deliverable 18.1 dissemination and training strategy plan and ...€¦ · d18.1 - dissemination and...

D18.1 - Dissemination and training strategy plan and preliminary materials

MD-Paedigree - FP7-ICT-2011-9 (600932)

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Model Driven Paediatric European Digital Repository

Call identifier: FP7-ICT-2011-9 - Grant agreement no: 600932

Thematic Priority: ICT - ICT-2011.5.2: Virtual Physiological Human

Deliverable 18.1

Dissemination and training strategy plan

and preliminary materials

Due date of delivery:28-02-2014

Actual submission date:

Start of the project: 1st March 2013

Ending Date: 28th February 2017

Partner responsible for this deliverable: LYNKEUS

Version: 1.4



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Title Dissemination and training strategy plan and preliminary materials

Deliverable 18.1

Reporting Period 15th March 2013 – 1st March 2014

Authors LYNKEUS

Work Package 18 – Dissemination & Training

Security Public

Nature Report

Keyword(s) Dissemination, training, material

Document History

Name Remark Version Date

Vanessa Diaz Training planning draft 1.0 05-12-2013

Almerico Bartoli First Complete draft 1.1 18-01-14

Mirko De Maldè Second complete draft 1.2 25-02-14

Mirko De Maldè Third complete draft 1.3 27-02-14

List of Contributors

Name Affiliation

Vanessa Diaz UCL

Mirko De Maldè LYNKEUS

Almerico Bartoli LYNKEUS

List of reviewers

Name Affiliation

Bruno Dallapiccola OPBG

C. MacGregor LYNKEUS

Edwin Morley-Fletcher LYNKEUS

Abbreviations

DoW Description of Work

MD-PAEDIGREE MD-Paedigree

Dos Dissemination Objects



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Table of Contents 1. Rationale ............................................................................................................................................ 5

2. Dissemination strategy plan ............................................................................................................... 5

2.1 Vision and key messages ............................................................................................................. 5

2.2 The strategy ................................................................................................................................. 5

2.3 MD-Paedigree and the Big Data Challenge ................................................................................. 6

2.4 The target audiences ................................................................................................................... 6

3. Most significative dissemination activities and achievements in the first year ...................................... 8

3.1 MD-Paedigree awarded as Best Exhibit at ICT ’13 ...................................................................... 8

3.2 Attended Conferences................................................................................................................. 9

3.3 Press coverage ........................................................................................................................... 10

3.4 Next steps .................................................................................................................................. 18

4. Preliminary dissemination materials and channels ............................................................................ 21

4.1 Md-Paedigree Logo ................................................................................................................... 21

4.2 MD-Paedigree in One Slide ....................................................................................................... 21

4.3 The MD-Psaedigree Kick-Off Meting Poster and Biannual Meeting Poster .............................. 22

4.3 Newsletter ................................................................................................................................ 23

4.4 Project website .......................................................................................................................... 23

Public website ............................................................................................................................. 23

Private website ............................................................................................................................ 24

4.5 Social Networks ......................................................................................................................... 24

4.6 Project Posters .......................................................................................................................... 25

4.7 ID Card ....................................................................................................................................... 26

4.8 Project’s “Business Card” .......................................................................................................... 26

4.9 Publications ............................................................................................................................... 27

5. Training Strategy .............................................................................................................................. 29

5.1 Introduction ............................................................................................................................... 29

5.2 Training Event (1) in a nutshell: Training clinicians to upload and use the MD-Paedigree’s

Repository ....................................................................................................................................... 29

5.2 The organisation of both Training Events can be broken down into 6 General Tasks .............. 30

5.3 Supporting materials ................................................................................................................. 33



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Index of Figures Fig. 1 – ICT 2013 Best Exhibit award ..................................................................................................... 8 Fig 3. – Screenshot from http://youtu.be/b8qIZS2j2T8 ......................................................................... 9 Fig. 4 – The MD-Paedigree Poster for the EHealth Forum Gastein ....................................................... 9 Fig 5. Article about MD-Paedigree published on “La Repubblica“ ...................................................... 11 Figure 6 – MD-Paedigree News for SIEMENS GLOBALE ...................................................................... 14 Fig. 7 – The UCL Institute of Child Health Entrance ............................................................................. 16 Fig.8 – One of the MD-Paedigree partner explaining .......................................................................... 16 the work perfomed .............................................................................................................................. 16 Fig.9 – The Plenary Session of the Internal Review ............................................................................. 16 Fig. 13 - Alberto Sanna ........................................................................................................................ 17 Fig.12 - Adam Shortland ...................................................................................................................... 17 Fig. 11 - Rolando Cimaz ....................................................................................................................... 17 Fig.10 - Marco Bonvicini ...................................................................................................................... 17 Fig.14 – The MD-Paedigree Logo ........................................................................................................ 21 Fig.15 – The MD-Paedigree infographic .............................................................................................. 21 Fig.16 – Kick-Off Meeting poster ......................................................................................................... 22 Fig.17 – Biannual Meeting poster ....................................................................................................... 22 Fig.18 – The MD-Paedigree Newsletter Frontpage ............................................................................. 23 Fig.19 – The MD-Paedigree Website Homepage ................................................................................ 23 Fig.20 – The MD-Paedigree Twitter Account ....................................................................................... 24 Fig.21 – The MD-Paedigree posters (one per disease area) ................................................................ 25 Fig.22 – The MD-Paedigree ID Card .................................................................................................... 26 Fig.23 – The MD-Paedigree “Business card” ....................................................................................... 26 Fig. 24 - Training event 1 in a nutshell (location still not confirmed) .................................................. 30



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1. Rationale This document outlines some preliminary elements of a comprehensive and effective dissemination strategy for MD-Paedigree (MD-PAEDIGREE), drawn up according to a 4 year plan which will be updated yearly as the project evolves. In this sense, the dissemination strategy should be considered as an evolutionary strategy, which will be implemented taking into account the most effective means of dissemination for the specific achievements reached during the project, and possibly adding to the tasks already indicated in this document new activities for appropriately disseminating the actions and results of MD-PAEDIGREE. In fact, the dissemination channels foreseen, deemed promising at present, may in the future be found to be unattractive and/or ineffective, while new ones may demand further exploitation. In the first part of the document, the dissemination strategy is explained, followed by a brief outline of the activities already implemented in the first year of the project. The second part of the document is devoted to the presentation of the different dissemination materials and channels implemented so far. The third and final part serves as a preliminary explanation of the training activities that will be implemented during the project.

2. Dissemination strategy plan 2.1 Vision and key messages MD-PAEDIGREE has the overarching ambition to develop a strong brand image, 5ptimize5l by both biomedical communities and IT experts, in order to become a quality label and a reference for the application of advanced technology in biomedical informatics. The MD-Paedigree central vision is of the project outputs resulting in a lasting and stable community of paediatric patients, parents and institutions who work around and with the MD-Paedigree Infostructure to bring together more and more knowledge, experience, data and technology to provide a clinical decision support system. Along side this vision is the aim to provide a knowledge sharing and discovery tool for paediatric research. Furthermore, MD-Paedigree looks toward the subsequent involvement of new subjects, which will make the models implemented in the project more reliable, thanks to the quality, depth, and volume of clinical data accruing into its federated digital repository, which will become larger and will be expanded to new pathologies and new clinical centres. It is the communication of these visions that is the primary motivation of the MD-Paedigree dissemination strategy.

2.2 The strategy In coherence with the above described vision, the MD-Paedigree dissemination has been conceived, since the very beginning of the project, to be bidirectional, in order to make it possible to disseminate the results, and in the meanwhile trying to bring in the platform new knowledge, data, and feedback from external organisations and experts, also in order to ensure greater compatibility of the outcomes of the project with other projects and existing technology.



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In order to reach in the most effective way the end users (clinicians and patients), part of the MD-Paedigree dissemination has been focused on the clinical use cases for the four different pathologies initially addressed by the project. These four use cases, were prepared by the clinical experts involved into the project. Finally to ensure the success of the dissemination activities, all the partners have at least one person month of involvement in WP18.

2.3 MD-Paedigree and the Big Data Challenge Furthermore, to reinforce the vision of MD-Paedigree (and of its future follow-up), it has been deemed strategic to clearly position MD-Paedigree in the Big Data challenge context, and in particular within the Big Data Analytics applied in healthcare. Big data in healthcare is rapidly becoming a very dynamic field, which is gathering great interest from a number of industries, research institutions and public bodies. Starting from this assumption, positioning MD-Paedigree as an early player into this growing field could represent a strategic and long-standing investment, capable to provide the project with great return both for the dissemination of its results both for guarantee a more effective exploitation of the outcomes of the project itself. To reach this strategic goal, a specific effort has been put in place to organise a dedicated session, and to prepare a specific paper, on this issue, at ICT 2013, in Vilnius (see the section 3.1 for more information).

2.4 The target audiences MD-Paedigree’s dissemination strategy will build on the dissemination experiences from previous EC projects (Health-e-Child and Sim-e-Child in particular), and will be further implemented in order to achieve the best dissemination outcomes for different target populations. In fact, as already stated in the DoW, MD-Paedigree’s outcomes are going to exert an impact on a variety of different work and research communities, ranging from clinicians and caregivers to biomedical researchers, as well as European industry, and finally also the general public opinion at large. Therefore the dissemination strategy will be implemented in order to reach in the most effective way each of these communities, adopting from time to time different languages and communication means, tailoring dissemination messages to each audience, and 6ptimize6l specific dissemination activities and channels for the two main audience domains, namely the specialized audience and the general non expert public. The specialized audience For the 6ptimize6l6 audience (researchers, clinicians, etc.), the main means of dissemination will be the publication of the most significative results achieved during the project. A list (not to be considered as exhaustive yet) of relevant journals is provided in Section 4.9. Furthermore, MD-Paedigree’s clinical and technical partners will attend relevant conferences in the field of interest of the project (and of any of it’s sub-domains), attempting to disseminate the work performed in the project and the main results. A preliminary list of events to which the MD-Paedigree consortium is likely to participate is provided in Section 3.4. MD-Paedigree will also organise specific workshops, exhibitions and specific dissemination materials to make the attendance to the external events as effective as possible.



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Moreover, for the 7ptimize7l7 audience, training sessions will be organised (please refer to the specific Section 5 for details), which will be implemented in order to highlight the outcomes achieved both in disease modelling and in the building of the Infostructure, as well as the potential for change management and innovation in clinical workflows to the medical/clinical and research community interested in VPH technology. The general public For the general public, the work will be disseminated in everyday language rather than in an academic or industrial language. One of the main means of dissemination is the project website (www.md-paedigree.eu), which is online from the very beginning of the project, and that will be constantly updated with new materials in the course of the project. The Project Newsletter will be another main tool for communicating in layperson terms the main achievements of the project, providing the main information about the project activities, the attendance to relevant events, and also providing the reader with a focus on a specific part of the project, together with real use-cases to translate the project results within the everyday life experience (i.e. case studies in the clinical practices). The ID Card and the press release has been conceived with the same rationale. Finally, as soon as the project will begin to achieve significant results, the production of the so-called “Dissemination Objects” (Dos) to be made available on the project website and on the project Youtube channel (still to be created) will start. Typically Dos will be 3-5 minute multimedia clips built from consortium presentations, talks given by consortium members and especially recorded video clips to present the highlights of the consortium’s work.

http://www.md-paedigree.eu/



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3. Most significative dissemination activities and achievements in the

first year 3.1 MD-Paedigree awarded as Best Exhibit at ICT ’13

ICT2013 was the 20th and largest event in ICT, hosting over

200 exhibits and 5500 participants from all over the world

and was hosted for the first time in Eastern Europe

(Vilnius, Lithuania 6-8 November 2013). It is one of the

most significant events in Lithuania’s presidency of the

European Union (EU) Council organised by the Ministry of

Transport and Communications and Ministry of Foreign

Affairs together with the European Commission (EC). The

most prominent EU ICT researchers, representatives of

new and experienced companies, strategists in digital

technology, engineers, investors, high-ranking EC and EP officials, ministers and PMs from EU

member countries, students and journalists were present at the event.

At ICT2013, MD-Paedigree hosted a booth giving demonstrations of its innovative technology,

conducted a packed networking session entitled Big data and data analytics impact in

healthcare and distributed a discussion paper on Big Data Healthcare. During the networking

session, MD-Paedigree brought together a wide range of stakeholders to come together and

discuss the challenges faced in improving Europe’s ability to manage and exploit its healthcare data

assets laying the foundation for a Big Data in Healthcare interest group.

On the 8th November 2013 MD-Paedigree was awarded the Best Exhibit Award at ICT2013.

Fig. 2 The MD-Paedigree Team receiving the award

See also: https://ec.europa.eu/digital-agenda/en/news/meet-winners-best-exhibitors-ict-2013

Fig. 1 – ICT 2013 Best Exhibit award

https://ec.europa.eu/digital-agenda/en/news/meet-winners-best-exhibitors-ict-2013



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After the event, a press release has been distributed, and the success at ICT2013 has been reported

also within the MD-Paedigree First Newsletter.

The EC prepared also an interview with the MD-Paedigree Project Manager, Edwin Morley-Fletcher

(who is also in charge of the dissemination activities within the project), in order to explain the

project and its linkage with the Big Data Challenge.

The video of the interview has been released trhough the EC Youtube channel, and also shared via

Twitter by the Official EU-eHealth Account.

Edwin Morley-Fletcher, “Big Data for health” – MD-Paedigree Project

Fig 3. – Screenshot from http://youtu.be/b8qIZS2j2T8

Furthemore, the paper Big data Healthcare, has been distributed by the EC within dedicated

ICT2013 pages of the Digital Agenda Website.

3.2 Attended Conferences In it’s first year of activity, MD-Paedigree took part (with persons from the consortium or with

dissemination materials) in a series of events:

1. The 16th European Health Forum in Gastein, Austria. For this event, MD-Paedigree’s Consortium

has been asked to produce a A0 poster to explain the project.

Fig. 4 – The MD-Paedigree Poster for the EHealth Forum Gastein

http://youtu.be/b8qIZS2j2T8



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2. The 22nd Annual Meeting of the European Society for Movement Analysis in Adults and Children

(ESMAC2013) in Glasgow, Scotland. Different person involved in the project, and more specifically

in the NND study, participated to this meeting. MD-Paedigree has been also represented through a

poster specifically dedicated to the NND part of the project.

3. The Pediatrics 2040 Conference in Anaheim, California: in this big event, hosted by the Childern

Hospital of Orange County (CHOC), MD-Paedigree has been provided with a time slot for a

presentation.

4. Big Data Challenges in the health sector, CTB – Nessi workshop, 11th February 2014, Madrid.

During this workshop, attended also by Mr. Wolfgang Treinen, Policy and Project Officer of the

European Commission (DG CONNECT), there has been a presentation of MD-Paedigree, to which

the representative of IBM has replied suggesting a possible cooperation with their Watson

supercomputer, now being tested at the Sloan Kettering Hospital in New York.

3.3 Press coverage MD-Paedigree appeared on several headlines and articles from national and international

newspapers, websites and blogs. Following are some of the most highlighted articles:

1 – La Repubblica (Newspaper, Italy) – 14-05-2013

“A Roma già si studia il baby cuore dilatato”

Anche l’ Italia è tra i protagonisti della ricerca sul paziente avatar, almeno nella sua versione

“cucciolo d’ uomo”. Sarà infatti l’ Ospedale Pediatrico Bambino Gesù di Roma a coordinare MD-

Paedigree, un progetto di quattro anni finanziato con 15 milioni di euro. È la prima volta al mondo

che il coordinamento di un progetto di Information and Communication Technology (ICT), viene

affidato ad un ospedale pediatrico. «La cosa più difficile per un medico – ammette Giacomo

Pongiglione, direttore del Dipartimento medico chirurgico di cardiologia pediatrica del Bambino

Gesù e coordinatore clinico del p r o g e t t o M D Paedigree – è decidere la cura migliore per il

malat o . F i g u r a r s i quando è un bambino. Al momento, ci basiamo sull’ epidemiologia, cioè le

risposte ai trattamenti su gruppi di persone, più o meno simili al nostro paziente. L’ epidemiologia

peròè una guida statistica, non ci consente di prevedere se nel nostro paziente una certa cura avrà

effetto». Ma si profilano altre strade, quali quella del paziente avatar. «Utilizzando i dati del

paziente – spiega Pongiglione – possiamo personalizzare il modello della sua patologiae quindi

testare le cure sul modello, prima che sul malato vero». L’ ologramma del malato, prende così vita

nel computer. Nel caso del cuore, ad esempio, si può realizzare il modello di un cuore dilatato, di un

cuore che non si contrae, di una valvola che non funziona, di una tetralogia di Fallot. Tutto ciò viene

descritto da un modello matematico, tanto più preciso quante più informazioni si hanno da

immettere. «È il terzo progetto finanziato dalla UE (i due precedenti sono stati Healthe-Child dal

2005 al 2009 che ha permesso di creare il modello del ventricolo destro del cuore, e Sim-e-Child dal

2010 al 2012, che ha realizzato il modello dell’ aorta) a cui abbiamo partecipato su questo

argomento. Questa volta noi siamo i leader del progetto (Bruno Dalla Piccola, direttore scientifico

del Bambino Gesù, è il coordinatore globale di MD-Paedigree), che in questa nuova fase, lavorerà

alla modellizzazione di sei patologie pediatriche: cardiomiopatie, rischio di malattie cardiovascolari

nei bambini obesi, artrite giovanile idiopatica, atrofia muscolare spinale di tipo 3, paralisi cerebrale



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emiplegica, distrofia muscolare di Duchenne. L’ altro obiettivo di MD-Paedigree è la realizzazione di

“infostrutture” (se ne occuperà la HESSO, Haute Ecole Spécialisée de Suisse Occidentale), che

porteranno a realizzare una banca dati mondiale di patologie pediatriche».

Fig 5. Article about MD-Paedigree published on “La Repubblica“

2 – Digital Agenda for Europe (Website, EU) – 27.05.2013

“EU awards 12 million euros to 11ptimize11l11es a healthier future for Europe’s children”

To help fight childhood obesity and other child diseases, the European Commission has awarded 12

million euros to a medical research project that will use mathematical models to improve the

treatment of children.

According to recent data from the World Health Organisation (WHO), the number of overweight

infants and children in Europe has steadily risen from 1990 to 2008. Childhood obesity is strongly

linked to risk factors for cardiovascular disease, type 2 diabetes, orthopaedic problems, mental

disorders, underachievement in school and lower self-esteem. To tackle this problem, the European

Commission has decided to fund the MD-Paedigree project. This project will provide decision

support to medical professionals when treating their young patients in four areas:

cardiomyopathies, obesity-related cardiovascular disease, juvenile idiopathic arthritis and

neurological & neuromuscular diseases. Disease simulation to predict treatment outcome Through

disease simulations, requiring the availability of high performance and supercomputer resources,

MD-Paedigree will improve the diagnostic precision of paediatricians and offer child-specific

treatment choices for Europe’s children. Using MD-Paedigree, doctors will be able to select a highly

11ptimize11l11es11 treatment options and receive on-the-spot support in predicting the likely

outcome of such treatments based on each patient’s personal medical data. This will lead us into a

future where child healthcare will become more effective, more personalised, and even more

affordable. Minimalize animal testing On top of that, there are strong ethical reasons to pursue

this revolutionary research: in the future, new drugs and procedures will be preliminarily tested

using computer simulations. In line with this vision, MD-Paedigree will open up the prospect of

exploring new treatments without or with only minimal animal testing. In the longer run, it is to be

expected that even first pre-clinical and phase one clinical trials may be substantially supported and

accelerated, thereby massively reducing the risk to patients involved in such endeavours. Presently,

http://www.euro.who.int/en/what-we-do/health-topics/noncommunicable-diseases/obesity/facts-and-figures



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very few new drugs are specifically tested for children – and here MD-Paedigree aims to open up a

whole new perspective for paediatric research. MD-Paedigree, which stands for Model-Driven

European Paediatric Digital Repository (www.md-paedigree.eu) is due to last 4 years, finishing in

2017. The project consortium of 21 partners from 10 countries (Belgium, France, Germany, Greece,

Italy, Romania, Switzerland, Netherlands, United Kingdom, USA) is coordinated by the Bambino

Gesù Hospital in Rome and includes five other clinical centres from across the EU (Amsterdam

University Hospital, Catholic University Hospital in Leuven, Giannina Gaslini Institute in Genoa,

Great Ormond Street Hospital in London, Wilhelmina Children’s Hospital in Utrecht). The 6 clinical

centres are scientifically and technologically supported by Siemens and other industrial partners,

research institutions and SMEs. MD-Paedigree follows two previous highly successful projects,

Health-e-Child (www.health-e-child.org) and Sim-e-Child (www.sim-e-child.org) making it possible

to apply innovative diagnostic tools to routine clinical data. It is meant to provide three

fundamental functionalities on top of those of an advanced Electronic Health Records registry:

1. Similarity search, allowing clinicians to access “patients like mine” (and finding decision support

for optimal treatment also based on comparative outcome analysis), and allowing patients to get in

touch with “patients exactly like me” 2. Model-based patient-specific simulation and prediction

3. Patient-specific clinical workflows. Through these tools, MD-Paedigree cognitively updates the

European ideal of granting universal access to clinical and patient enhanced awareness of evidence

based best treatment.

3 – ICT2013 (Website, EU) – 08.11.2013

MD-Paedigree (Model-Driven Paediatric European Digital Repository) wins “ICT2013 Best Exhibit Award” On 8th November 2013 MD-Paedigree (Model-Driven Paediatric European Digital Repository) was awarded the Best Exhibit Award at ICT2013. An event hosting over 200 exhibits and 5500 participants from all over the world (Vilnius, Lithuania 6-8 November 2013). ICT2013 was the 20th and largest event in ICT and was hosted for the first time in Eastern Europe. It is one of the most significant events in Lithuania’s presidency of the European Union (EU) Council organised by the Ministry of Transport and Communications and Ministry of Foreign Affairs together with the European Commission (EC). The most prominent EU ICT researchers, representatives of new and experienced companies, strategists in digital technology, engineers, investors, high-ranking EC and EP officials, ministers and PMs from EU member countries, students and journalists were present at the event. MD-Paedigree is a clinically-led ICT project aiming at developing a model-driven data and workflow-based digital repository, leveraging an information processing and knowledge discovery evolutionary framework, for personalised and predictive treatment in paediatrics. It makes it possible to apply Big Data analytics to routine paediatric clinical data, bringing together all information and knowledge in a private Big Data Cloud which forms the basis of a Smart Analytics service meant to provide three fundamental functionalities: 1) Search for clinicians to access patients like mine and for patients to find patients exactly like me; 2) Model-based patient-specific simulation and prediction; 3) Patient-specific clinical workflows.


http://www.health-e-child.org/

http://www.sim-e-child.org/



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At ICT2013, MD-Paedigree hosted a booth giving demonstrations of its innovative technology, conducted a packed networking session entitled Big data and data analytics impact in healthcare and distributed a discussion paper on Big Data Healthcare. During the networking session, MD-Paedigree brought together a wide range of stakeholders to come together and discuss the challenges faced in improving Europe’s ability to manage and exploit its healthcare data assets laying the foundation for a Big Data in Healthcare interest group. Due to the rapid growth in the availability of data, Big Data technologies define a market projected to grow annually by 40% over the coming years. However, only a small number of European companies appear in the most recent list of largest companies by Big Data revenue.

In modelling environments for predictive, 13ptimize13l13es13 healthcare, personalised solutions are key to improving patient safety and treatment efficacy. The focus of MD-Paedigree is the use of large bases of patient and health system data to drive healthcare innovation. Bringing together stakeholders from Big Data, data analytics and biomedical research, this project gathers emerging needs and service scenarios for storage, sharing, collaboration, multimodal similarity search, outcome analysis, risk stratification, 4P Medicine (Preventive, Personalized, Predictive and Participatory) and patient-specific clinical decision support.

“I am confident that this ground-breaking research MD-Paedigree will open up a new perspective for paediatric research and lead to a future where child healthcare will be more effective, more personalised, and even more affordable.” Concludes Bruno Dallapiccola MD- Paedigree Project Coordinator and Scientific Director of Ospedale Pediatrico Bambin Gesù (Rome, Italy).

4 – Le Monde (Newspaper, France) – 20.01.2014

“Vers un big data européen de la santé”

Plusieures initiatives, soutenues par la commission européenne, s’appuient sur les technologies du

cloud computing et de la big data pour 13ptimi accessibles, compatibles et comparables les données

de différents hôpitaux relatives à des pathologies complexes. Les explications de David Manset,

dirigeant de la société Gnúbila, à l’origine des plateformes dématérialisées.

Le projet Sim-e-Child porte le cloud au service de la pédiatrie. Il s’agit d’un programme lancé par la

Commission européenne en janvier 2010. Ce projet repose sur une plateforme transatlantique,

développée par la société Gnubila, qui relie 5 hôpitaux européens et américains.

Cette plateforme dématérialisée permet aux acteurs de la santé de valider de nouveaux modèles

de simulation sans installer des dispositifs lourds et coûteux pour les hôpitaux. La plateforme cloud

permet notamment de s’affranchir des contraintes 13ptimize13l13es des systèmes d’information et

de 13ptimi les données, concernant des pathologies cardiaques complexes, compatibles et

comparables .

Cette plateforme pourrait à terme devenir un outil prometteur d’aide à la décision. Grâce à elle, les

cardiologues peuvent d’ores et déjà consulter à tout moment une énorme base de données, les

croiser et obtenir des rendus statistiques et autres résultats de fouilles avancées. De quoi

13ptimize13l plus facilement le diagnostic des patients, consulter des cas de 13ptimize13 ou bien

encore identifier des cas similaires par delà les 13ptimize13.



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Le projet Sim-e-child a été développé pour la cardiologie pédiatrique et plus particulièrement pour

les cas de coarctation de l’aorte (rétrécissement 14ptimize14l de l’aorte) chez les jeunes patients.

Toutefois, les chercheurs planchent actuellement sur une déclinaison de ce modèle pour d’autres

pathologies (maladies cardiovasculaires, en particulier liées à l’obésité, maladies rhumatologiques,

maladies neuromusculaires et neurologiques chez l’enfant) à travers le projet MD-Paedigree. Co-

financé par la Commission européenne à hauteur de 11,8 millions d’euros, celui-ci regroupe au

total 22 partenaires, dont 7 hôpitaux en Italie, Belgique,Royaume-Uni,Pays-Bas et Etats-Unis.

Le patient au coeur du système

L’ambition ici est de développer un véritable big data européen pour la santé au service de la

recherche transversale. L’objectif est également de créer un réseau social médical, permettant au

patient de se (ré)identifier pour accéder à ses données et d’en définir les droits

d’accès/exploitation, avec une notion de “don de données” pour la science et de

“péage/rétribution” des patients dans le cadre d’études pharmaceutiques volontaires.

Ce nouveau paradigme de plateforme place le patient au cœur du système et lui permet de

reprendre le contrôle sur ses informations que ce soit en termes de portabilité des données, de

droit à l’oubli ou bien encore de monétisation des accès, adressant une partie des préconisations

de la nouvelle Réglementation Générale de la Protection des Données (GDPR) Européenne, visant à

harmoniser les pratiques au sein de l’Union européenne.

5 – Euronews ‘Futuris’ (website, News – EU) – 23/09/2013

“Visions from the Heart”

In this video dedicated to the EC project Sim-e-Child (which ended on July 2012), MD-Paedigree is

quoted at the end of the video: “Researchers are now adapting the same platform for use with

other paediatric health issues, including rheumatism, neurological and neuromuscular diseases, and

obesity… for the sake of other children all around Europe”.

The video has been published both on the Euronews Website

(http://it.euronews.com/2013/09/23/passi-avanti-nello-studio-delle-patologie-cardiache-infantili/)

and on the Euronews Youtube Channel (https://www.youtube.com/watch?v=4d1nB9ZC910)

6 – SIEMENS GLOBALE NEWS

The Siemens Group

involved in the Project

have prepared a news

article for the internal

SIEMENS Globale News.

Figure 6 – MD-Paedigree

News for SIEMENS GLOBALE

http://it.euronews.com/2013/09/23/passi-avanti-nello-studio-delle-patologie-cardiache-infantili/

https://www.youtube.com/watch?v=4d1nB9ZC910



15

7 – Other press coverage:

Garr News (website, health – Italy): La ricerca medica italiana premiata con il Best Exhibit Award di

ICT 2013

Peds 2040: Choc Children (website, health – eng.): MD-PAEDIGREE: the first EU-funded big data

project in paediatrics” (paper)

Wiki of future (wiki): Model-Driven European Paediatric Digital Repository

Techeconomy (website, technology – Italy): #ICT2013EU: big data, mobilità sostenibile e salute al

centro della manifestazione

Diplo News (website, News – Eng): ICT2013 has been the EU Presidency most visited event in

Lithuania

MedGift- Content Basedmedical image retrieval (Website, health – Switzerland): Projects“MD-

Paedigree

HD TV one (Website, Tv – Italy): ICT 2013: Premiato a Vilnius (Lituania) l’Avatar pediatrico del

Bambino Gesù

HealthManagement (website, health – Eng.): EU Awards 12 million Euros to Supercompute a

Healthier Future for Europe’s Children

Romaonline (website, news – Italy): Scheda di approfondimento Progetto MD-PAEDEGREE

Universo Mamma (website, health – Italy): L’ospedale Bambin Gesù premiato per l’avatar

pediatrico

Superabile (website, health – Italy): ICT, PREMIATO L’OSPEDALE BAMBINO GESÙ PER “L’AVATAR

PEDIATRICO

Insigneo ‘Institute for InSilico Medicine’ (website, health/technology – Eng.): INSIGNEO Research

Projects – Infrastructure & Frameworks

ASCA ‘Agenzia Stampa Quotidiana Nazionale’ (website, news – Italy): Salute: premiato l’avatar

pediatrico dell’ospedale Bambino Gesu’

Haute école spécialisée de Suisse occidentale, (website, press release, Switzerland): Un projet HES-

SO reconnu par l’Europe a Vilnius.

Radio Rottu Oberwallis, (website, press release, Switzerland): Vilnius/Siders: Internationale

Anerkennung für Hes-so.

1985.ch, (website, press release, Switzerland), Wirtschaftsinformatiker der HES-SO Wallis

ausgezeichnet.

VPH Institute, (website), New VPH project kicks off: MD-Paedigree.



16

Furthremore, Cloudsource magazine (SUCRE) have requested two articles from a special edition on

‘Cloud-computing In The Healthcare Industry’ from Athena and Gnubila based on MD-Paedigree.

8 – The Internal Review as means of dissemination

Recently, on 20-22 February, the Internal Review of

MD-Paedigree has been held at the Institute of Child

Health in London, at the presence of independent

reviewers for each disease area addressed by the

project.

Fig. 7 – The UCL Institute of Child Health Entrance

This meeting has represented also an internal

dissemination tools, amongst the partners of the

Consortium itself, and it has been useful in order

to increase awareness about what are the overall

progress of the project and about the main

Fig.8 – One of the MD-Paedigree partner explaining the work perfomed

Furthremore, a press release about the outcomes of the Internal Review has been released through

the project website.

MD-Paedigree has made good progress in this

first year. In any project of such size, complexity,

and encompassing such a diverse range of

activities, there will be all manner of hurdles.

Nevertheless, we have gathered momentum

over year one and we are now truly hitting our

stride in time to tackle the middle straight of the

project and the great challenges it contains. If

we, as a project consortium, maintain the levels

of engagement and motivation shown so far we

will be well set to meet these challenges.

At the core of the project is the data repository and infostructure which is the platform on

which the rest of the project stands. The progress made for the first prototype integration

was able to show the power and potential of the MD-Paedigree vision, enabling us to win

Fig.9 – The Plenary Session of the Internal Review



17

the Best Exhibit Award at ICT’13. Over the next twelve months we will see more clinical

centre nodes, more functionality and more data types come online and so the Infostructure

will really begin to take on its role as the backbone of the project.

The specific activities in the clinical applications were started in close collaboration between

clinical and technical partners. Although some data collection took some time to begin in

earnest, there were subsequent mitigation efforts in finding alternatives, e.g. giving access

to legacy data to allow the technical activities to progress according to plan. Nevertheless,

the risks are understood and particular focus will be dedicated to data acquisition in the

second project year.

Since the clinical involvement implies a number of diagnostic modalities encompassing

different clinical specialities, well-specified practical workflows are crucial for patient

recruitment and effective data collection. Even though DHZB joined the project at a later

point, such workflow preparation was achieved in close alignment with the other centres

and patient groups could successfully be identified. Cardiac MRI protocols have been

adapted at DHZB and lab infrastructure was agreed upon to 17ptimize the pre-analytics for

all clinical sites.

A continued open communication within and between work packages will be a key driver in

leveraging the effect of scale of this large project. In addition to the scientific and clinical

achievements, the enthusiasm and engagement of the consortium members are the best

prerequisite to make this project successful at international level.

As a demonstration of our commitment to transparency between collaborators, we plan to

establish a mechanism for publication that will include the MD-Paedigree consortium in the

author list of every publication. Furthermore, every publication will be sent to the Co-

ordinator for formal approval prior to submission, to ensure appropriateness of authorship

and maintain high publication standards.

As a further extension of our focus on clinical leadership as a driver for healthcare

innovation we will be conducting a data privacy and risk stratification assessment that is

founded upon clinical need and use this to inform our engagement with policy makers. In

this way we hope to drive the privacy agenda so that Big Data can be better but securely

applied for the benefit of the healthcare of European citizens.

The four reviewers:

Fig.10 - Marco Bonvicini Fig. 11 - Rolando Cimaz Fig.12 - Adam Shortland Fig. 13 - Alberto Sanna



18

3.4 Next steps The work performed during the first year will go on on a similar track, attending the various VPH

Community meetings and other relevant events. Furthermore, in an approach which has proved to

be fruitful for Health-e-Child and Sim-e-Child, MD-Paedigree’s conference participation will seek to

be done in conjunction with similar projects, therefore maximizing the opportunities to increase

the frequency of conference attendance with booths or other dissemination means.

As already done during the first year, apart from booth-based conference attendance, MD-

Paedigree will also seek to participate with “light” dissemination materials such as posters and

brochures of the project.

Furthremore, as stated already in the DoW, the MD-Paedigree Consortium will also attempt to

engage Parent and Patient Associations, disseminating news of its work, expected results and

potential future developments through these channels.

Finally, MD-Paedigree will organise a final conference, to be held at the end of the project,

targeting both internal and external clinical and research communities as well as patient

organisations and the interested media.

For the following months, MD-Paedigree’s Consortium is planning to attend a number of relevant

events.

The most important are:

1. The 2nd International Conference on Research Infrastructure (ICRA 2014), which will be held

in Athens on 2-4 April. MD-Paedigree will apply for an exhibit and will attend with some

dissemination materials.

2. The IEEE-EMBS International Conferences on Biomedical and Health Informatics (BHI), which will

be held in Valencia on 1-4 June 2014. For this event, MD-Paedigree already submitted a proposal for

a special session on Big Data in Healthcare, which has been accepted.

3. The European Scientific Society for Clinical Gait and Movement Analysis Conference (ESMAC),

which will be held in Rome on 1-4 October 2014.

Besides these three events, a detailed list of the events which MD-Paedigree’s partners are likely to attend to within 2014 is the following:

Meeting Date Name Location Full Conference Name

March 3-4, 2014 NTRC2014 London - UK 7

th Annual Neuromuscular Translational

Research Conference (UCL)

March 3-6, 2014

HEALTHINF 2014

Eseo, Angers, Loire Valley - France

7th International Conference on Health Informatics

March 5 -8, 2014 RE(ACT) Basel - Switzerland International Congress on Research of



19

Rare Diseases

March 8 - 9, 2014

SMA Den Haag - Netherlands

SMA Europe Annual Meeting

March 19-20, 2014 EDF 2014 Athens –Greece European Data Forum 2014

March 27-28, 2014 IIHC 2014 Berlin - Germany 2014 Innovations & Investments in Healthcare

April 2-4 2014 WoHIT Nice, France World of Health IT 2014

April 1-4 2014 ICRI 2014 Athens –Greece 2nd International Conference on Research Infrastructure

April 9-11, 2014 Med-e-Tel 2014 Luxembourg

The International eHealth, Telemedicine and Health ICT forum for education, networking and business.

April 28 – May 2, 2014 ISBI2014 Beijing - China 2014 IEEE International Symposium on Biomedical Imaging

April 29 – May 1, 2014 Bio-IT 2014 Boston, MA - USA 2014 Bio-IT World Conference

May -- eHealth 2014 Athens - Greece eHealth Forum 2014

May 6-8, 2014 ConhIT 2014 Berlin, Germany Connecting Healthcare IT

May 8 – 10, 2014 ECRD 2014 Berlin, Germany

European Conference on Rare Diseases and Orphan Products

May 10 – 16, 2014 ISMRM Milan - Italy

International Society for Magnetic Resonance in Medicine

May 21-24, 2014 AEPC Helsinki - FInland Association for European Paediatric and Congenital Cardiology

May 28-31, 2014 ECO2014 Sofia - Bulgaria 21

st European Congress on Obesity

June 1-4, 2014 (BHI) Valencia IEEE-EMBS International Conferences on Biomedical and Health Informatics (BHI)

June 7-10, 2014 PICS & AICS 2014 Chicago, IL - 2014 Pediatric & Adult Interventional Cardiac Symposium

June 17-18, 2014 GHIT 2014 Washington, DC - USA

Government Health IT Conference 2014

22-25 June, 2014 ISMRM Workshop

Charleston, SC - USA

ISMRM Workshop on Functional MRI: Emerging Techniques & New Interpretations

July 3-5, 2014 EACD 2014 Vienna - Austria 26th Annual Meeting of the European Academy of Childhood Disability

July 11 -14, 2014 ISMRM Workshop Tromsø - Norway ISMRM Workshop on Motion Correction in MRI

September 9-12 VPH 2014 Trondheim, Norway Virtual Physiological Human 2014

http://iihc.eu/agenda/

http://iihc.eu/agenda/

http://www.ismrm.org/workshops/Motion14/

http://www.ismrm.org/workshops/Motion14/



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Sep 14 -18, 2014 MICCAI 2014 Boston (MIT), MA - USA

Medical Image Computing and Computer Assisted Intervention

October 1–3, 2014 EHFG Gastein - Austria 17th European Health Forum Gastein

October 1-4, 2014 ESMAC 2014 Rome, Italy European Scientific Society for Clinical Gait and Movement Analysis –

October 27-30, 2014 ICIP 2014 Paris - France 2014 IEEE International Conference on Image Processing

November 30, 2014 RSNA 2014 Chicago, IL – USA Radiological Society of North America



21

4. Preliminary dissemination materials and channels During the first year, a number of dissemination materials have been produced. Since the Project is

at its very beginning, the materials produced are rather generic.

First of all, we have produced the branding of the project (logo, template, graphic), then the public

website, the ID Card and the Newsletter. Furthermore, a number of posters focused on each

disease area of the project have been produced. Finally, a first channel of communication have

been opened, besides the website, namely the MD-Paedigree Twitter Account.

The MD-Paedigree Consortium expects to multiply the dissemination materials (both specific and

generic) and channels during its course, as the number of significant achievements starts to

increase.

In the following sections, details are provided for each of the dissemination material produced so

far.

4.1 Md-Paedigree Logo The MD-Paedigree logo was presented to and agreed by the project partners at the project kick-off

meeting.

4.2 MD-Paedigree in One Slide A poster summarising the MD-Paedigree effort has been produced and is shown in the following

figure:

Fig.14 – The MD-Paedigree Logo

Fig.15 – The MD-Paedigree infographic



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4.3 The MD-Psaedigree Kick-Off Meting Poster and Biannual Meeting Poster

Fig.16 – Kick-Off Meeting poster

Fig.17 – Biannual Meeting poster



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4.3 Newsletter

4.4 Project website

Public website

The first Newsletter has been produced after 6

months of activity of the project. The newsletter

was divided in various sections: an editorial

interview with Project Coordinator Bruno

Dallapiccola, a Project Overview, Highlight &

Objectives, and a section dedicated to the

Clinical applications of MDP for the various

areas (Cardiomyopathies, Gabriele Rinelli;

Obesity Related Cardiovascular disease, Andrew

Taylor; Juvenile Idiopathic Arthiritis with Alberto

Martini; and Neurological and Neuromuscular

Disease with Jaap Harlaar). Another section was

dedicated to the Infostructure Challenges. Latest

News of the project’s achievements were

presented and in the last section ‘Special

Feature’ a position paper on ‘Big Data

Healthcare’ (an overview of the challenges in

data intensive healthcare) by Edwin Morley-

Fletcher has been included.

As foreseen in the DoW, the newsletter has been

published into the Project Website and

distributed in other channels on the web. Fig.18 – The MD-Paedigree Newsletter Frontpage

Fig.19 – The MD-Paedigree Website Homepage



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The MD-Paedigree community is supported by a dedicated website, linked to all partners’ websites, and to all the other VPH and infrastructure projects’ websites of interest for MD-Paedigree. The website has been designed to raise awareness and to promote contents, discussion and suggestions. The project’s website provides access to:

A public area, providing promotional information on public results as well as news, public deliverables, articles and material from participation at events;

A link to EMDesk collaboration platform (see below);

A link to the MD-Paedigree Infostructure.

A link to the MD-Paedigree Twitter Feed.

A link to the EC official RSS Feed. Updates to the website are reviewed on a regular basis, with new material being posted as they become available.

Private website

The MD-Paedigree Consortium adopted the EM-DESK tools: EMDESK is a web-based collaboration and project management platform developed especially for European research projects. An integrated and secure tool, EMDESK supports the entire project life-cycle.

4.5 Social Networks Since it’s inception, MD-Paedigree has been active on a number of social network platforms, from

Twitter, to Youtube, to LinkedIn.

Fig.20 – The MD-Paedigree Twitter Account



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4.6 Project Posters Posters have been produced and displayed at events where MD-Paedigree participated

Fig.21 – The MD-Paedigree posters (one per disease area)



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4.7 ID Card An ID Card of the project has been produced, as shown in the following figure:

4.8 Project’s “Business Card” The MD-Paedigree Business Card has been conceived to be a very light and general dissemination

means, for a wide and massive distribution during the events. It contains only the main information

about the project: Logo, website and Twitter account.

Fig.22 – The MD-Paedigree ID Card

Fig.23 – The MD-Paedigree “Business card”



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4.9 Publications

Publications up to February 2014

1. E. Morley-Fletcher, Big Data healthcare, paper attached to the article Big data: What is it and why is it important?, published in the EC Digital Agenda Website (http://ec.europa.eu/digital-agenda/en/news/big-data-what-it-and-why-it-important). 2. E. Morley-Fletcher & B. Bressan, Innovation and Big Data, concluding chapter included in the forthcoming Wiley book for the 60th anniversary of CERN, From Physics to daily life: Applications in Biology, Medicine and Healthcare: How the technology and knowledge transfers from fundamental research changed industrial process, human behaviour and society at large, edited by Beatrice Bressan.

Both these publications are attached in the Appendix.

Scientific journal and magazines for next publications

The following scientific journals have been identified the most appropriate places in which to

publish the work performed within MD-Paedigree:

Computer Magazine, IEEE Press Information Processing in Medical Imaging (IPMI), Springer International Journal of Multiscale Computational Engineering, Begell House Publications The Fraunhofer IESE Series on Software and Systems Engineering, Fraunhofer Medical Image Analysis, Miccai Society - Elsevier IEEE Transactions on Biomedical Engineering, EMB | PubMed IEEE Transactions on Medical Imaging, IEEE Publications Biomechanics and Modeling in Mechanobiology, Springer American Journal of Physiology, APS Publications Annals of Biomedical Engineering, Springer Journal of Applied Mechanics, ASME Journals Interface focus - Royal Society Publications Journal of Cardiovascular Magnetic Resonance, SCMR Publications Circulation, American Heart Association Publications European journal of medical research, BioMed Publications Journal of Magnetic Resonance Imaging (ISMRM), Wiley-Blackwell Journal of Medical Imaging, SPIE Journal of the American Medical Informatics Association (JAMIA), BMJ Publishing Group Studies in Health Technologies and Informatics, IOS Press Rheumatology, Oxford Journals Journal of Biomechanics, Elsevier Arthritis & Rheumatology, Wiley-Blackwell Gait & Posture, Elsevier Information Processing & Management, Elsevier Paediatric Critical Care Medicine, Lippinkott | Williams & Wilkins

http://ec.europa.eu/digital-agenda/en/news/big-data-what-it-and-why-it-important




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IEEE Journal of Biomedical and Health Informatics (Retitled from IEEE Transactions of Information Technology on Biomedicine – T-ITB), IEEE

Clinical Biomechanics, Elsevier Medical & Biological Engineering & Computing, Springer Developmental Medicine & Child Neurology, Wiley | Blackwell Nature Genetics, nature publishing group (ngp) Paediatric Radiology, Springer Methods of information in Medicine, Schattauer Nature Review: Neurology, Nature Publishing Group (ngp) Transactions on Database Systems (TODS), ACM Very Large Databases (VLDB) Journal, Springer Eurointervention, EuroPCR | EAPCI, European Society of Cardiology American Journal of Hematology, Wiley | Blackwell Diagnostic Microbiology and Infectious Disease, Elsevier Transactions on Case‐Based Reasoning, IBAI Publishings Proteomics – Clinical Applications, Wiley-Blackwell



29

5. Training Strategy

The training strategy of MD Paedigree is embedded in a large dissemination strategy that considers not only scientific dissemination but clear reach towards its target audience in terms of concrete outputs of the project (i.e, the MD Paedigree platform). This is also a very important part of the MD Paedigree sustainability strategy.

5.1 Introduction The training activities correspond to task 18.3 of the workplan. It was mentioned in the DoW that anecdotal evidence has confirmed (via WP4 of the VPH NoE and via feedback from the DISCIPULUS (‘Roadmap Towards the Digital Patient’) meeting (30/03/2012; Barcelona)) that training is recognized to be one of the most solid and long-lasting dissemination strategies in place. This was also highlighted in the paper “Virtual Physiological Human: Training Challenges1 The training activities within MD Paedigree will consist of 2 ‘hands-on’ workshops to be delivered during years 2 and 4 of the project (at approx. 1 or 1.5 year interval) in order to expose the outcomes achieved both, in disease modelling and in building the infostructure, highlighting the potential for change management and innovation in clinical workflows to the medical/clinical and research community interested in VPH technology. The first workshop will also seek to provide feedback to the research and development activities, so as to refine the outcomes for the final workshop. The workshop participants will fill in a detailed feedback questionnaire that will be passed to the developers. The level of detail in this training strategy is intended to give sufficient guidance as to what the training will entail, what is the target audience, the format that will be used and the expected (and desired) outcomes.

5.2 Training Event (1) in a nutshell: Training clinicians to upload and use the MD-Paedigree’s Repository Format The format of the training is based on a ‘hands-on’ workshop in which participants (mainly clinicians) will learn how to use the MD Paedigree platform and, if they so desire, they will bring a clinical case of their own (prior notification to the MD Paedigree team). Although the first training event will be on a “first-come, first-served” basis, the MD Paedigree Consortium will also identify key clinicians to participate. Key technical participants:

1 facilitator from a clinical background involved in the development of the platform 1 facilitator from a technical background involved in the development of the platform An initial group of around 20 individuals split into 4 sessions (1 in the morning, one in the

afternoon, 2 days in total), where each per application will be shown individually (risk of obesity in children, cardiomyophaties, juvenile idiophatic arthritis, neuromuscular and neurological disorders)

Typical example of 1 session (4 hours): Introduction of the tool (1 hour)

Test case studies (including cases brought by participants*)

Exploration of the tool by individual participants (1.5 hours)

1 http://www.ncbi.nlm.nih.gov/pubmed/20478909



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Real case presentation of use in a hospital setting – discussion about implementations and obstacles (45 minutes)

Feedback and discussion (45 minutes)

The organisation of a Training session is complex and time consuming. If the training is to be a success, team effort is needed. The following plan is a suggestion based on the experience and lessons learned in the organisation of different training events in the past. Although there are 2 training events planned, a general plan for all the training will be provided below. However for the purpose of this deliverable, we will use the Training Event (1) as an exemplar. This is because Training event 2 is (a) still too far away in the future and (b) its concrete implementation will depend on the success and lessons learned in Training Event 1. The summary of training event 1 is shown in Fig. 1.

Fig. 24 - Training event 1 in a nutshell (location still not confirmed)

5.2 The organisation of both Training Events can be broken down into 6 General Tasks Task I: Set up, including building the organising team, elaborating a plan for the training organisation, and budget considerations. Task II: Handling logistics for completion before the announcement of the training and before the application period starts. Task III: Tasks to be performed during the dissemination and application period, related mainly with the preparation of the materials before the actual training takes place. Task IV: Invitations and course preparation tasks.



31

Task V: Tasks during the training event. Task VI: Tasks to be carried out after the training event. These tasks do not necessarily occur in chronological order and are dependent on one another. The estimated duration of each phase and overview plan are presented in section 2.3.

Task I: Set up An organising team is made up of the following roles:

Training organiser: The person/entity who has ultimate responsibility for the training and the organising team and may make decisions on organisation of the training.

Training secretariat: a person who will prepare the materials and coordinate its distribution (together with the WP leader in order to ensure a consistent alignment with the general dissemination strategy). The secretariat is also the main contact point for all the organising team members and for the participants.

Tutors: 2 people with advanced or senior level scientific profile in related fields of the selected case

studies, each with the responsibility to guide their team’s efforts during course preparation (before the training week starts) and over the training event.

Local clinicians to demonstrate a real use of the platform

The only role not requiring scientific knowledge over the training topics is the training secretariat. For the other roles people of advanced/senior scientific knowledge and expertise should be assigned. The same people may take on many roles, reducing this way the number of staff involved in the training organisation and execution.

Task II: Logistic tasks The first training event will take place on the occasion of one of MD-Paedigree’s periodic meetings. The preparation of the dissemination material is better to be started at the same time as the logistics arrangements, so that the application period can be opened as soon as necessary confirmations are done. It is often the case that the development of dedicated web pages and online applications, and the design of the dissemination material takes longer than expected.

One meeting room with reliable and fast internet connection, ethernet cables (as back-up), sufficient power supply, projector, and optionally also a printer.

At least one extra working station with standard Office tools (Word, PowerPoint etc.) MD Paedigree platform available and set up

The Participant Dossier will include information about the available resources provided by the organiser.

Task III: Dissemination and application period The preparation of materials consists of developing the material necessary to disseminate the training information, and the application form. If everything goes smoothly, the development of the dissemination material should not take more than two months. Spending some time in the beginning of the planning in thinking about the visual image of the Training Event is a good idea, since that will help with the



32

preparation of all the relevant material and gives a uniform look to it. After the material is ready and the application period starts, the workload reduces significantly, and increases again after the deadline for applications has closed. Dissemination materials

Announcement of the training: The training event announcement should be supported by a website (or a dedicated section of a website) and electronic material. Print material is not necessary, but can be of use for example for dissemination at scientific conferences or meetings.

Web: Introduction of the Training Event: The complete, up-to-date information about the training event and key participants (if any) and application process should be available online. For this purpose, building a section on the MD Paedigree website is needed. The dissemination can be reinforced by the use of banners, and dissemination through relevant mailing lists and social media such as twitter, facebook, and LinkedIn (this task will be handled by the Project dissemination team). The information in this summary should include:

o A short description of the training event i.e. what is it? o The objectives of the training o Definition of the target; to whom the training is aimed at and who can apply (are there any

restrictions to apply) o Information about the important dates and deadlines o Instructions on how to apply

Application Application form: This will be done using Event Brite on any other online tool. Application period: 2 months.

Task IV: Confirmation and course preparation A quick check on the credentials of the applicants will be performed in order to ensure consistency and maximum impact. This will be done within 5 working days of registration. Participant Dossier Each valid participation request will receive the Participant Dossier which is a document including all the information about the training, and detailed instructions to the participants regarding the next steps, i.e. how to confirm the assistance to the training. Updates of the Participant Dossier can be re-sent to the participants during the preparation phase, thus the last version of the Dossier should include complete information about the training in one single document. Sent along with the confirmation about acceptance to the course, the first version of the Participant Dossier should include at least the following information:

What is the Training event, and how does it work Calendar for important dates and deadlines Description of the venue(s) Tentative schedule Information about the resources provided by the organiser Information about the tutors, and their contact details Required actions from the participants (see next item for ‘Case Study Preparations’) Information about the expenses (what is covered by the organisation and what is not)

And in the final version, in addition to the previous information:

Final schedule Information on logistics:

- Contact details (secretariat) - Addresses and instructions on how to arrive



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Case study preparations Each applicant will have the possibility to bring a case study to the training event. Prior to the event, (at least 3 weeks prior) the participant will contact the MD-Paedigree organisers about his/her case in order to get it ready for the training session. The organisers will put them in touch with the tutors who will handle their sessions. Failure to comply to this rule will mean that the case will not be considered. The tutors’ role is to make sure all the session participants understand the platform, are comfortable sharing their case studies (if any), access to necessary material and data is assured for the training and the course preparation is taken seriously by all. It is important that the tutor takes on an active role in leading the team. Also, the team member whose case study is selected has an important role in providing relevant readings and further information about the case study to the rest of the team, and the tutors should work intensively to involve and engage all participants at this point.

Task V: During the training For tutors and experts it is good to realize that many participants will have only little experience in working with such technologies and/or within a multi-disciplinary team. At the start of the session, the tutors give an introductory presentation on the platform. The final outcome of the training is to provide a clear idea of what the MD-Paedigree platform provides, to entice future participation, generate discussion and collect feedback.

Task VI: After the training Wrapping up the Training event will take between 4 and 5 weeks. The immediate tasks are related with dissemination, such as publishing a press release and updating the web with information about the success of the training event. A satisfaction survey is handed out to the participants, either in paper during the last day of the training, or in electronic format by email shortly after the training week. The organisers will prepare a report (which is a formal deliverable of the project), including a brief overview of the results obtained. The preparation of this report included input from the team as well, and possibly some observations from the tutor. Experience with this has shown that it takes quite some time to get these reports ready and continue to have the involvement of the teams in delivering the requested information.

5.3 Supporting materials A number of supporting materials will have to be developed before the training event. They consist of the material used for dissemination, material related with the application process, material providing information to the participants, and documents used for the evaluation of the training. The Supporting Documents are:

Web/Print: Leaflet, which will have to be produced prior to the meeting in conjunction with the WP leader. It will help disseminate the events via a section on the website and printed material.

Application form: A simplified online application form will be produced. This form will require the specification as to whether or not the participant would like to bring his/her own material to test the MD-Paedigree Platform. In case of a positive answer, the workshop organiser will contact the applicant and will put the applicant in contact with the technical team so that this data is ready to be tested.



34

Participant Dossier: Agenda, background about the project, key contacts, information on test cases and why they were chosen.

Certificate of assistance

Satisfaction survey: the objective of the satisfaction survey is to provide feedback about the event and how could be made better (for planning of the 2nd event)

Brief Overview of the work plan The estimated duration and dependencies of the main tasks of the organisation are shown in the work plan below. The estimation is based on the assumption that the organisation is done as a part time activity of the organising team. In any case, the preparation of the training is preferably started at least 6 months before the training commences, since the application period is only expected to last about 2 months.

Month 1 Month 2 Month 3 Month 4 Month 5 Month 6 Prepare dissemination material

Admin: Booking space – flexible as it will depend on demand

Dissemination

Application open (1)

Identification of experts and invitations

(2) Space confirmation

Data processing if applicants send their own (3)

Training

Analysis feedback

(1) checking interest and re-arranging sessions if necessary. Send material to participants. (2) are experts here? do we need to identify additional experts



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Appendix 1. Big Data healthcare, paper attached to the article Big data: What is it and why is it important?, published in the EC Digital Agenda Website (http://ec.europa.eu/digital-agenda/en/news/big-data-what-it-and-why-it-important). 2. Innovation and Big Data, concluding chapter included in the forthcoming Wiley book for the 60th anniversary of CERN, From Physics to daily life: Applications in Biology, Medicine and Healthcare: How the technology and knowledge transfers from fundamental research changed industrial process, human behaviour and society at large, edited by Beatrice Bressan.



1

Please address all proposals of edits and comments to the current draft to [email protected]

Big Data Healthcare

An overview of the challenges in data intensive healthcare1

Edwin Morley-Fletcher In order to bring about the revolution in healthcare that mod-

ern IT promises, there are legal, technical and cultural/societal

barriers that must be overcome. In this draft paper we deal

with the concept of Big Data as applied to healthcare, or Big

Data Healthcare, and the developments it may bring. We then

consider some of the current major hurdles to its acceptance in

standard healthcare.

1. Big Data Healthcare

Big Data Healthcare is the drive to capitalise on growing patient

and health system data availability to generate healthcare inno-

vation. By making smart use of the ever-increasing amount of

data available, we can find new insights by re-examining the

data or combining it with other information. In healthcare this

means not just mining patient records, medical images, bi-

obanks, test results , etc., for insights, diagnoses and decision

support advice, but also continuous analysis of the data streams

produced for and by every patient in a hospital, a doctor’s

office, at home and even while on the move via mobile devices.

Current medical hardware, monitoring everything from vital

signs to blood chemistry, is beginning to be networked and con-

nected to electronic patient records, personal health records,

and other healthcare systems.

1 This document constitutes a preparatory draft for the Networking Session on “ Big data and data analytics impact in healthcare” organised by the FP7 integrated project MD-Paedigree, partially funded by the European Com-mission, for November 7th, 2013, as part of the ICT’13 conference in Vilnius. Its author is Edwin Morley-Fletcher, who, though fully assuming all responsibili-

ties for the current draft, acknowledges his intellectual debts to the fruitful

discussions he had with various members of the Virtual Physiological Human

Institute Public Affairs Working Group (among whom, particularly, Martina

Contin, Ann Dalton, Liesbet Geris, Adriano Henney, James Kennedy and Marco

Viceconti), as well as with the MD-Paedigree community (among whom, partic-

ularly, Bruno Dallapiccola, Ludovica Durst, Harry Dimitropoulos, Yannis Ioan-

nidis, Alex Jones, Titus Kuehne, Callum MacGregor, David Manset, Omiros Met-

axas, Henning Mueller, Giacomo Pongiglione, Patrich Ruch, Alberto Sanna,

Constantin Suciu, Michael Suehling, Karl Stroetman, and Frans Van der Helm),

the leaders of the p-medicine and the VPH-Share projects (Norbert Graf and

Rod Hose), and scholars met on recent occasions, such as the Teradata Big

Analytics conference in London, 20 September 2013 (Yasmeen Ahmad, Mike

Gualtieri), the Pediatrics 2040 conference in Anaheim, CA, 3-5 October 2013

(particularly Gregory Auner, Anthony Chang, Ho Chih-Ming, Wendy Swanson, ,

Han Wang, Randall Wetzel), and the IEEE Big Data in Santa Clara, CA, 6-9 Octo-

ber 2013 (particularly Ingemar Cox, Benedikt Elser, Mike Franklin Joseph Gonza-

lez, Irwin King, Pantelis Koutroumpis, Natasa Milic-Frayling).

The resulting data stream is monitored by healthcare profes-

sionals and healthcare software systems. This allows the

former to care for more patients, or to intervene and guide

patients earlier before an exacerbation of their (chronic)

diseases. At the same time data are provided for bio-medical

and clinical researchers to mine for patterns and correla-

tions, triggering a process of “data-intensive scientific dis-

covery”, building on the traditional uses of empirical descrip-

tion, theoretical computer-based models and simulations of

complex phenomena.

Big Data has been characterised as raising five essentially

independent challenges:

volume,

velocity,

variety,

veracity (lack thereof),

value (hard to extract).

As elsewhere, in Big Data Healthcare the data volume is in-

creasing and so is data velocity as continuous monitoring

technology becomes ever cheaper. With so many types of

tests, and the existing wide range of medical hardware and

personalised monitoring devices healthcare data could not

be more varied, yet data from this variety of sources must

be combined for processing to reap the expected rewards.

In healthcare, veracity of data is of paramount importance,

requiring careful data curation and standardisation efforts

but at the same time seeming to be in opposition to the en-

forcement of privacy rights2.

Finally, extracting value out of big healthcare data for all its

beneficiaries (clinicians, clinical researchers, pharmaceutical

companies, healthcare policy-makers, etc.) demands signifi-

cant innovations in data discovery, transparency and open-

ness, explanation and provenance, summarisation and visua-

lisation, and will constitute a major step towards the cove-

ted democratisation of data analytics.

2 The more we remove information from patient data that can be

used to identify patients, the lower the data’s veracity and thus clinical

value. A patient’s name may not be clinically significant, but age, gender,

blood type and clinical event timings can be used to help identify patients

while also having obvious clinical relevance.

mailto:[email protected]

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2. Healthcare Data Access and Protection

Three political, academic and business discussions are currently

at the core of the EU debate in this area - the need to ensure

that EU citizens’ data are adequately protected, the need for

open access to data for research purposes, and the need for

Europe to develop a vibrant Big Data industry, capable of inve-

sting a growing amount of resources into break-through innova-

tions in healthcare that the appropriate utilisation of Big Data

promises to deliver. The combined impact of the effective use

of Big Data in research and clinical applications has the poten-

tial to significantly enhance the health and wellbeing of the EU

citizens.

These three debates are running largely in parallel with the fol-

lowing:

1) the loss of trust in data privacy promises, caused by recent

disclosures highlighting massive intrusions by government

agencies and globally acting commercial companies, as well as

inappropriate data sharing by social media organisations;

2) the continuous growth of healthcare data, which awaits tran-

sformation into bio-medical knowledge by adequate data crun-

ching and data analytics applications that will bring about inno-

vations and improvements in healthcare;

3) the open access debate, being driven by the ever increasing

size of scientific literature and the rising cost of access to jour-

nal articles, which is further complicated by recent discussions

of treating datasets as first-class objects in scholarly communi-

cation and the research life-cycle, thereby bringing them into

the open access sphere as well.

2.a. An ownership issue and the European gift relationship tra-

dition

A further topic, often overlooked in reference to Big Data, is the

rights of patients to their data or “the data subject's legitimate

interests”. There are currently instances where others use the-

se data in a supposedly anonymised form, usually without the

patients’ knowledge or consent, to earn profits without letting

patients participate in the wealth generated from adding their

respective data to large repositories.

For example, in the pharmaceuticals industry there is already a

growing market for data on individual prescriptions. Regulation

aimed at letting patients participate in some way in the ad-

vantages derivable from their data is a complex topic in need of

extended public debate leading eventually to policy attention.

Richard Titmuss’ 1970 seminal study on The Gift Relationship:

From Human Blood to Social Policy, has long been an implicit

reference standard due to its concern regarding “processes,

institutions and structures which encourage or discourage the

intensity and extensiveness of anonymous helpfulness in socie-

ty” and its warning that “patients can be harmed, physically and

psychologically, by giving themselves, willingly or unwillingly,

knowingly or unknowingly, as teaching material” to scientific

research and the medical profession. International agree-

ments, such as the 1997 European Convention on Human

Rights and Biomedicine (and its 2002 additional protocol),

have stated that “the human body and its parts shall not, as

such, give rise to financial gain or comparable advantage”.

Also the UK House of Lords Science and Technology Com-

mittee’s 2001 report on human genetic databases concluded

that: “we do not regard ownership of biological samples as a

particularly useful concept *…+ we prefer the notion of part-

nership between participants and researchers for medical

advance and the benefit of others”, while the 2011 report

on Human Bodies: Donation for Medicine and Research, is-

sued by the Nuffield Council on Bioethics, is not opposed, in

principle, to money payments for people donating human

bodily materials but insists on a subtle distinction between

paying for the material purchase of a thing and paying for

the people for their donation, rejecting the former.

Another point, raised by Anne Philips’ Whose Bodies, Whose

property? (2013), is the advocacy for introducing “a levy on

the proceeds of research to be returned, either as assistance

to the specific group suffering from the disease, or to the

wider community”.

How these complex issues are reflected in the Big Data de-

bate is still entirely to be ascertained, even though it is clear-

ly of paramount importance for the development of bio-

medical research.

2.b. The goal of a European Research Area and the Data Pro-

tection Regulation

Access to data is recognised by the European Commission as

key to the completion of the European Research Area (ERA)

and to ensuring a “single-market of data”. The Commission

Communication on “A reinforced European Research Area

Partnership for Excellence and Growth” establishes the fun-

damental priorities for completing ERA. Amongst them, it

includes the optimal Union-wide circulation, access and

transfer of scientific knowledge, as also imbedded in data, in

order to ensure optimal transnational co-operation in the

frame of building ERA. In order to foster Europe’s research

and innovative potential, Member States shall implement

the Commission’s recommendation of improving research

teams’ access, inter alia, to medical record data.

The Conclusions issued by the European Council on 24/25

October 2013, however, have been rather generic, stating

that “Europe must boost digital, data-driven innovation

across all sectors of the economy”, that “strategic technolo-

gies such as Big Data and Cloud computing are important

enablers for productivity and better services”, that “EU ac-

tion should provide the right framework conditions for a


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aingle market for Big Data and Cloud Computing”, that “the

European Council calls for the establishment of a strong net-

work of national digital coordinators which could play a strate-

gic role in Cloud, Big Data and Open Data development”, and

that “the commitment to complete the Digital Single Market by

2015 has to be delivered on”.

A precondition for all this to become a reality, and for Europe to

assert a dynamic and independent role in this crucial area, is

the speedy adoption of the Data Protection Regulation, which is

still under discussion. Two basic principles in the current text

are the “right to be forgotten” (when a data subject no longer

wants its data to be processed and when there are no legitima-

te grounds for retaining it, the data must be deleted) and

“informed consent” (consent should be given explicitly by any

appropriate method enabling a freely given informed indication

of the data subject's wishes).

2.c. The right to be forgotten

The right of data subjects “to be forgotten” implies, de facto,

some sort of ownership of data relating to them, and therefore

the right to eventually donate or sell them. It is true, however,

that when it comes to data relating to individual subjects, the

preferred model for the advancement of scientific research

seems to have been not to allow intellectual property (IP) rights

deriving from raw datasets, while IP rights should only become

attached to the analytic work performed on the data, in the

same way as current IP law covers arrangement of facts, but

not the facts themselves.

Therefore, a distinction needs to be made between data and

the products and services developed using these data. For a

market in data analysis to be feasible, if ownership of such

products and services should rest with the developer, the basic

ownership of the data on which it was based should rest with

the data subject, but no direct IP rights should be derived from

the raw data as such.

2.d. Data donation and data inheritance

What remains unclear is how the “right to be forgotten” affects

these products and services. Suppose there is some patient

data based product (e.g. a disease model that has been devel-

oped by analysing and in some novel way aggregating a da-

taset). If some data subjects whose data makes up part of the

underlying dataset request the deletion of their data, must the

effects of their data be removed from the developed product?

If the product has been sold or licensed, or if the data was in

fact totally anonymised, which in itself is difficult to prove, this

may no longer even be possible.

The demands for informed consent and de-identification imply

the implementation of appropriate counteracting measures to

prevent deductive disclosure, i.e. the ability to re-identify

data based on some inferences either by aggregating more

data or by querying the available dataset.

A simple and effective approach consists of setting a minimal

threshold on query engines so that queries returning less

than a minimum number of cases do not inform the user on

the real count (K-anonymity). Another approach is the injec-

tion of noise into datasets for prevention of re-identification.

Nevertheless, with both of these approaches, there is clearly

a balance to be struck between protecting privacy and main-

taining the utility of the datasets. As more and more diverse

person-related datasets become available, data that were

once regarded as 99.99% anonymised may no longer qualify

for this score.

From an ethical perspective, data subjects should be ade-

quately informed of the current and future health care ad-

vantages that they may derive by making their data available

for biomedical exploitation. Yet, how can a data subject

grant consent for as yet unknown purposes? There is a need,

in this sense, for what could be defined as “enhanced priva-

cy” or “enhanced consent”, based on spreading the aware-

ness of the personal and social significance of anonymised

individual patient and personal data for preventative and

predictive purposes in healthcare, and for promoting “data

donation” mechanisms. This could be combined with “data

inheritance” mechanisms, which are automatically applied

after a certain period has elapsed from the data subject’s

time of death, unless they have explicitly opted-out prior to

death. Certainly, a different treatment should be considered

for the parts of these data whose the public availability

could have detrimental consequences for the relatives of the

deceased, such as genetic information.

2.e. Anonymisation and enhanced privacy

“Enhanced privacy/enhanced consent” could also focus on

the subject being able to define restrictions under which the

consent becomes void (e.g., when the study involves the

development of armaments) and should be coupled with the

concept of “personal data portability”: an individual should

be able to export or delete his or her data from the system

at the end of a relationship with a particular service provider

or researcher.

In an age of intense and pervasive innovation, the speed of

technological development is likely to outpace the legislative

process, so there is a need to constantly update legal fra-

meworks to implement forward-looking policies and laws,

allowing for flexible regulations to keep step with technolo-

gy.

It may be that the more pragmatic approach is to legislate

against misuse of data as opposed to prescribing allowable


4


uses. Article 81 of the Data Protection Regulation states that

Union law or Member State law dealing with processing of per-

sonal data concerning health shall provide for suitable and spe-

cific measures to safeguard the data subject's interests and

fundamental rights versus the necessity for the purposes of

preventive or occupational medicine, medical diagnosis, the

provision of care or treatment or the management of

healthcare services, and where those data are processed by a

health professional subject to the obligation of professional

secrecy, or another person also subject to an equivalent obliga-

tion of confidentiality.

Article 83 states that within the limits of the Regulation, per-

sonal data may be processed for historical, statistical or scien-

tific research purposes only if the following hold:

(a) these purposes cannot be otherwise fulfilled by processing

data that do not permit or no longer permit the identification of

the data subjects;

(b) data enabling attribution of information to an identified or

identifiable data subject are kept separately from all other in-

formation under the highest technical standards, and all neces-

sary measures are taken to prevent unwarranted re-

identification of the data subjects.

This highlights the crucial importance and desirability of de-

identification of data, but recognizes that it is not always possi-

ble to carry out the necessary research if the data are fully de-

identified3.

It must also be noted that full anonymisation is in principle im-

possible, depending on the data recorded; e.g. both DNA

(fingerprinting) and images (e.g. 3D) do always theoretically

allow the identification of a specific patient.

To address this, wherever possible, data "itemising" must be

considered so that there is not a complete image/genotype

record, and therefore identification of the individual is more

difficult, although in principle not impossible.

The debate on data protection and open access should

come to an ethically-based consensus agreement, allowing

for the views of minorities to be respected, if the right of

citizens to appropriate data protection is to be adequately

balanced against their right to further improved healthcare

based on patient data-facilitated clinical research. This ba-

lance is crucial if legislators wish to avoid overprotection of

the rights of a minority becoming detrimental to the delivery

of effective healthcare for the majority.

To this end, it must be ensured that it is not just the nature

of the data that influences the level of protection afforded,

but also the intended use of those data, and the potential

risks implied by their usage. EU citizens have shown time

and time again in surveys that their concerns over data secu-

rity relate not to the use of their data per se, but to who will

use it, and to who might have access to it in the future.

In a UK survey conducted in 2009, it was revealed that an

overwhelming number of UK citizens were willing to donate

their health data where such data would be used solely for

the advancement of medical knowledge. Concerns over the

use of data were confined to fears that their data would be

leaked to their employer or insurance company. The nuan-

ces of EU citizens’ concerns shows that there is a need to

ensure that in crafting the solution to the data protection vs.

open access and Big Data analytics dichotomies, the specifi-

cities pertaining to each of these issues are appropriately

identified.

Denying health researchers access to data necessary for

potentially life-saving research, the results of which EU citi-

zens may someday need, is no guarantee that their data is

any more secure. On the contrary, it will serve only to hinder

the progress of that research, delaying, or even preventing,

the development of new treatments in demand by EU citi-

zens.

2.f. Striking a balance between between privacy, security,

and innovation

Today in Europe there is an increasing call by some for un-

ambiguous data protection regulation that could ease new

scientific research instead of hampering it.

Regulators should understand that where and when strong,

consistently audited data security measures are applied, the

benefits of medical research in all probability outweigh the

putative risks associated with the use of patients’ data by

health professionals.

A harms-based approach, focusing more on the regulation of

possible misuse of data, rather than on limitations of

3 Note that a 100% anonymisation of patient data is impossible, or they may be rendered useless. Whether, for all practical purposes, data are anony-mised with a probability of 99.9%, 99%, or 95% will depend on the availability of other data on individuals (which is hard to predict for the future), computer power and related factors. This seems to be a largely unexplored field. Of great interest, however, is the legal framework adopted by the p-medicine project, based on the following pillars: Pseudonymization In clinical care pseudonymization should be the norm, but, when in need of using these data for research or to upload them with semantic links to a data warehouse, a second pseudonymization should take place, performed by a trusted third party (TTP) which is in case of p-medicine is the CDP (Center for Data Protection; http://www.privacypeople.org). This pseudonymization is done using a tool called CAT (Custodix Anonymization Tool). Contracts between data providers and data users These contracts bind users legally to use the data only according to the research for which the data are requested. Fines are defined if somebody tries to re-identify a subject. National differences (which might disappear after the approval of the new Data Protection Regulation) Today there are different laws regarding data protection in different member states throughout Europe, where the usage of data is different.

Informed consent is seen as a fallback in this legal framework Data are regarded as de-facto-anonymous if they fulfil the above mentioned points.


5


usage, is likely to be the most appropriate approach for striking

a reasonable balance between privacy, security, and innova-

tion.

Article 70 of the new draft Data Protection Regulation suggests

that the “indiscriminate general notification obligation should

be abolished, and replaced by effective procedures and mecha-

nism which focus instead on those processing operations which

are likely to present specific risks to the rights and freedoms of

data subjects by virtue of their nature, their scope or their pur-

poses. In such cases, a data protection impact assessment

should be carried out by the controller or processor prior to the

processing, which should include in particular the envisaged

measures, safeguards and mechanisms for ensuring the protec-

tion of personal data particularly as specified in the enhanced

consent and for demonstrating the compliance with the Regula-

tion”.

Article 71 significantly specifies that “this should in particular

apply to newly-established large scale filing systems, which

aim at processing a considerable amount of personal data at

regional, national or supranational level and which could affect

a large number of data subjects”.

This paper proposes the inclusion of a concept of enhanced

consent where the data subject is able specifically to exclude

certain data usage whilst allowing data utilisation for the bene-

fit of, for example, healthcare research, alongside maintaining

and ensuring that consent can be withdrawn and data com-

pletely deleted.

As described in the Regulation, all data management will be

under the auspices of a controller4 to ensure compliance and

thus the rights of the citizen are protected and the benefits of

the utilization of Big Data in research, innovation and business

development in healthcare are retained and developed.

3. Dealing with the Data Glut

With the ever-increasing volumes of data being produced ou-

tstripping5, and arguably being driven by, the computing power

available to analyse them (quantum computing aside possibly),

we already are faced with the reality that we have too much

data. As of 2011, this “data glut” was estimated to be 150 exa-

bytes (150 billion gigabytes) for healthcare globally.

To make sense of so much data, where sense is to be found, will

require innovative analytical techniques that can make it possi-

ble to efficiently search, process and analyse these massive da-

tasets. Some handle may be gained over the torrent of data by

reducing the dimensionality of a dataset.

Feature selection methods, whether selecting features on the

basis of existing medical knowledge or on statistical techni-

ques, can be used to map a dataset with many feature di-

mensions to one with significantly fewer, thus creating a

manageable search space. This simpler search space is then

used for querying and analysis, with the full dataset only

referred to when necessary, allowing for the application of

goal-oriented search techniques such as Model-Driven Ana-

lysis. Fractional Factorial Design uses this sort of approach to

concentrate search efforts in areas of a multi-dimensional

dataset that have been selected by searching a lower-

dimensional one that it has been mapped to.

Following a top-down system level approach, Feedback Sy-

stem Control (FSC) has also been proposed recently to redu-

ce the number of experiments in in silico clinical trials. FSC

was shown to efficiently hone-in on an optimised drug com-

bination with 102–106 times fewer experiments than a typi-

cal high throughput (“brute force”) approach. As opposed to

collecting all measurable data and trying to find a needle in a

haystack, the FSC scheme is a goal-oriented method, which

uses the phenotypic outcome to tune the intervention of

engineering systems, achieving system-in-system integra-

tion.

These various techniques allow us to find correlations,

patterns and structures in overwhelming volumes of data,

giving them value.

They reinforce the fact that data do not possess inherent

value in the absence of a means to make sense of them.

They are meaningless until analysed for significance, visual-

ised within a context or compared to other data. This means

that the value of a dataset will vary according to its context.

The corollary of this is that the value of a dataset is the sum

of its value in each analytical context. This fits neatly with

the concept that it is the research results, services and prod-

ucts generated from data that will provide the value in a Big

Data economy.

3.a. Statistical and mechanistic methods within the moving

boundaries of the “dimensionality curse”

Ultimately, data will be as useful as the knowledge that can

be derived from them, implicitly or explicitly. Two diverging

modelling approaches need, therefore, to be taken into ac-

count here. On the one hand, the bottom-up approach,

based on statistical models that can identify patterns and

correlations between observed variables in large datasets,

and that are necessarily dependent on the data to which

they are trained.

These models will not reveal fundamental causality between

variables or dynamic aspects, as would be needed for under-

standing complex biological processes. In the era of Big Da-

ta, however, a priori knowledge is supposedly not enough

but given the fact that valuable information exists is various

4 Who in turn needs to be controlled or audited.

5 For instance, in genetics DNA gene sequencing machines based on Big

Data analytics can now read 26 billion characters of the human geno-

me in seconds.


6


data sources, these can be additionally used for enriching the

global amount of available information. Thus, Big Data (bottom-

up) knowledge discovery and data mining (KDD) can remain the

primary goal, even if, to address such a challenge, it becomes

necessary to move beyond classical – one source, one modality

– statistical simulation models, and there is the need of analys-

ing and combining information from different data sources (e.g.

biomedical data & literature) and modalities (e.g. clinical varia-

bles & images or streams).

On the other hand, following a top-down approach, mechanis-

tic models, based on an understanding of the behaviour of bio-

logical systems’ components, are obviously more difficult to

build and generally larger in scale, but are more robust to noise

and to local inaccuracies, and eventually provide, when execut-

ed properly, unprecedented power for extrapolation and pre-

diction in domains in which the other techniques fail.

This is why, approaches such as those advocated by molecular

systems biology, or more recently by the Virtual Physiological

Human across all scales, need to be fully exploited in healthca-

re. The development of such models, requiring identification of

the system nonlinearities and model structure and estimates of

the system model parameters, implies, however, a computation

time directly related to the number of terms and increasing

dramatically with the model order. A trade-off between accura-

cy and complexity becomes therefore the way to avoid the

“curse of dimensionality” leading eventually to a computatio-

nally intractable combinatorial optimisation problem.

Furthermore, when studying highly complex and inherently non

-linear physiological systems, it would not make sense to assu-

me that their model order and structure can be sufficiently well

known a priori. In fact, in biological systems analysis the main

objective is precisely to gain insight into the underlying structu-

re and the system identification is based on a learning process,

associating parameter estimation with structure selection algo-

rithms, aimed at finding the simplest possible model capable of

revealing the unifying rule relating the components and beha-

viours of the system under study. However, since patterns di-

scovered through KDD enrich already existing a priori knowled-

ge, and the latest statistical simulation models can incorporate

a priori logic (e.g. formulating constraints, imposing dependen-

cies, etc. ), a sensible goal is to combine both bottom-up and

top-down approaches.

The search for parsimonious and computationally efficient me-

thods enabling the determination of the simplest model struc-

ture, has additionally led to the development of methods that

can cope with partial and/or incomplete mechanistic knowled-

ge, such as the nonlinear, autoregressive, moving average exo-

genous (NARMAX) structure detection approach, pioneered for

many years by the signal processing and complex systems re-

search of Stephen Billings and others at the University of

Sheffield.

This method is based on black-box parametric system identi-

fication and leads to a mathematical model on the basis of

input/output causal relationship calculated with non-linear

difference equations. It has demonstrated a remarkable ca-

pability of accommodating the dynamic, complex and nonli-

near nature of real-world time series prediction problems,

successfully achieving the goal of determining the model

form and estimating the numerical values of the unknown

parameters, and eventually validating those results which

have shown sufficient accuracy. In the analysis of physiologi-

cal systems this mathematical modelling can allow to inte-

grate very large data sets into a consistent information

framework, which can further be used to arrive at novel

mechanistic insight and predictions.

3.b. The value of bio-medical Big Data repositories

Given the fact that data are “experience goods”, according

to Arrow’s paradox it should be true that once access to

them has been awarded, their value should be significantly

reduced because of the inherent non-rivalry and non-

excludability characteristics of open access information.

However, Big Data repositories coupled with analytics can be

utilised in a variety of excludable ways and their value is not

critically influenced by Arrow’s paradox, while the

“experience goods” challenge mainly concerns the availabil-

ity of “enough” descriptive information about the data, their

structure, process of collection, and possibly “teasers” about

the analyses or outputs from the database.

However, it remains true – as stated in the OECD Report on

“Supporting Investment in Knowledge Capital, Growth and

Innovation” – that in order to reap this growing value there

will be the need not only for clinicians and researchers to

acquire Big Data analytics skills and services, but also to de-

velop a framework for data repositories which adheres to

international standards for the preservation of data, sets

common storage protocols and metadata, protects the in-

tegrity of data, establishes rules for different levels of access

and defines common rules that facilitate the combining of

datasets and improve interoperability. These frameworks

could, someday, render some of today’s data protection

rules and procedures invalid.

Such repositories can allow “testing” of the prior knowledge

of clinicians, who identify the data features deemed to be

key for specifying a patient’s treatment, versus the correla-

tions that big data crunching may highlight, possibly leading

to further knowledge discovery.

Indeed, by statistically and semantically reasoning on the

data, existing pathophysiological patterns may be revealed

and inputted as a first step in a fractional factorial and model

driven research process supporting physicians in their


7


iterative and interactive quest to discovering new knowledge.

The goals here are: to be able to provide model-driven patient-

specific predictions and simulations and consequent optimised

personalised clinical workflows, to allow for advanced similarity

search among patients, such that clinicians can find “the patient

like mine”, and to get support through risk stratification and

outcome analysis. Eventually it is hoped that specific patho-

physiological patterns (“disease signatures”) can be detected,

refined and made available to other clinicians and researchers

in the form of pattern libraries. These pattern libraries, identify-

ing homogenous groupings among patients and model similari-

ties, could be shared between researchers and clinicians to al-

low for data intensive pathophysiological diagnoses. Allied to

the above is the potential to revolutionise health communica-

tions by making it possible, on the basis of semantically ad-

vanced repositories, to use social media among patients aware

of sharing highly similar conditions (“patients exactly like us”),

empowering them to bridge the gap with the clinicians, espe-

cially in the case of paediatric patients and their parents.

4. The “Long Tail” potential in bio-medical research

The Pareto Distribution, introduced in 1906 by the Italian econ-

omist Vilfredo Pareto, described a feature of social distributions

which has been a recurrent mantra in organizational studies.

Presented in simplified form as Pareto's Principle, or the “80/20

Rule”, it states that 20 percent of something would normally be

responsible for 80 percent of the results.

In the economic arena, this traditional 80/20 concept has begun

to be reversed, as highlighted by the innovative insights in Chris

Anderson’s book, The Long Tail (2006). This developed the con-

cept that, when transaction costs are greatly lowered, “the big-

gest money is in the smallest sales”, whereby a series of small

niches cumulatively achieve a much larger amount than the

traditional focus on selling the preferred 20% of the items.

Internet businesses such as Amazon have demonstrated this,

because they have infinite shelf space and a radically different

cost structure to “bricks and mortar” retailers. The long tail has

been extremely lengthened as a result. Consumers really can

find and choose whatever they want, no matter how popular or

sought after the item is. While such retail niches were not eco-

nomically viable in the past, they can now better fulfil the mar-

ket.

An analogy to epidemiological studies can apply here. What

works for business transactions can also work for clinical inter-

actions. Big Data applications in medicine radically change the

capacity for going beyond the average patient population,

searching for specific cohorts of patients fitting into very peculi-

ar niches of their own.

Such an approach can show the way to truly personalised medi-

cine. Applying the Long Tail insight to biomedical research and

to drug discovery, will lead to people receiving treatments

and drugs specifically targeted to their own genomic, prote-

omic, and metagenomic characterisation. Tailoring treat-

ments, drugs and research to everyone's individual needs is

precisely what the long tail's approach is about. Thus focus

on the long tail in healthcare should allow medicine to

better address all those diseases and ailments suffered by a

relatively small number of people or by a large number of

people whose common conditions have numerous underly-

ing causes.

Big data allows for “long-tail medicine” drugs with enhanced

personalised information content, based on customized al-

gorithms tackling the individual disease conditions that can

be best addressed only by personalised treatment. Notably,

such conditions may often be the most serious. For example,

cardiovascular disease is responsible globally for more mor-

bidity, mortality and economic burden than any other dis-

ease. Despite growing knowledge about its various causes

and risk factors, it remains difficult to tackle in a preventa-

tive sense. A key element of this difficulty results from the

complex nature of the disease, arrived at by a multitude of

pathways, influenced by genome, metagenome and environ-

mental exposures. Thus, no single intervention or set of in-

terventions applied in a blanket fashion to the population

has been able to tackle this disease adequately. Personalised

interventions, resulting from a big data focus on the dispar-

ate underlying genotypic and phenotypic drivers of the dis-

ease offers a possible solution to this.

There are also “orphan” diseases, which affect relatively

small numbers of people. The low prevalence of these dis-

eases results in little or no direct research investment being

made by industry to understand them or to develop new

treatments for them. The biopharmaceutical industry in par-

ticular is still largely focusing on a “one size fits all” ap-

proach, but “one size” medicines do not fit all patients, and

the same is true of the R&D process. The limitations of this

approach have become increasingly clear, starting from the

need of increased paediatric knowledge discovery, as exem-

plified at a basic level by the Paediatric-use marketing au-

thorisations which are promoted by the European Medicines

Agency.

Data sets from a sub-population or from longitudinal clinical

data have the potential to expedite the development of tar-

geted therapies in terms of both patient population and

disease. This model of research is in its infancy but has tre-

mendous implications for medical knowledge discovery and

for future drug development.

5. Big Data Literacy in Healthcare

In parallel to the legal and technological challenges we have

outlined above, and to the fact that the healthcare domain

is known for its ontological complexity, variety of medical


8


data standards and variable data quality, there are also

healthcare cultural changes that will be required to fully capital-

ise on Big Data Healthcare as a movement. This especially re-

volves around the need for medical staff to be educated using

examples of its success. Clinicians will need to understand and

be persuaded of the potential of Big Data Healthcare, as well as

its limits. To maximise the acceptability and utility of Big Data

Healthcare solutions in the clinical arena, clinicians should

therefore be involved in the development of these solutions

from their inception. Thus, a clinically led development ideology

will ensure that technical know-how and innovation translates

into clinically useful tools that fit more naturally into clinical

workflows. Clinicians and engineers must work together to

translate and extend their existing and advanced data analysis

technology, that is the clinically trained human mind, into tar-

geted big data analytical approaches that will achieve clinically

useful outputs. Although engineers and clinicians have long

collaborated successfully, development work in Big Data

Healthcare will require particularly intimate reciprocal under-

standing by each disciplinary culture of the other. This will re-

quire further cultural development in both areas.

This should be achieved at a grass-roots level by a greater em-

phasis of clinical informatics in medical curriculums and similar

exposure of developing bioinformatics engineers to the unique

challenges that medical bioinformatics faces.

At the same time, decision support systems will need to be

more than black boxes but be capable of showing clinicians why

they advocate particular courses of action, providing the neces-

sary assurance that advice is based on sound principles.

Diffusion of medical technologies is necessarily a lengthy pro-

cess. This means that these processes of education and culture

shift should begin now, preparing new generations of clinicians

and bioinformatics engineers for forthcoming data intensive

methodologies and collaboration.

The following points will need therefore to be fleshed out:

Management of big data

Seamless end-to-end big data curation

Data discovery, profiling, extraction, cleaning, integration, analysis, visualisation, summarisa-tion, explanation

Use of big data

Appropriate use of big data – avoiding over-reliance

Responsible use of automated techniques

Communicating big data findings to patients

Integrating data analytics into clinical workflows

Data (clinical) scientist

6. The consequences for policy-making

When Jean Monnet launched the European construction in

1950, he based it on coal and steel, as these sectors were of

strategic importance at the time. In an updated spirit, Nellie

Kroes has suggested that the unifying vision for Europe

should presently be based on two concepts: “being wired”

and “being digital”. Sometime earlier, she had also stated

that “knowledge is the engine of our economy, and data is

its fuel”.

It is hard to disagree with her. However, this vision brings

with it the need to go beyond the usual proliferation of EC-

jargon acronyms that hint at barely specified future goals.

In line with the targets set out in the Europe 2020 Strategy,

the issue of Big Data – as already mentioned - has now been

singled out as a political priority at the 24-25 October Euro-

pean Council. There is consequently much discussion about

creating a European Big Data Analytics Service (EBDAS) net-

work, aimed at fostering a sustainable, smart and inclusive

European Big Data economy, while the Digital Agenda for

Europe (DAE) has been assigned the overall task of creating a

European digital single market through a number of

measures directed at the use of data sources in Europe. All

this will need to pass through the EU Open Data strategy

(conceived of as an amendment of the Public Sector Infor-

mation Directive), the Data Value Strategy (addressing the

entire data life-cycle), and the crucial Data Protection Regu-

lation.

At the same time, due to the staggering, and ever growing,

size and complexity of bio-medical big data, computational

modelling in this area is likely to provide the most intriguing

insights into the emerging complementary and synergistic

relationship between computational and living systems. Bio-

medical research institutions and related industries, as well

as the whole healthcare and pharmaceutical sectors, must

therefore be considered as key stakeholders in the European

process leading to the next generation of data-centric sys-

tems. Whether these are systems capable of learning from

data, or data-analysis products and applications capable of

translating medical knowledge discovery into widespread

medical practice, they will put predictive power in the hands

of clinicians and healthcare policy makers.

First of all, this requires the swift definition of an updated

legal and ethical framework that adequately protects citi-

zens on a European-wide basis. This is a necessary premise

for allowing private and public European investment to be

directed towards this crucial area of innovation. Doing so

will support the entrepreneurial initiatives aimed at com-

moditising once complex big data analytics, foster massive

clinical uptake, and reinforce European competitiveness in

medical developments. FP8 European research funding

needs to be directed accordingly.


Innovation and Big Data

Edwin Morley-Fletcher

President Lynkeus Consultancy

Rome, Italy

Beatrice Bressan

Coordinator, TOTEM Outreach CERN, the European Organization for Nuclear Research

Geneva, Switzerland A succession of paradigm shifts has accompanied the varying perceptions of wealth in

economics. Starting from those who thought that wealth resided in agriculture, moving then

to those who saw wealth as correlative to the division of labour, and further to those who

posited it as corresponding to profits, in opposition to rents and wages, as industrialisation

became, for some, synonymous with exploitation. Marginalism redirected economics away

from this connection, positing wealth as the outcome of perfect competition, where profits

would wane. Economies of scale were then left as only factor of increasing returns operating

as engine of economic growth. This pleased at the same time free-traders, empire builders,

and socialists and communists, who were both striving to transform the whole of society into

a single factory. Schumpeter’s ‘creative destruction’, followed, on the one hand, by Frank

Knight’s and John Maynard Keynes’ distinction between risk and uncertainty, showed a

different reading of capitalism, while on the other hand socialism’s demise appeared to

originate from having pursued accumulation without disruptive innovation. The sudden shock

incurred since 2008 by globalisation and free flows of capitals proved financial stability to be

still an inherently uncertain precondition for securing further growth and a larger diffusion of

wealth. Since 1990 knowledge had, however, started to be theoretically perceived as the

endogenous factor that really allows increasing returns. This implied the need to understand

what interconnected set of market and non-market institutions could best make the innovation

process work effectively, and what role should be taken by governments. Being immaterial,

knowledge appears to be limitless and cognitive capitalism passage ‘from atoms to bits’

entails various post-scarcity features. Non-rivalness, non-excludability, cumulativeness, as

well as network effects, jointly lay the ground for moving past abundance. Finding the

adjacent scarcity allows to discover potential economies of scale which ensue from free (or

nearly free) digital goods opening up new markets where to exert monopoly power and reap

extraordinary profits. Where the primary remaining scarce resource is human creativity, the

relative advantages of peer production emerge, opening to criticism of traditional intellectual

property rights. Peer-produced knowledge can be more cost-effective than either markets or

hierarchies, provide better information about available human capital, and better allocate

creative efforts, by taking into account the highly variable and individuated nature of human

intellectual efforts. The ecosystem of technologies is expected to grow in complexity and

colonise new areas. Some anticipate an ‘intelligence explosion’ that will ultimately make

humans and their machines merge and become indistinguishable. The HBP (Human Brain

Project), IBM’s Watson, and MD-Paedigree (Model-Driven European Paediatric Digital

Repository) testify the growing and converging power of Big Data analytics, when applied to

healthcare. Amidst signals of growing difficulties in guaranteeing the sustainability of

welfare states, rather than a further cost driver, a knowledge-based redesign of healthcare

systems needs now to become a political and economic priority for overcoming the residual

grip of scarcity which is hindering mankind from attaining its potential of holistic growth.

IIIa.1 The wealth of nations: agriculture, the division of labour, or profits?

A succession of paradigm shifts has accompanied the varying perceptions of wealth in

economics.

The Physiocrats1 believed that agriculture was the only activity generating a ‘net product’,

while industry was deemed to be ‘sterile’. Given this belief, that Nature allowed mankind to

get a surplus only in agriculture, they focused all they reform efforts in trying to spread what

they termed ‘la grande culture’, i.e., a reorganisation of land plots leading to the

establishment of big farms.

A great shift was brought forward by Adam Smith,2 who subverted the physiocrat approach

by persuasively arguing that the ‘wealth of nations’ resided instead in the division of labour.

His forecast was that the latter, by increasing specialization, and having it mediated through

the ‘invisible hand’ of the market, would bring about rising efficiency in production and

rising living standards.

However, then came David Ricardo. Expounding on Smith’s basis, Ricardo argued that the

wealth of nations was founded on profits, in opposition to agricultural rents and working

class salaries. While wages, were not due to increase in conditions of abundance of

population, because of the working of the ‘iron law of wages’, growth was destined – in

Ricardo’s forecast – to peter out owing to the scarcity of land and the correlative increase of

rents, unless, through free trade, Britain were to take advantage of comparative costs and

become the workshop of the world, purchasing most of her food overseas.

IIIa.2 Industrialisation and/or exploitation Saint Simon saw wealth as depending on the process of ‘industrialisation’ (this was the new

term that he coined), which allowed the translation of the scientific and technical revolution

of his age into economics. On this basis, he could project a future society in which poverty

and war would eventually be eradicated, provided the ‘industrial system’ were to be applied

on sufficiently large scale. In his Parabole politique3 he posited that if France were to lose

her 50 best scientists, artists, military officers, physicians, entrepreneurs and craftsmen, it

would take her a whole generation to recover, while if 30,000 of her nobles and bureaucrats

were suddenly to disappear, this would not be such a dramatic loss, given that “current

society is truly an inverted world, where in all types of occupations men who are not capable

are in charge of directing the capable ones.”4,5

Karl Marx, introduced in his youth to the writings of Saint Simon by his future father–in-

law,6,7

stated that the bourgeoisie had “accomplished wonders” and “conducted expeditions”

that surpassed by far any of those of the past, and was “constantly revolutionising the

instruments of production, and thereby the relations of production, and with them the whole

relations of society.” By the “rapid improvement of all instruments of production, by the

1 F. Quesnay and the Marquis of Mirabeau, Philosophie rurale, 1763; Du Pont de Nemours, La

Physiocratie, 1768. 2 A. Smith, An Inquiry into the Nature and Causes of the Wealth of Nations, 1776

3 C.H. de Saint-Simon, L’Organisateur, 1819

4 N. Coilly et Ph. Régnier, La parabole de Saint-Simon, in Le siècle des saint-simoniens: du Nouveau

christianisme au canal de Suez, Bibliothèque nationale de France, 2006 5 R.B. Carlisle, The Proffered Crown: Saint-Simonianism and the Doctrine of Hope, Johns Hopkins

University Press, 1987 6 Baron Ludwig von Westfalen, whose daughter Jenny Karl Marx (1818-1883) married in 1843.

7 M. Kowalewsky’s personal recollections on Marx (1909), in L. Meldolesi, L’utopia realmente

esistente: Marx e Saint-Simon, Laterza, 1982

http://www.amazon.it/s?_encoding=UTF8&field-author=Robert%20B.%20Carlisle&search-alias=english-books

immensely facilitated means of communication”, the bourgeoisie – he reckoned – was

drawing “even the most barbarian nations into civilisation.” The cheap prices of commodities

– he added – were “the heavy artillery” with which all nations were being compelled, “on

pain of extinction, to adopt the bourgeois mode of production, […] i.e., to become bourgeois

themselves.”8

Modern industry, “by means of machinery, chemical processes and other methods”, was

“continually transforming not only the technical basis of production but also the functions of

the worker and the social combinations of the labour process”, revolutionising at the same

time “the division of labour within society, and incessantly throwing masses of capital and of

workers from one branch of production to another.”9

The flip side, in all this, was for Marx that profits were strictly founded on exploitation, i.e.,

on not recognising labour’s real value, but only its money-mediated market value, where the

labour force appeared to have a given price, as with any other good. This intertwining of

exploitation and capital accumulation made capitalism to be an inherently contradictory

process, leading inevitably – in Marx’s forecast – to under-consumption crises.

IIIa.3 Perfect competition, the disappearance of profits, economies of scale Marginalism

10 redirected the economic discourse away from the dangerous connection

between profits and exploitation, positing that ultimately wealth depended on the degree by

which society could approach perfect competition, where marginal returns on all forms of

resource investment would eventually be equalised.

This way, entrepreneurs’ profits would tend to fall in line with capitalists’ interests, and vice

versa. This link would work to minimise both their rates. Economies of scale,11

could,

however, allow for increasing returns operating as engine of economic growth, but also

leading to the monopolisation of markets, as large firms would develop lower cost structures

than smaller firms, driving smaller competitors out of business. Though this was not fully

realised then, the economies of scale were therefore contradicting the assumption of perfect

competition being the preferred social outcome as the fundamental mechanism for

maximising wealth.

The pursuit of economies of scale could justify free trade, as well as, conversely, empire

building and ‘imperial preference’. As a synonym of modern industry, economies of scale

also became a supporting argument for socialism. The greatest economy of scale would in

fact be attained by organising society as ‘a single, huge industrial company’12

: a goal

8 K. Marx and F. Engels, Manifesto of the Communist Party, 1848

9 K. Marx, Das Kapital, Kritik der politischen Ökonomie, Volume 1, Verlag von Otto Meissener,

1867 10

Started by three forerunners: Stanley Jevons (1835-1882), Karl Menger (1840-1921), and Léon

Walras (1834-1910). 11

Alfred Marshall (1842-1924). 12

K. Kautsky, Das Erfurter Programm: An earlier draft of this programme, adopted by the Social

Democratic Party of Germany during the congress held at Erfurt in 1891, had been commented upon

by Friedrich Engels (1820-1895), who, on 18 and 19 June 1891, had famously written that the aim

should be “the transformation of present capitalist production […] into socialist production on behalf

of society as a whole and according to a preconceived plan” (A Critique of the Draft Social-Democratic Program of 1891). Kautsky (1854-1938) had specified that it was “by no means

necessary” that the passage to socialism “be accompanied with violence and bloodshed. […] Neither

[was] it necessary that the social revolution be decided at one blow” since “revolutions prepare

themselves by years or decades of economic and political struggle” (Das Erfurter Programm).

commonly shared by both Kautsky and Lenin,13

even though they conflicted on all other

issues.

IIIa.4 Creative destruction

At this point, however, a young Austrian economist, Joseph Schumpeter, had entered the

scene. In a path-breaking book,14

both the static marginalist vision and Marx’s picture of

exploitative capitalism were replaced by the process of what Schumpeter termed ‘creative

destruction’, fuelled by a new character: “The dynamic, innovating entrepreneur as the

lynchpin of the capitalist system, responsible not just for technical progress but for the very

existence of a positive rate of profit.”15

As Schumpeter later further clarified,16

the initial

super-profits of the innovator were a sort of ‘quasi-rent’, derived by a position of temporary

monopoly supply of a particular good, which in due course would be eroded by imitators,

requiring new innovations from the successful entrepreneur to make him survive.

Lately, Schumpeter’s vision has been sharply criticised by the 2006 Nobel laureate for

economics, Edmund Phelps.17

Schumpeter’s context was one in which an outstanding

German intellectual personality like Max Weber had in his vocabulary “no room for

experimentation, exploration, daring, and unknowability”18

(so much so – we may add – that

moving away from the weberian ‘synoptic rationality’ assumption with regard to public

decision makers has required, some decades later, the fundamental theoretical switch brought

forward by the Herbert Simon’s19

‘bounded rationality’20

concept). It is not surprising,

therefore, that Schumpeter still thought that, though “getting the job done” was a particularly

onerous task, the “likelihood of an ‘innovation’ [was] as knowable as the prospects faced by

established products. There [would not be any] chance of misjudgement, provided there

[were] due diligence. An expert entrepreneur’s decision to accept a project and a veteran

banker’s decision to back it [would be] correct ex ante, even uncanny, though ex post bad

luck might bring a loss and good luck an abnormal profit.”21,22

13

V.I. Lenin, The State and Revolutionn (1917): “The whole of society will become a single office

and a single factory, with equality of labour and pay.” For Lenin there was, however, a proviso: it

should be born in mind that “this ‘factory’ discipline, […] is by no means

our ideal, or our ultimate goal. It is only a necessary step for thoroughly cleansing society […]

and for further progress.” 14

W.J.A. Schumpeter, Theorie der wirtschaftliche Entwicklung, Duncker & Humboldt, 1912 15

M. Blaug, Great Economists before Keynes, Wheatsheaf Books, 1986 16

W.J.A. Schumpeter, Business Cycles: A Theoretical, Historical and Statistical Analysis of the Capitalist Process, McGraw Hill, 1939; History of Economic Analysis, Allen & Unwin, 1954 17

In 2006, Edmund Phelps was awarded the Nobel Prize in Economic Sciences “for his analysis of

intertemporal tradeoffs in macroeconomic policy”: www.nobelprize.org/nobel_prizes/economic-

sciences/laureates/2006. 18

E. Phelps, Mass Flourishing: How Grassroot Innovation Created Jobs, Challenge, and Change,

Princeton University Press, 2013 19

In 1978, Herbert A. Simon was awarded the Nobel Prize in Economic Sciences: “for his pioneering

research into the decision-making process within economic organizations”:

www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1978. 20

H.A. Simon, Administrative Behavior: a Study of Decision-Making Processes in Administrative Organization, Macmillan, 1947; Models of Bounded Rationality: Behavioral Economics and Businees Organization, The MIT (Massachusetts Institute of Technology) Press, 1982. 21



N. Rosenberg, Schumpeter and the Endogeneity of Technology: Some American Perspectives,

Routledge, 2000.

http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/2006



On a more practical note, some years before, an influential American business consultant,

Clayton Christensen, had already highlighted the “dilemmas posed to innovators by the

conflicting demands of sustaining and disruptive technologies.”23,24

In a book later to be

thought of as revolutionary in its own domain, he had showed how “very capable executives

in […] extraordinarily successful companies, using their best managerial techniques”25

, could

perfectly face the challenges of developing and adopting sustaining innovations, but had

often “led their firms toward failure”26

, when confronting disruptive technological changes

which could allow to reap the real competitive advantage, by “facilitating the emergence of

new markets”27

, and where leadership would eventually be paying off huge dividends. The

point was – Christensen stated – that, on one hand, “innovation is inherently unpredictable”28

,

because “not only are the market applications for disruptive technologies unknown at the time

of their development, they are unknowable”; and on the other hand, that “amid all the

uncertainty surrounding disruptive technologies, managers can always count on one

anchor”29

, namely that “experts’ forecasts will always be wrong.”30

IIIa.5 Risk and uncertainty

Whatever the current perception of the limitations of Schumpeter’s assumptions regarding the

key role of innovation in driving long-term economic growth, it deserves to be noted that the

initial focus on the crucial issue of unpredictability had been prompted, already in 1921, by

an American economist, Frank Knight. He had in fact posited that it was precisely “true

uncertainty” – which he defined as being “not susceptible to measurement and hence to

elimination” – that was “preventing the theoretically perfect outworking of the tendencies of

competition.”31

This was, according to him, the reason which gave “the characteristic form of

23

C.M. Christensen, The Innovator’s Dilemma, Harper, 2000 24

A similar distinction, dubbed ‘incremental innovation’ and ‘radical innovation’, is at the core of

David Sainsbury’s Progressive Capitalism, though on a much different note. Following the literature

on the “varieties of capitalism” (M. Morishima, Why Has Japan 'Succeeded'?: Western Technology and the Japanese Ethos, Cambridge University Press, 1982; R. Dore, Taking Japan Seriously: A Confucian Perspective on Leading Economic Issues, Athlone Press, 1987; M. Albert, Capitalisme contre capitalisme, Seuil, 1991; D. De Giovanni et al., Le politiche industriali della Cee, Il Sole 24

Ore, 17, 24, 31 May, 1991; C. Hampton-Turner and F. Trompenaars, The Seven Cultures of Capitalism, Doubleday, 1993; K. Seitz, Die japanisch-amerikanische Herausforderung, Aktuell

Verlag, 1994; S. Berger and R. Dore (Eds.), National Diversity and Global Capitalism, Cornell

University Press, 1996; R. Dore, Stock Market Capitalism: Welfare Capitalism: Japan and Germany versus the Anglo-Saxons, OUP (Oxford University) Press, 2000; P.A. Hall and D. Soskice (Eds.),

Varieties of Capitalism: Institutional Foundations of Comparative Advantage, OUP, 2001; F.

Giavazzi and R. Prodi, Discutono di modelli di capitalismo, Il Mulino, 2011. Sainsbury posits that

Germany and Japan excel in the first type of innovation, while the US in the second, and that “neither

variety of capitalism is consistently better than the other, and the task of economic policy-makers has

therefore to be the improvement of their particular variety of capitalism and its constant adaptation to

the changing economic and technological opportunities and challenges that it faces” (Progressive Capitalism: How To Achieve Economic Growth, Liberty and Social Justice, Biteback Publishing,

2013). 25

C.M. Christensen, The Innovator’s Dilemma, Harper, 2000 26

Ibidem 27

Ibidem 28

Ibidem 29

Ibidem 30

Ibidem 31

F.H. Knight, Risk, Uncertainty and Profit, Houghton Mifflin Co., 1921

‘enterprise’ to economic organization”, while also accounting for “the peculiar income of the

entrepreneur”32

: had there not been such uncertainty, the entrepreneur’s income would not be

“‘what is left’, after the others are ‘determined’”33

– stated Knight – but only the normal

return required to pay the manager’s wage and the competitive level of interest to creditors.

Though sharply aware of the crucial role of irreducible uncertainty in economic life, John

Maynard Keynes objected then that “to convert the business man into the profiteer” would be

a sure way for striking “a blow to capitalism, because it [would] destroy the psychological

equilibrium which permits the perpetuance of unequal rewards. The economic doctrine of

normal profits, vaguely apprehended by everyone, is a necessary condition for the

justification of capitalism. The business man is only tolerable so long as his gains can be held

to bear some relation to what, roughly and in some sense, his activities have contributed to

society.”34

Still, Keynes’ most eminent biographer, Robert Skidelsky, is right in stating that “uncertainty

pervades Keynes’s picture of economic life. It explains why people hold savings in liquid

form, why investment is volatile, and why the rate of interest doesn’t adjust savings and

investment. […] Under capitalism, uncertainty is generated by the system itself, because it is

an engine for accumulating capital goods whose rewards come not now but later. The engine

of wealth creation is at the same time the source of economic and social instability.”35

Keynes

prescription would follow Knight’s distinction between risk and uncertainty: in general terms,

“risk could be left to look after itself; the government’s job was to reduce the impact of

uncertainty.”36

Controlling demand, while not interfering with the supply of goods,37

was for

Keynes what ought therefore to be the first objective of government policy to maintain

economic and political uncertainty within acceptable bounds. A second level of intrinsic

uncertainty, related to investment and financial markets, should be countered, in practical

terms, by national and international monetary policy, and culturally by spreading the

philosophical choice of “arranging our affairs in such a way as to appeal to the money-motive

as little as possible, rather than as much as possible.”38

IIIa.6 Accumulation without innovation The disquieting association of profits, innovation, and uncertainty, that was coming to

maturity in those years, was meanwhile looked at in a dismissive way by those who were

pursuing the maximum economies of scale through economic planning. In this sense, it can

be noted that when Joseph Stalin promoted a process of accelerated industrialisation in the

32

Ibidem 33

Ibidem 34

J.M. Keynes, The Collected Writings of John Maynard Keynes, in R.E. Backhouse and B.W.

Bateman, Capitalist Revolutionary: John Maynard Keynes, p. 60, Harvard University Press, 2011 35

R. Skidelsky, Keynes, The Return of the Master, Allan lane, 2009 36

Ibidem, 37

A choice which was in line with the traditional and highly influential approach of John Stuart Mill

(1806-1873), who famously stated that “the laws and conditions of the Production of wealth partake

of the character of physical truths. […] We cannot foresee to what extent the modes of production

may be altered […] by future extensions of our knowledge of the laws of nature, suggesting new

processes of industry of which we have at present no conception. But howsoever we may succeed in

making for ourselves more space within the limits set by the constitution of things, we know that there

must be limits. […] It is not so with the Distribution of wealth. That is the matter of human institution

solely. The things once there, mankind, individually or collectively, can do with them as they like”

(Principles of Political Economy, Book 2, 1852). 38

J.M. Keynes, The End of Laissez-Faire (1926), in Essays in Persuasion, The Collected Writings of

John Maynard Keynes, Volume 9, p. 293, MacMillan-Cambridge University Press, 1989

Soviet Union he ended up by providing exactly what was to be the most compelling example

of the unintended consequences of trying to use forced accumulation as the key to economic

development. As Jeffrey Sachs and John McArthur put it, “the Soviet economy had very

little technological change in the civilian sector for decades and, as a result, came about as

close as possible to a case of a high saving rate combined with stagnant technology. It is

probably fair to say that it proved a key result […]: capital accumulation without

technological advancement eventually leads to the end of economic growth.”39

Ironically, the

process which had had as grand goal the “universal development of productive forces”40

,

leading to a situation where “with the all-around development of the individual, […] all the

springs of co-operative wealth [would] flow more abundantly”41

, endogenously turned,

through centralised planning, into a system which in 1980 would be bluntly characterised as

the Economics of Shortage by the great Hungarian economist, Janos Kornai.42

IIIa.7 The real engine of economic growth

When these outcomes became suddenly apparent to all, in the last decade of the 20th

century,

the euphoria engendered by the ‘victory of capitalism’ and the ‘demise of socialism’ induced

many to believe that globalisation, in conjunction with the free flow of capitals, would bring

about increasing wealth, provided economic growth could be channelled into boundaries of

ecological sustainability.

Forgetting about intrinsic economic uncertainty, the theory of efficient market expectations

assumed that “financial markets [were] equivalent to insurance markets”43

, until the financial

crisis of 2008 proved once more that financial stability was still an inherently uncertain

precondition for securing further growth and a larger diffusion of wealth.

The economic slowdown which followed led not only to increased competition, but also

highlighted the growing impossibility for advanced economies to guarantee a continual

growth of output just by increasing the inputs to production process, as understood during

industrialisation. A new paradigm won through: the key driver should be believed to be none

of those referred to until then; knowledge, and its continuous translation into innovation and

knowledge and technology transfer, were instead to be seen as the real engines of long-term

economic growth.

In broad conceptual terms, Hayek44

had laid the ground for this new way of looking at

development by introducing already in the 30s the idea that in a complex economy know-how

had necessarily to be highly dispersed and no central planner would ever be able to put it all

39

J.D. Sachs and J.W. McArthur, Technological Advancement and Long-Term Economic Growth in Asia, in Chong-En Bai and Chi-Wa Yuen (Eds.), Technology and the New Economy, p. 161, MIT

Press , 2002 40

K. Marx, The German Ideology, 1845. 41

K. Marx, Critique of the Gotha Programme, 1875 42

J. Kornai, Economics of Shortage, North-Holland, 1980; Growth, Shortage and Efficiency,

Blackwell, 1982; Contradictions and Dilemmas: Studies on the Socialist Economy and Society, The

MIT Press, 1986; The Socialist System: The Political Economy of Communism, Clarendon Press, 1992 43

R. Skidelsky, Keynes, The Return of the Master, Allan lane, 2009 44

In 1975 Frederick A. Hayek was awarded the Nobel Prize in Economic Sciences jointly to G.

Myrdal “for their pioneering work in the theory of money and economic fluctuations and for their

penetrating analysis of the interdependence of economic, social and institutional phenomena”: www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1974.


together;45

furthermore, in 1968 he had come to dub competition itself as a “discovery

procedure.”46

IIIa.8 Endogenous Technological Change

How the reorientation of economic research could technically make its way towards

recognising a fundamental role to knowledge is in its own right a fascinating tale. This tale

has been neatly told by, amongst others,47,48

David Warsh,49

showing how it started from the

matter of fact assessment by Robert Solow,50

in 1957, that technological change accounted

for seven-eighths of the growth of the US economy while increases in capital stock accounted

for only one-eighth of the growth of income per person in the US.51

The new perception of

the problem then slowly moved on, passing first through Lucas52

and Prescott in 1971,53’54

then Martin Weitzman in 1982,55

followed by Elhanan Helpman and Paul Krugman56

in

1987,57,58

until entered the scene Paul Romer in 1990.59

Romer clearly posited knowledge as

45

F.A. Hayek, Socialist calculation, p. 181 (1935); Economics and Knowledge, p. 33 (1937), The Use of Knowledge in Society, p. 77 (1945), in F. A. Hayek, Individualism and Economic Order, University

of Chicago Press, 1948 46

F. von Hayek, Competition as a Discovery Procedure (1968), in F. A. Hayek, New Studies in Philosophy, Politics, Economics and the History of Ideas, p. 179, Routledge & Kegan, 1978. 47

D. Acemoglu, Introduction to Modern Economic Growth, Princeton University Press, 2008 48

P. Aghion and P. Howitt, The Economics of Growth, The MIT Press, 2009 49

D. Warsh, Knowledge and the Wealth of Nations: A Story of Economic Discovery, Norton, 2006 50

In 1987, Robert Solow was awareded the Nobel Prize in Economic Sciences “for his contributions

to the theory of economic growth”: www.nobelprize.org/nobel_prizes/economic-


R.M. Solow Technical Change and the Aggregate Production Function, Review of Economics and

Statistics, Volume 39, N. 3, p. 312, 1957; The last 50 years in growth theory and the next 10, Oxford

Review of Economic Policy, Volume 23, N. 1, p. 3, 2007 52

In 1995, Robert E. Lucas was awarded the Nobel Prize in Economic Sciences “for having

developed and applied the hypothesis of rational expectations, and thereby having transformed

macroeconomic analysis and deepened our understanding of economic policy”:

www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1995. 53

R.E. Lucas and E.C. Prescott, Investment Under Uncertainty, Econometrica, Volume 39, N. 5, p.

659, 1971 54

R. Lucas, On the Mechanics of Economic Development, Journal of Monetary Economics, Volume

22, Issue 1, p. 3, 1988; Why Doesn't Capital Flow from Rich to Poor Countries, American Economic

Review, Volume 80, N. 2, p. 92, 1990 55

M. Weitzman, Increasing Returns and the Foundations of Unemployment Theory, Economic

Journal, Volume 92, Issue 368, p. 782, 1982. 56

In 2008, Paul Krugman was awarded the Nobel Prize in Economic Sciences “for his analysis of

trade patterns and location of economic activity”: www.nobelprize.org/nobel_prizes/economic-


E. Helpman & P. Krugman, Market Structure and Foreign Trade: Increasing Returns, Imperfect Competition, and the International Economy, MIT Press, 1987 58

E. Helpman, The Mystery of Economic Growth, Harvard University Press, 2004 59

P.M. Romer, Endogenous Technological Change, Journal of Political Economy, Volume 98, N. 5,

p. 71, 1990; Increasing Returns and Long Run Growth, Journal of Political Economy, Volume 94, N.

5, p. 1002, 1986; Growth Based on Increasing Returns due to Specialization, American Economic

Review, Volume 77, N. 2, p. 55, 1987; Crazy Explanations for the Productivity Slowdown, NBER

(National Bureau of Economic Research) Macroeconomics Annual, Volume 2, p. 163, 1987; New goods, old theory, and the welfare costs of trade restrictions, Journal of Development Economics,

Volume 43, Issue 1, p. 5, 1994






the endogenous factor that economists had hitherto failed to take adequately into account: its

accumulation and deepening was the source of increasing returns, which explained why

economic growth could be accelerating in rich countries, where the standard of living was

diverging rapidly from poorer countries, contradicting the classical economic law of

diminishing returns. It was by investing in knowledge that the final output could increase

more than proportionately, because of the impetus given this way to the introduction of new

technology.

A metaphor, Romer later made use of, was plainly taken from everybody’s experience in the

kitchen: “To create valuable final products – he stated - we mix inexpensive ingredients

together according to a recipe. […] If economic growth could be achieved only by doing

more and more of the same kind of cooking, we would eventually run out of raw materials

[…]. History teaches us, however, that economic growth springs from better recipes, not just

from more cooking. […] Every generation has perceived the limits to growth that finite

resources and undesirable side effects would pose if no new recipes or ideas were discovered.

And every generation has underestimated the potential for finding new recipes and ideas. We

consistently fail to grasp how many ideas remain to be discovered. […] Possibilities do not

add up. They multiply.”60

IIIa.9 The appropriate set of market and non-market institutions Clearly, this new approach implied the need to understand what interconnected set of market

and non-market institutions could best make the innovation process work effectively.61

This

subsequently implied understanding what type of strategies governments should follow in

order to foster highly innovative economic systems.

A crucial feature of any innovation strategy would be, for instance, betting on promoting

enhanced higher education as a major long-term investment by government. Such a public

policy priority would go hand in hand with the need to fostering sufficient scale for a

dynamic environment where scientific and technological progress could jointly feed the

development of a competitive innovation system.62

As further feature, the option of an export

oriented, open economy would assume the whole world as a potential market, compensating

60

P.M. Romer, Economic Growth, in D.R. Henderson (Ed.), The Concise Encyclopedia of Economics,

Liberty Fund, 2007

61 The need to be aware of the “richness of the institutional alternatives between which we have to

choose” had already been most influentially brought forward by Ronald Coase (The Choice of Institutional Framework, Journal of Law and Economics, Volume 17, Issue 2, p. 493, 1974; The Institutional Structure of Production, The American Economic Review, Volume 82, Issue 4, p. 713,

1992), Oliver Williamson (The Economic Institutions of Capitalism: Firms, Markets, Relational Contracting, The Free Press, 1986), and Douglass North (Institutions, Institutional Changes and Economic Performance, Cambridge University Press, 1990). 62

Both the above reported features are, by the way, not at all new. They can be traced back to Das Nationale System Der Politischen Ökonomie (1841), where Friedrich List (1789-1846) had written

that “the present state of the nations is the result of the accumulation of all discoveries, inventions,

improvements, perfections, and exertions of all generations which have lived before us: they form the

mental capital of the present human race, and every separate nation is productive only in the

proportion in which it has known how to appropriate these attainments of former generations, and to

increase them by its own acquirements. […] There scarcely exists a manufacturing business which has

no relation to physics, mechanics, chemistry, mathematics or to the art of design, etc. No progress, no

new discoveries and inventions can be made in these sciences by which a hundred industries and

processes could not be improved or altered. In the manufacturing State, therefore, sciences and arts

must necessarily become popular” (D. Sainsbury, Progressive Capitalism: How To Achieve Economic Growth, Liberty and Social Justice, Biteback Publishing, 2013).

this way, by the scope of the market, the significant R&D investments which had to be taken

into account as necessary ‘fixed costs’ and ‘barriers to entry’ of innovation endeavours, and

which needed therefore to be recouped through subsequent worldwide sales. Last but not

least, special financing mechanisms beyond the banking sector would prove to be crucial,

since banks would normally not fund projects, even if based on excellent ideas, because

knowledge constituting their background would be intangible and non-collateralisable. And if

banks do not and should not lend for non-collateralised ideas, the innovation process requires

then somebody else who will be able to consider ‘creativity and vision are resources’63

:

venture capitalists, in the first place, and more generally a capital market making it possible

for successful IPOs (Initial Public Offerings) to raise equity. This way, the openness to

innovation processes implies not only specific forms of organisation that develop, test, and

prove ideas,64

and “bureaucratic regulatory environments that [do not] impede capital and

labour movements [nor] place unnecessary burdens on firm creation and dissolution”65

, but

also a general frame of mind that truly takes into account that the economic death of old

sectors is part and parcel of the advance of new sectors.66

IIIa.10 Limitless knowledge

On top of these, other features appear to be further disruptive on a deeper theoretical level.

While, with industrial development, economic growth was based on the usage of limited

natural resources, knowledge, being immaterial, appears to be limitless. The passage “from

atoms to bits”67

seems to imply also that the number of bytes we make use of can continue to

grow exponentially.

This is not, however, the only post-scarcity element characterising what has been termed

‘cognitive capitalism.”68,69,70

Paradoxically, Marx’s insight on machines, being “organs of the

human brain, created by the human hand”, which express “the power of knowledge,

objectified”71

, acquires in this context a prophetic echo. So much so, as to announce with an

anticipation of 150 years, “the beginning of a new phase of the division of labour in which

the development of fixed capital indicates to what degree general social knowledge has

become a direct force of production, and to what degree, hence, the conditions of the process

of social life itself have come under the control of the general intellect and been transformed

in accordance with it; to what degree the powers of social production have been produced,

63



Ibidem: McKinsey estimated that, from 10,000 business ideas, 1,000 firms are founded, 100 receive

venture capital, 20 go on to raise capital in an initial public offering of share, and 2 become market

leaders. 65

R.D. Atkinson and S.J. Ezell, Innovation Economics: The Race for Global Advantage, Yale

University Press, 2012 66

Ibidem: Atkinson and Ezell remark that “even though Scumpeter was a European, Europeans are

not Schumpeterians. They want the benefit of a knowledge-based economy without the creative

destruction that not only accompanies it but also is required to achieve it”. 67

N. Negroponte, Being Digital, Knopf, 1995 68

C. Vercellone (Ed.), Capitalismo cognitivo: Conoscenza e finanza nell’epoca post-fordista,

Manifestolibri, 2006 69

Y. Moulier Boutang, Le capitalisme cognitif: La nouvelle grande transformation, Editions

Amsterdam, 2007 70

A. Fumagalli and S. Lucarelli, A model of Cognitive Capitalism: a preliminary analysis, European

Journal of Economic and Social Systems, Volume 20, N. 1, p. 117, 2007 71

K. Marx, The Fragment on Machines, Grundrisse, 1857-1858

not only in the form of knowledge, but also as immediate organs of social practice, of the real

life process.”72

It is irrelevant to establish, here, whether Marx was positing, in these passages, “the

possibility of a direct transition to communism.”73

What is significant for us is the fact that

the current “radical transformation of the foundations of wealth”74

appears to be the

consequence of a “virtualisation of the economy” entering into a condition of growing non-

scarcity.

The non-rivalness,75

non-excludability,76

cumulativeness,77

and network78

characteristics of

knowledge have “the potential of creating a ‘combinatorial explosion’ [allowing it to achieve]

“almost infinitely increasing returns.”79

Correlatively, a knowledge-based economy would be,

however, expected to prevent natural market incentives from achieving allocatively efficient

outcomes. But instead of determining a “tragedy of the commons” – as would run a much

cited theory put forward by Garrett Hardin80

– the final result looks likely to be a ‘happy

ending’, provided it is understood that “for managing ‘knowledge commons’ the social

regulations which will be needed are to be totally different from those generally used for

regulating systems founded on exhaustible resources.”81,82

IIIa.11 Post-scarcity and networks

To explain this assumption, Yann Moulier Boutang has recourse to the metaphor of

pollination by bees. Google – he writes – employs 19,000 people at its premises in Mountain

View, California, while having at the same time 15,000,000 people who work for it for free,

producing information, creating a network: “The value of this network creation activity can

be compared to that developed by bees when they pollinate. The value of their honey, and of

the wax extracted from their alveoles, when brought to the market, is from 350 to 1,000 times

72

Ibidem, Chapter The Development of Machinery: “In a word – Marx had stated, just a few lines

above – it is the development of the social individual which appears as the great foundation-stone of

production and of wealth”. In publishing Das Kapital, Marx would confine in a footnote the remark

that: “A critical history of technology would show how little any of the inventions of the 18th century

are the work of a single individual. Hitherto there is no such book.” 73

C. Vercellone, The Hypothesis of Cognitive Capitalism, paper presented at the Birkbeck College

and at SOAS (School of Oriental and African Studies) Annual Conference on Towards a

Cosmopolitan Marxism, 2005 74

Y. Moulier Boutang, Le capitalisme cognitif: La nouvelle grande transformation, Editions

Amsterdam, 2007 75

Non-rivalness is the characteristic of indivisible benefits of consumption, such that one person’s

consumption does not preclude that of another. 76

Non-excludability is the characteristic that makes impossible to prevent others from sharing in the

benefits of consumption. 77

Cumulativeness is the characteristic that allows increasing the value of existing knowledge by

adding further knowledge to it. 78

Network is the characteristic by which the utility that a user derives from consumption of a good

increases with the number of other agents consuming the good. 79

D. Foray, L’économie de la connaissance, La Découverte, 2000 80

G. Hardin’s thesis was that, when sharing a resource, individuals acting independently and having

in mind only each one's self-interest, would act contrary to the group's long-term best interests and

deplete the common resource (The Tragedy of the Commons, Science, Volume 168, N.3859, p. 1242,

1968) 81

See footnote 743 82

S. J. Liebowitz and S. E. Margolis, Network Externality: An Uncommon Tragedy, Journal of

Economic Perspectives, Volume 8, N. 2, p. 133, Spring, 1994

lower than the value of the pollination that they perform. Google achieves to draw market

profit from human pollination.”83

On a different side of the political spectrum, a similar perception of the role of networks –

which exist when a product’s value to the user increases as the number of users of the product

grows and “each new user of the product derives private benefits, but also confers external

benefits (network externalities) on existing users”84

– has led to focus on the concept that

“today the most interesting business models are in finding ways to make money around

Free”85

, i.e., to make use of network effects and of the correlated economies of scale in

providing for free (or almost free) a digital good to create a new market where they can exert

a strong monopoly power and reap extraordinary profits.86,87

Abundance thinking – as Chris Anderson has written – is not only discovering “what will

become cheaper, but also looking for what will become more valuable as a result of that

shift, and moving to that. It’s the engine of growth, something we've been riding since even

before David Ricardo defined the ‘comparative advantage’ of one country over another in the

eighteenth century. Yesterday’s abundance consisted of products from another country with

more plentiful resources or cheaper labour. Today’s also consists of products from the land of

silicon and glass threads.”88

Chris Anderson has tried to describe some of the conceptual passages implied by the shift

from scarcity to abundance as shown in table IIIa.1. His basic assumption is that the new way

to compete within the Free economic environment will be “to move past the abundance to

find the adjacent scarcity. If software is free, sell support. If phone calls are free, sell distant

labour and talent that can be reached by those free calls (the Indian outsourcing model in a

nutshell). If your skills are being turned into a commodity that can be done by software

(hello, travel agents, stockbrokers, and realtors), then move upstream to more complicated

problems that still require the human touch. Not only can you compete with Free in that

instance, but the people who need these custom solutions are often the ones most willing to

pay highly for them.”89

Table IIIa.1 Examples of scarcity and abundance thinking.90 Scarcity Abundance

Rules Everything is forbidden unless it is

permitted

Everything is permitted unless it is

forbidden

Social model Paternalism (‘We know what's best’) Egalitarianism (‘You know what's best’)

Profit plan Business model We'll figure it out

Decision process Top-down Bottom-up

Management style Command and control Out of control

IIIa.12 Intellectual property rights

Would, however, Free destroy the financial incentives which are needed to push individuals

and firms to undertake the costs, risks, and efforts of developing new knowledge?

Particularly in research – it had been stated – “there are huge incentives to free-ride the

83

Y. Moulier Boutang, L’abeille et l’économiste, Carnets Nord, 2010 84

W.H. Page and J. E. Lopatka, Network Externalities, Encyclopedia of Law and Economics, 1999 85

C. Anderson, Free: The Future of a Radical Idea, Random House, 2009 86

O. Bomsel, Gratuit! Du déploiment de l’économie numérique, Gallimard, 2007 87

O. Shy, The Economics of Network Industries, Cambridge University Press, 2001 88

See footnote 749 89

Ibidem 90

Ibidem

system: let someone else finance the big breakthroughs and let us focus our money on

development, where big payoffs are not far away. Even among governments, there is now an

incentive to free-ride and let some other government somewhere else in the world pay for the

basic research.”91

According to Christensen, the argument that Free represents an attack against intellectual

property rights “straddles the line between libre and gratis. […] The history of intellectual

property [is] based on the long traditions of the scientific world, where researchers freely

build on the published work of those who came before. In the same vein, the creators of the

patent system (led by Thomas Jefferson) wanted to encourage sharing of information, but

they realized that the only way people thought they could get paid for their inventions was-to

hold them secret. So the founding fathers found another way to protect inventors––the

seventeen-year patent period. In exchange for open publication of an invention (libre), the

inventor can charge a license fee (not gratis) to anyone who uses it for the term of the patent.

But after that term expires, the intellectual property will be free (gratis). […] However, a

growing community of creators doesn’t want to wait that long. They’re choosing to reject

these rights and release their ideas […] under licenses such as Creative Commons […]

believe that real Free – both gratis and libre – encourages innovation by making it easier for

other people to remix, mash up, and otherwise build on the work of others.”92

On the whole, innovative societies have adopted pragmatic compromises, such as that basic

scientific discoveries, in general, are not patentable, and that patents are limited to specific

new technologies, and – as we have just seen - are given for a limited period of time. Still, the

indivisibility, uncertainty and externalities characteristics of knowledge-generating activities

may determine a situation of ‘market failure’: on the one hand, markets provide too few

incentives to introduce new innovations, and the production of information may well,

therefore, be insufficient from a social point of view; on the other, innovators and creators

face a consistent risk of incomplete returns for their efforts.

IIIa.13 Governments’ support of scientific research

This problem of appropriability has widely led governments to support basic scientific

discovery through direct subsidization of primary research in universities, government

research laboratories, and even private companies that qualify for government grants. But in

fact the state does more than just fix market failures: “Large-scale and long-term government

investment has been the engine behind almost every general purpose technology in the last

century.”93,94,95,96,97

This is true for ICT (Information and Communication Technology), but

91

L. Thurow had squarely advocated that, “just as the industrial revolution began with an enclosure

movement in England that abolished common land and created private land, the world now needs an

organized enclosure movement for intellectual property rights” (The New Rules For Individuals, Companies, and Nations in a Knowledge-Based Economy, Chapters: Building Wealth and Building Wealth, HarperCollins, 1999). 92

C. Anderson states: “Generation Free – he further posits – doesn’t assume that as go bits, so should go atoms.

They don’t expect to get their clothes or apartments for free; indeed the’re paying more than ever for those. Give

the kids credit: They can differentiate between the physical and the virtual, and they tailor their behavior

differently in each domain” (Free: The Future of a Radical Idea, Random House, 2009). 93

V.W. Ruttan, Is War Necessary for Economic Growth?: Military Procurement and Technology Development, in M. Mazzucato, The Entrepreneurial State: Debunking Public vs. Private Sector Myths, p. 62, Anthem Press,

2013 94

F.L. Block and M.R. Keller (Eds.), State of Innovation: The U.S. Government’s Role in Technology Development, Paradigm, 2011 95

E. Helpman (Ed.), General Purpose Technologies and Economic Growth, The MIT Press, 1998

also “three-quarters of the new molecular biopharmaceutical entities owe their creation to

publicly funded laboratories,” and “in the past ten years, the top ten companies in this

industry have made more in profits than the rest of the Fortune 50098

companies combined.”99

Yet, in general, “nothing at all is paid in royalties. It is simply assumed that the public

investment is meant to help create profits for the firms in question, with little to no thinking

about the obvious distorted distribution of risk and reward this presents.”100,101

This has led Mariana Mazzucato to devote a chapter of her 2013 book, The Entrepreneurial State, to “socialisation of risk and privatisation of rewards,” and to raise the question “can the

Enterpreneurial State eat its cake too?”102,103

Whatever will be the prevailing answer, and whether research funding may come to look

more like venture capital, maintaining a stake in the future exploitation of results, the key

theoretical issue which is at stake implies reaching a much deeper understanding of the way

markets, organisations, and “commons-based peer production” reciprocally position

themselves with regard to their specific comparative advantages.

IIIa.14 The remaining scarce resource is human creativity Yochai Benkler is the author who has most eminently raised the possibility that a ‘networked

information economy’ can prove more economically efficient than one in which innovation is

encumbered by the strict enactment of exclusive intellectual property rights, when the

marginal cost of re-producing most information is effectively nearing zero. In a context in

which physical capital costs for fixation and communication of knowledge are low and

widely distributed, and where existing information is itself a public good, the primary

remaining scarce resource – he states – is human creativity, and it is in this context that the

relative advantages of peer production emerge, highlighting how, under certain

circumstances, peering can in fact be a more cost-effective institutional form than either

markets or hierarchical organizations.104

Benkler has provided a simple table showing how ideal organisational forms can be deducted

as a function of relative social cost (table IIIa.2).

Table IIIa.2 Ideal organisational forms as a function of relative social cost.105

96

B. Jovanovic and P.L. Rousseau, General purpose technologies, in P. Aghion and S. Durlauf (Eds.),

Handbook of Economic Growth, Elsevier, 2005 97

; R.G. Lipsey et al., Economic Transformations: General Purpose Technologies and Long-Term Economic Growth, OUP, 2005 98

The Fortune 500 is an annual list compiled and published by Fortune magazine that ranks the top

500 U.S. closely held and public corporations as ranked by their gross revenue after adjustments made

by Fortune to exclude the impact of excise taxes companies incurred. The list includes publicly and

privately held companies for which revenues are publicly available. The first Fortune 500 list was

published in 1955. 99

M. Mazzucato, The Entrepreneurial State: Debunking Public vs. Private Sector Myths, Anthem

Press, 2013 100

M. Angell, The Truth about the Drug Companies, Random House, 2004 101

M. Mazzucato and G. Dosi (Eds.), Knowledge Accumulation and Industry Evolution: The Case of Pharma-Biotech, Cambridge University Press, 2006. 102

See footnote 763 103

L. Burlamaqui et al., Knowledge Governance: Reasserting the Public Interest, Anthem, 2012 104

Y. Benkler, The Wealth of Networks: How Social Production Transforms Markets and Freedom, Yale

University Press, 2006 105

Y. Benkler, Coase’s Penguin, or, Linux and The Nature of the Firm, The Yale Law Journal, Volume 112,

Issue 3, p. 400, 2002

Property system more valuable than

implementation costs

Property system implementation costs higher than

opportunity cost Market exchange of x more efficient

than organizing or peering of x

Markets

(Farmers markets)

Commons

(Ideas & facts; roads)

Organizing x more efficient than

market exchange or peering of x

Firms

(Automobiles; shoes)

Common property regimes

(Swiss pastures)

Peering of x more efficient than

organizing or market exchange of x

Proprietary ‘open source’ efforts

(Xerox’s Eureka)

Peer production processes*

(NASA106

Click workers)

*‘Cost’ would include the negative effects of intellectual property on dissemination and downstream productive

use.

IIIa.15 Different organisational modes for overcoming uncertainty Behind this taxonomy, there is the taking into account of recent fundamental shifts in

economic reasoning, which Yochai Benkler summarises into four conceptual moves:

1. Ronald Coase107

asked why clusters of individuals operate under the direction of an

entrepreneur, a giver of commands, rather than interacting purely under the guidance of

prices, and answered that using the price system is costly (in terms of ‘transaction

costs’).108

2. Assuming that the cost of organization increases with size, Coase consequently posited

that “any given firm will cease to grow when the increased complexity of its

organization makes its internal decision costs higher than the costs that a smaller firm

would incur to achieve the same marginal result.”109

Coase could assume, this way, to

have a “natural” – i.e., internal to the theory – limit on the size and number of

organizations.

3. Harold Demsetz’s treatment of property rights110

added a specific element of

explanation: property in a resource emerges if the social cost of having no property in

that resource exceeds the social cost of implementing a property system in it.

4. Benkler posits that, when “the social cost of using existing information as an input into

new information production is zero”, “the digitization of all forms of information and

culture has made the necessary physical capital cheaper by orders of magnitude than in

the past”, and “the dramatic decline in communications costs radically reduces the cost

of peering”, the centrality of human capital to information production and its variability

becomes “the primary source of efficiency gains from moving from markets or

hierarchical organization to peering.”111

Hence the conclusion that “commons-based

106

NASA (National Aeronautics and Space Administration) 107

In 1991, Ronald H. Coase was awarded the Nobel Prize in Economic Sciences “for his discovery

and clarification of the significance of transaction costs and property rights for the institutional

structure and functioning of the economy”: www.nobelprize.org/nobel_prizes/economic-


R.H. Coase, The Nature of the Firm (1937), now in The Firm, the Market, and the Law, University of

Chicago Press,1988 109

Ibidem 110

H. Demsetz, Toward a theory of property rights, American Economic Review, Volume 57, N. 2, p.

347, 1967; Ownership, Control, and the Firm: The Organization of Economic Activity, Blackwell,

1988 111

Y. Benkler, Coase’s Penguin, or, Linux and The Nature of the Firm, The Yale Law Journal,

Volume 112, Issue 3, p. 404, 2002



peer production creates better information about available human capital and can better

allocate creative effort to resources and projects.”112

Different organisational modes have different strategies for overcoming persistent

uncertainty: markets reduce uncertainty regarding allocation decisions through prices that act

as clear and comparable signals for choosing which use of the relevant factors would be most

efficient; firms or hierarchical organizations resolve uncertainty by instituting a process by

which information about which actions to follow is ordered. Both are, however, ‘lossy’

mediums, in the sense – specifies Benkler – that “much of the information that was not

introduced in a form or at a location that entitled it to ‘count’ toward an agent’s decision is

lost.”113

IIIa.16 Information and allocation gains of peer production

It is with reference to this intrinsic ‘lossiness’ that peering may end up having lower

information opportunity costs than markets or hierarchies. In particular, Benkler suggests that

the primary source of gains – which he calls ‘information gains’ – peer production offers is

its capacity to collect and process information about human capital.

“Human intellectual effort – he posits – is highly variable and individuated. People have

different innate capabilities, personal, social, and educational histories, emotional

frameworks, and ongoing lived experiences. These characteristics make for immensely

diverse associations with, idiosyncratic insights into, and divergent utilization of, existing

information and cultural inputs at different times and in different contexts. Human creativity

is therefore very difficult to standardize and specify in the contracts necessary for either

market-cleared or hierarchically organized production.”114

In addition to these information gains, peering has also potential allocation gains, enabled by

the large sets of resources, agents, and projects available to this form of organisation. “As

peer production relies on opening up access to resources for a relatively unbounded set of

agents, […] this is likely to be more productive than the same set could have been if divided

into bounded sets in firms.” Furthermore, while the use of a rival resource would have

excluded the use by others, this is not true for a purely non-rival good like information, where

the allocation gain can be attained by “allocating the scarce resource – human attention,

talent, and effort – given the presence of non-rival resources to which the scarce resource is

applied with only a probability of productivity.”115

Benkler’s conclusion entails, therefore, a criticism of any attempt to apply strong intellectual

property rights with regard to peer-produced information: “Since the core of commons-based

peer production entails provisioning without direct appropriation and since indirect

appropriation—intrinsic or extrinsic—does not rely on control of the information but on its

widest possible availability, intellectual property offers no gain, only loss, to peer

production.”116

The idea that openness and connectivity may, in the end, be more valuable to innovation than

purely competitive mechanisms is shared also by Steven Johnson, who states that “we are

often better served by connecting ideas rather than we are by protecting them. […] good ideas

112

Y. Benkler, The Penguin and the Leviathan: How Cooperation Triumphs over Self-Interest, Crown

Business, 2011 113

Y. Benkler, Coase’s Penguin, or, Linux and The Nature of the Firm, The Yale Law Journal,

Volume 112, p. 409, 2002 114

Ibidem, p. 412 115

Ibidem, p. 421 116

Ibidem

may not want to be free, but they do want to connect, fuse, and recombine. They want to

reinvent themselves by crossing conceptual borders. They want to complete each other as

much as they want to compete.”117

Ideas – he writes – “rise in liquid networks where connection is valued more than

protection”118

, and on this ground “perhaps ‘commons’ is the wrong word […], though it has

a long and sanctified history in intellectual property law. The commons’ metaphor doesn’t

suggest the patterns of recycling and exaptation119

and recombination that defer so many

innovation spaces. I prefer another metaphor from nature: the [coral] reef”120

, which is the

most extraordinary engine of biological innovation: “Coral reefs make up about one-tenth of

one percent of the earth’s surface, and yet roughly a quarter of the known species of marine

life make their homes there”121

, and what makes them so inventive is not “the struggle

between the organisms but the way they have learned to collaborate.”122

IIIa.17 An ecosystem of technologies leading to the singularity? By analogy – says Johnson – Kuhn’s paradigms of research are like coral reefs, “raised by

myriads of tiny architects”123

, which provide fertile environments for new developments and

represent “the scientific world’s equivalent of a software platform: a set of rules and

conventions that govern the definition of terms, the collection of data, and the boundaries of

inquiry for a particular field.”124

Since modern scientific paradigms are rarely overthrown,

but rather tend to be built upon, the outcome of such a process is a stacked platform that

supports new paradigms above the old ones, and, “in a funny way, the real benefit [of it] lies

in the knowledge you no longer need to have.”125

Such an ecosystem of technologies is expected to grow in complexity and colonise new areas,

just like life itself. Kevin Kelly calls it the ‘technium’, and describes it as a living, evolving

organism that has its own unconscious needs and tendencies: just as water “wants” to flow

downhill and life tends to fill available ecological niches, so technology is similarly due to

expand and evolve.126

In a visionary book published in 2005,127

the Director of Engineering at Google, Ray

Kurzweil, predicted that 2045 will be the year of the singularity,128

when computers meet or

exceed human computational ability and when their ability to recursively improve themselves

can lead to an ‘intelligence explosion’ that will affect all aspects of human culture and

technology, ultimately making humans and their machines merge and become

indistinguishable from each other. “Once a planet yields a technology-creating species –

117

S. Johnson, Where Good Ideas Come From: The Seven Patterns of Innovation, Penguin, 2011 118

Ibidem 119

The term is taken from S.J. Gould and E. Vrba, Exaptation: A Mising Term in the Science of Form,

Paleobiology, Volume 8, N. 1, p. 4, 1982, where they suggested that “characters, evolved other usages

(or for no function at all), and later, p. 6, ‘coopted’ for their current role, be called ‘exaptations’. 120

S. Johnson, Where Good Ideas Come From: The Seven Patterns of Innovation, Penguin, 2011 121

Ididem 122

Ibidem 123

This was the expression used by Charles Darwin admiring the Keeling Islands in April 1836 (The Voyage of the Beagle, Penguin, 1989). 124

See footnote 784 125

Ibidem 126

K. Kelly, What Technology Wants, Viking, 2010 127

R. Kurzweil, The Singularity Is Near: When Humans Transcend Biology, Duckworth, 2005 128

Mathematics defines a ‘singularity’ as a point at which an object changes its nature so as to attain

properties that are no longer the expected norms for that class of objects.

Kurzweil stated – and that species creates computation (as has happened here), it is only a

matter of a few centuries before its intelligence saturates the matter and energy in its vicinity,

and it begins to expand outward at least the speed of light (with some suggestions of

circumventing this limit). Such a civilization will then overcome gravity (through exquisite

and vast technology) and other cosmological forces – or, to be fully accurate, it will

manoeuvre and control these forces – and engineer the universe it wants. This is the goal of

the singularity.”129

IIIa.18 Big Data analytics and data-intensive healthcare

While the forecast of such technological singularity remains highly controversial, in 2009

Microsoft Research published a book of essays, entitled The Fourth Paradigm: data-intensive scientific discovery.130

One of the essays was dedicated to The Healthcare Singularity and the Age of Semantic Medicine,

131 on the assumption that medicine would be likely to be the first

Big Data domain where a threshold moment, dubbed the ‘Healthcare Singularity’, will be

reached, making medical knowledge become ‘liquid’ and its flow from research to practice

(‘bench to bedside’) become frictionless and immediate.132

In the few years which have passed, a number of projects and realisations have made similar

goals look now much nearer. The HBP (Human Brain Project)133,134,135

has been awarded 1

billion euros FET (Future and Emerging Technologies) flagship funding for the next 10 years

by the European Commission. IBM’s Watson,136

the artificially intelligent supercomputer

system capable of answering questions posed in natural language, which won the American

television quiz show Jeopardy!, can process 500 gigabytes, the equivalent of a million books,

per second, and uses natural language capabilities, hypothesis generation, and evidence-based

learning to support medical professionals as they make decisions. MD-Paedigree (Model-

Driven European Paediatric Digital Repository),137

the clinically-led FP7 IP (7th

Framework

Programme Integrated Project) that won the Best Exhibit Award at ICT ’13 European

Conference in Vilnius, Lithuania, in 2013, is applying Big Data analytics to build a scans-

based, genomic and meta-genomic repository of routine patients in four disease areas. The

‘model-driven’ repository is meant to provide, as key functionalities: 1) similarity search,

allowing clinicians to access ‘patients like mine’ (and finding decision support for optimal

treatment also based on comparative outcome analysis), and allowing patients to get in touch

with ‘patients exactly like me’; 2) model-based patient-specific simulation and prediction;

and 3) patient-specific clinical workflows.

129

R. Kurzweil, The Singularity Is Near: When Humans Transcend Biology, Duckworth, 2005 130

T. Hey et al. (Eds.), The Fourth Paradigm: Data-Intensive Scientific Discovery, Microsoft Research,

2009 131

M. Gillam et al., The Healthcare Singularity and the Age of Semantic Medicine, Microsoft , 2009 132

Ibidem 133

H. Markram et al., The Human Brain Project: A Report to the European Commission, HBP-PS

(Human Brain Project- Preparatory Study) Consortium, EPFL (École Polytechnique Fédérale,

Lausanne), 2012 134

R. Kurzweil, How To Create A Mind: The Secrets Of Human Thought Revealed, Viking, 2012 135

M. Kaku, The Future of the Mind: The Scientific Quest to Understand, Enhance and Empower the Mind, Penguin, 2014 136

J. E. Kelly III and S. Hamm, Smart Machines: IBM’s Watson and the Era of Cognitive Computing,

Columbia University Press, 2013 137

MD-Paedigree (Model-Driven European Paediatric Digital Repository) Newsletter, N. 1, 2013:

www.md-paedigree.eu.


Big Data analytics, and specifically Big Data healthcare, are becoming more and more a

generalised concern.138,139,140,141,142,143

Due to the staggering, and ever growing, size and complexity of bio-medical Big Data,

computational modelling in medicine is likely to provide the most intriguing insights into the

emerging complementary and synergistic relationship between computational and living

systems. Bio-medical research institutions and related industries, as well as the whole

healthcare and pharmaceutical sectors, must therefore be considered as key stakeholders in

the process leading to the next generation of data-centric systems. Whether these are systems

capable of learning from data, or data-analysis products and applications capable of

translating medical knowledge discovery into widespread medical practice, they will put

predictive power in the hands of clinicians and healthcare policy makers.

At the same time, Big Data applications in medicine radically change the capacity for going

beyond the average patient population, searching for specific cohorts of patients fitting into

very peculiar niches of their own. Such an approach can show the way to truly personalised

medicine. Applying the Long Tail144 insight to bio-medical research and to drug discovery,

will lead to people receiving treatments and drugs specifically targeted to their own genomic,

proteomic, and metagenomic characterisation. Tailoring treatments, drugs and research to

everyone’s individual needs is precisely the point of the long tail approach. Thus focus on the

long tail in healthcare should allow medicine to better address all those diseases and ailments

suffered by a relatively small number of people or by a large number of people whose

common conditions have numerous underlying causes. Furthermore, Big Data will allow for

‘long-tail medicine’ drugs with enhanced personalised information content, based on

customized algorithms tackling the individual disease conditions that can be best addressed

only by personalised treatment.

Amidst signals of growing difficulties in guaranteeing the long term sustainability of all

European welfare states, the acceleration of technological innovation in healthcare has long

appeared as a further cost driver. Leaving aside whether such acceleration could clear the

way to the hoped-for ‘singularity’, a knowledge-based redesign of healthcare systems

(including how to assess value in people’s lives), needs now to become a political and

economic priority for overcoming the residual grip of scarcity which is hindering mankind

from attaining its potential of holistic growth. Eventually, wealth is health.

138

N. Sliver, The Signal and the Noise: The Art and Science of Prediction, Penguin, 2012 139

B. Kayyali et al., The big-data revolution in US health care: Accelerating value and innovation,

McKinsey Company, 2013 140

Intel IT Centre, Big Data Analytics, Peer Research Report, Intel Corporation, 2013 141

E. Morley-Fletcher, Big Data Helthcare: An overview of the challenges in data intensive healthcare, discussion paper, Networking Session, ICT (Information and Technology

Communications) ‘13 Conference, 2013 142

V. Mayer-Schӧnberger and K. Cukier, Big Data: A Revolution That Will Transform How We Live, Work and Think, Murray, 2013 143

T. Davenport and J. Kim, Keeping Up with the Quants: Your Guide To Understanding and Using Analytics, Harvard Buisness Review Press, 2013 144

C. Anderson developed the concept that, when transaction costs are greatly lowered, “the biggest

money is in the smallest sales”, whereby a series of small niches cumulatively achieve a much larger

amount than the traditional focus on selling the preferred selection of block-buster items. The ‘long

tail’ can be extremely lengthened as a result. Consumers really can find and choose whatever they

want, no matter how popular or sought after the item is, and retail niches which were not

economically viable in the past, can now better fulfil the market. An analogy to epidemiological

studies does apply here (The Long Tail: How Endless Choice is Creating Unlimited Demand, Random

House, 2006).