowledge
intelligence Health land

Data/Time/Space
Management
Health land
Ehealth opportunities
Augmented
Learning services
communities
Accelerated
intelligence

Health land
Expenditure Inefficiencies

Health land expenditure
OECD average
2000 2010
18
16
14
12
10
8
6
4
2
0
% of GDP
OECD Health Data 2013

1
2
3 4 5 6 7
0 CC (no chronic conditions )
8 CC
0 10 20 30 40 50 60 70 80 +85
100
75
50
25
0
% of people by age
Data for Southampton city 2012

100
90
80
70
60
50
40
30
20
1850 1900 1950 2000 2050
Life expentancy
OECD Health Data 2013

30% of health
spending
is intended to be
ineffective or
unnecessary care,
resulting from the
impact of medical
errors & redundancy
procedures*
inefficiencies
Health land
* Elliott Fisher & John E. Wennberg

Ehealth opportunities
Augmented
Learning services
communities
Data/Time/Space
Management
Health land
Accelerated
intelligence

DTS management
Adena Regional Medical Center
distances

DTS management
TRHLAB
time

DTS management ubiquity

DTS management not enough

Augmented services Monitoring
“your pill is watching you“

Augmented services Integrating
user
therapist
Primary care
families
eTona 2008

eHealth Opportunities
Learning
communities
Augmented
services
Data/Time/Space
Management
Health land
Accelerated
intelligence

Learning communities sharing
e-fer. IPL / UPC 2008

Learning communities participating
TRHLAB. Living Learning Community for Spasticity

eHealth Opportunities
Augmented
Learning services
communities
Data/Time/Space
Management
Health land
Accelerated
intelligence

Accelerated knowledge
GDP
World Patenet filling
Global Patent. Andrei Ionescu
production

Collective intelligence networks

Collective intelligence big data
Detecting influenza epidemics using search engine query data. Google & C. Disease Control & Prevention. Nature 2009

Collective intelligence sharing

Collective intelligence brilliant

Collective intelligence
Applications & modalities

Collaborative design of health models
Case
Selection of chronic care
indicators

Selection of chronic care Indicators
1
Model
Revision of the
proposed model
of indicators
2
Profile
Personal
experience and
knowledge
3
Round
Votes and
opinions for
consensus
4
Evaluation
Usability and
utility of the
consensus

We ask you to answer for each indicator the questions proposed.
1/36
% of patients
enrolled in
“Adherence to the
pharmacological
treatment”
Program
Which is the level of relevance of this indicator
Low High
1 2 3 4 5 6
Capacity to calculate affectively this indicator?
1 2 3 4 5 6
Very limited
Feasible
Is this indicator important for the patient?
Not at all To much
1 2 3 4 5 6

N: 37
1 2 3 4 5 6
1/36
% of patients
enrolled in
“Adherence to the
pharmacological
treatment”
Program
Low High
1 2 3 4 5 6
1 2 3 4 5 6
Very limited
Feasible
1 2 3 4 5 6
Not at all
To much

N: 37
1 2 3 4 5 6
1/36
% of patients
enrolled in
“Adherence to the
pharmacological
treatment”
Program
Low High
1 2 3 4 5 6
1 2 3 4 5 6
Very limited
Feasible
Not at all To much
1 2 3 4 5 6
N: 37
1 2 3 4 5 6

N: 37
1 2 3 4 5 6
1/36
% of patients
enrolled in
“Adherence to the
pharmacological
treatment”
Program
Low High
1 2 3 4 5 6
1 2 3 4 5 6
Very limited
Feasible
Not at all To much
1 2 3 4 5 6
N: 37
1 2 3 4 5 6
N: 37
1 2 3 4 5 6

Clinic Management Planning Weighting of your
answers according to
your personal profile of
knowledge and
experience
Clinic
Management
Planning
1 2 3
1 2 3
1 2 3

Meta results
Communicating chronic management strategy (Learning).
Alignment of the system via consensus (Decision Making).
Collaboration for the establishment of priority indicators.
Broad participation of healthcare professionals in
identifying needs and opportunities.

Publication
Paper in press …

Innovation participative space
Case
Primary Care Innovation

Primary Care Innovation

Innovation participative space
Consensus on clinical cases.
Case
Training on mental health

Training on mental health

Methods of application
Delphi Express Continuous

Conclusions
1. In the health area, professionals respond
positively to the model of HC participation
2. The HC process is efficient and operational as
shown by satisfaction levels and perception of
involvement.
3. Professionals perceive that they provide value
with their participation.
4. The results of participation are considered
useful and relevant contributions.

E health opportunities monguet oct 2014 vilnius

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Editor's Notes