Economic Performance and Sustainability of HealthGrids: Evidence from Two Case Studies Alexander DOBREV a1, Stefan SCHOLZ b, Dainis ZEGNERS a, Karl A. STROETMANN a, and Sebastian C. SEMLER b a empirica Communication and Technology Research, Bonn, Germany b Telematikplattform für medizinische Forschungsnetze (TMV e.V.), Berlin, Germany
Abstract. Financial sustainability is not a driving force of HealthGrids today, as a previous desk research survey of 22 international HealthGrid projects has showed. The majority of applications are project based, which puts a time limit of funding, but also of goals and objectives. Given this situation, we analysed two initiatives, WISDOM and MammoGrid from an economic, cost-benefit perspective, and evaluated the potential for these initiatives to be brought to market as selffinancing, sustainable services. We conclude that the topic of HealthGrids should be pursued further because of the substantial potential for net gains to society at large. The most significant hurdle to sustainability – the discrepancy between social benefits and private incentives – can be solved by sound business models. Keywords. Health Grids, economic, sustainability, business, market, success
Introduction Financial sustainability and economic performance are crucial challenges to every Grid project that is meant to be more than a technical showcase. Corresponding to the German MediGRID2 project [1], a satellite study was initiated by TMF3 to analyse the sustainability strategies of relevant Grid projects in the healthcare and bioscience sectors. A desk research survey conducted as first part of the study depicted 22 cases of grid technology in the healthcare sector, mainly from Europe, but also some important examples from the USA and Japan (ACGT, @neurIST, Biogrid/Japan, BioinfoGRID, caBIG, CDSS, CLEF, CONDOR, eDIAMOND, EIT, GEMSS, MAGIC V +, MammoGrid, MEGrid, NDMA, NEOBASE, NeuroGrid/Japan, NeuroGrid/UK, ProGenGrid, µGrid, VL- e, WISDOM) [2].
1
Corresponding author. empirica Communication and Technology Research, Oxfordstr. 2, 53111 Bonn, Germany,
[email protected] 2 MediGRID is part of the German D-Grid initiative, funded by the Federal Ministry of Education and Research (BMBF), 3 TMF e.V. is a German non-governmental management and support organisation for medical research, initiated and supported by the Federal Ministry of Education and Research (BMBF),.
22 20
Number of Cases
18 16 14 12 10 8 6 4 2 0
Academic
Industrial
Non-public research organisation
Associations
Healthcare provider
Others
no reliable information found
Type of participants Figure 1. Typology of participants in HealthGrid projects (surveyed in [2]).
Main areas of HealthGrid applications are medical imaging, image or visualisation processing, biomedical simulation, pharmaceutical and clinical research. The screened initiatives were driven by academic and semi-academic organisations (shown in figure 1), which explains the relative low level of business orientation. All of the cases on which information on financing was available made use of public funding (see figure 2). Private funding was explicitly involved in only two cases [2,3].
12
Number of Cases
10
8
6
4
2
0
Public funding
EC (EU Private funding Other sources 5FP/6FP/7FP) Type
Figure 2. Types of funding of HealthGrid projects (surveyed in [2]).
no details found
We found that financial sustainability is not a driving force of HealthGrids today. The majority of applications across Europe, and to a large extent in USA and Japan, are project based, which puts a time limit of funding, but also of goals and objectives. The work in the field is focused on development projects, illustration activities, test beds, proof-of-concept and proof-of-workability initiatives. Not surprisingly, information on potential or actual business cases is not readily available, and in most cases the design of an initiative would not provide any obvious way of even potentially converting the one-off project activity into a sustainable service. Given this situation, as second part of the study we analysed two initiatives in more detail. The two case studies, WISDOM and MammoGrid, underwent a costbenefit analysis in order to determine the socio-economic impact of using HealthGrids. Further, we analysed the potential for these initiatives to be brought to market as selffinancing, sustainable services. The rest of this paper deals with the socio-economic desirability, sustainability, and business aspects of HealthGrids illustrated by these two case studies.
1. Case Studies The two case studies were selected on the grounds that they both have clearly defined goals within one of the common content areas, imaging and pharmaceutical research, and target the two biggest beneficiary groups of HealthGrids – healthcare professionals and biomedical researchers. Further, both initiatives have managed to continue activity beyond the lifetime of the first research grant funding period, indicating the potential for sustainable business cases. 1.1. Case Study I: WISDOM [4] WISDOM (Wide In Silico Docking On Malaria) uses a computational grid application, combining different grid infrastructures, for the purpose of in silico docking tests as part of the drug discovery process. The focus is on neglected and emerging diseases, currently malaria and avian flu. The goal is to accelerate the discovery of novel potent inhibitors (chemical compounds hindering the life of a disease) by analysing potential chemical compounds in a simulation (in silico) run on a grid infrastructure. Laboratory (in vitro) research can then focus on the compounds most likely to have the desired effect on the disease. The first run of WISDOM was conducted in 2005 as part of an EU co-funded project and targeted potential inhibitors for Malaria. The WISDOM concept proved to be applicable to the fight against other diseases as well. A run in search of potential drug compounds against Avian Flu was conducted in 2006, followed by another one on Malaria in 2006-2007. 1.2. Case Study II: MammoGrid [5] The MammoGrid initiative is concerned with developing a European distributed reference database of mammograms, structured and accessed via a grid. The aim is to exploit the potential of grids to support collaboration between geographically dispersed healthcare professionals.
Development started with the Fifth Framework EU co-funded MammoGrid project (2002-2005) as a proof-of-concept activity. The aim was to prove that grid infrastructures can be used for collaborative clinical analysis of database-resident, but geographically distributed medical images. The main output of the MammoGrid project was a grid-enabled software platform (called the MammoGrid information structure) which federates multiple mammogram databases. Managing a federation of autonomous multi-centre sites, the application facilitates sharing pre-standardised mammograms, comparing diagnoses (with and without computer aided detection of tumours), and performing sophisticated epidemiological studies.
2. Methodology of evaluation and analyses The economic performance of the two initiatives were analysed on the basis of a costbenefit analysis (CBA), following the eHealth IMPACT methodology for evaluation of the socio-economic impact of eHealth [6]. CBA is a formal technique from economics used to decide on the comparative and absolute desirability of a project. From society’s perspective, desirable means that the expected positive impacts of a project are greater than its expected negative effects to all stakeholders. The values of these positive and negative effects, or benefits and costs, respectively, are estimated in monetary units in order to provide comparability. This does not necessarily mean that they are convertible to actual financial flows. The monetary values are not meant to be exact estimates, but rather indicate the order of magnitude and serve as an indicator for the desirability of the services. The eHealth IMPACT methodology [7] follows a three step approach: First we define the scope of the subject of evaluation i.e. the service or activity that utilises eHealth, and in this case a HealthGrid. Second, we identify the associated benefits and costs, and third we estimate their monetary value and aggregate them. Results are in the form of present value cumulative benefits and costs over time. Net benefits are discounted to present values in order to reflect the monetary value of time and risk. In addition, we analysed the potential long-term sustainability of the initiatives by reviewing relevant business aspects. We based our analysis on a recently developed business model framework for HealthGrids [8,9]. This framework identified three main components: utility view, which focuses on the product and its utility; value creation view, concerning the value chain and affected players; and capital view, outlining revenue and cost expectations. The framework emphasises the fundamental need of a customer-oriented value proposition and a reliable revenue model. In this paper, we concentrate on those latter aspects: • Target customers: First, we state the relevant customer groups and their specific characteristics and needs. The target customer is the crucial element of the overall value chain. He determines the value of all intermediate activities that may form own business cases. • Utility and value proposition: In a second step, we identify the main sources of added value that is provided by the grid applications and services. Based on that information, a project manager may define tailor-made products that address the specific needs of the identified customer groups. • Revenue model: In a further step, we specify the adequate revenue model. It indicates what specific price scheme is appropriate for a certain setup of
•
revenue partners (target customers) and revenue source (added value of a product/ value proposition). Challenges: Finally, we discuss the major challenges of the specific market, which managers of grid initiatives must address in order to succeed in providing sustainable grid-based services in the long run.
3. Results - Economic Performance and Business Aspects of HealthGrids 3.1. WISDOM The scope of the WISDOM service is defined as the preparation of targets (proteins vital to a disease), the in silico test of a large number of relevant chemical compounds, and the evaluation of the results. This service then “produces” a small number of high probability compounds that can enter the next stages of the drug development process. 3.1.1. Economic Performance The main benefits fall into the categories time savings, effectiveness, and efficiency. WISDOM speeded up the drug discovery process by 4 months for each malaria run. This potentially saves the lives of those who would have died, had the drug been discovered by the conventional methods. For the avian flu challenge, the benefits to citizens are mainly focused on being prepared for the possibility of a N1H5 pandemic breakout. Because the effects are related to the time when a new drug is actually being produced, benefits to people occur years after the WISDOM service. Researchers avoid extra computing costs by using existing grid infrastructures. The more significant benefit, however, is achieving this high quality outcome, measured by the rate of active molecules, compared to a process without using the grid infrastructure. Whether or not actual new drugs enter the market does not make a difference to this conclusion, as the benefits related to the use of a new drug are qualified by the probability of this actually taking place. The main costs include a proportional share of the set-up and running costs of the involved grids and labour costs allocated to each service run. The analysis, summarised in Figure 3, indicates that the use of grids for the already performed runs of WISDOM has been meaningful from an economic perspective. The present value of the cumulative benefits exceeded the costs as early as 2005, year two of the initiative. From there the cumulative benefits continue to increase, whereas the costs do not increase further. Activities to date have led to positive net economic returns of over 370% (measured as the ratio of estimated net benefits to estimated costs) over 12 years. Even though about half of the benefits can only be realised in the future, the performance is impressive: the net benefits to cost ratio over the first five years is 1.4:1.
17500 15000
€ 000s
12500 10000 7500 5000 2500 0 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Present value of cumulative costs
Present value of cumulative benefits
Figure 3. Estimated present value of cumulative costs and benefits from WISDOM.
3.1.2. Business Aspects The positive net benefit by no means proves a business case itself. There is a critical difference between economic net benefit and financial profitability. E.g. the benefits to citizens comprising 50% of total economic gain, will never convert into financial flows. For a sustainable commercialisation of the service, a business model must predict financial profitability of all involved in the medium term. Target Customers There are three main groups of potential service users, i.e. customers, of WISDOM: • public academic laboratories, • private small and medium sized enterprises (SMEs) researching targets, whose financial capacity is overstretched by the cost of conventional screening, and • pharmaceutical corporations. Whereas the last one is financially strongest, it poses challenges in terms of data security and intellectual property rights on service outcomes. Publicly available grids, used in WISDOM, are not deemed secure enough for working with proprietary ligand structure data (WISDOM uses public data). The attractiveness for the big pharmaceutical companies lies in the fact that WISDOM changes the cost structure for moving into the market segment of rare and emerging diseases. So far, the excessive costs associated with drug research required the promise of a large market, which a prioiri excluded research in this field. Another possible scenario is a Public-Private Partnership (PPP) constellation, where the established WISDOM service forms the core activity, embedded for instance into the World Health Organisation (WHO) Third World drug discovery and development program. In such a model, the client for WISDOM would be the WHO. Given the payment contributions of the public sector, industry players would have to accept certain features of the service. Value Proposition / Utility The computational grid is the main part of the technological component of the WISDOM service, but is not the main advantage over possible competitors. Any
application providing large amounts of computing capacity is an alternative. Options include renting commercial grids (from HP, or SUN) or supercomputer capacity (IBM). Combining the use of distributed data and knowledge with distributed computing should provide WISDOM with the necessary competitive profile. WISDOM advantages are the extra services that come in addition to the purely technological component. These are the expertise to prepare the targets, manage and analyse the resulting data. An additional, more subtle, advantage is the unique mix of research organisations which provides access to various data and knowledge resources. A business case for commercialisation of the WISDOM service has to be based on benefits with explicit financial implications occurring within a reasonable timeframe. Such benefits include: • Save Time: Reducing time for testing the impact of chemical compounds on a target protein vital to a disease. • Save Resources: Using existing resources instead of carrying significant setup costs for computing power. • Increase Focus: Enabling focused in-vitro testing, with a higher probability of success (rate of active molecules). • Affordable Improvement in Quality: The cost of achieving the same stage of drug discovery with the same rate of active molecules without computing is prohibitive. Revenue Model The service WIDOM offers is difficult to standardise, which means that a fixed price is difficult to calculate. The nature of the service allows, and even forces, individual price setting for each unit. Prices for each run of the service should be set on the basis of the number of targets and components to be tested, the investigated disease, and the cost of acquiring chemical compounds. The price should include a margin for covering the extra costs of maintaining the network of partners, borne by them. Challenges Before WISDOM can be turned into a sustainable service, two strategic challenges need to be met. First, a commitment by all involved, potentially in the form of setting up a legal entity as the service supplier, must be secured. Secondly, the primary target customer segment – public sector versus industry – needs to be chosen and the services set-up – technical as well as organisational – needs to be adjusted accordingly. The service has to allow proprietary outcomes in order to attract financially strong private sector customers, which may prove difficult in connection with the participation of publicly funded organisations (most of the WISDOM partners). WISDOM is in a position where the hardest challenge of using grids in the medical field, managing security and access to sensitive patient data, is not a big concern. At this early stage of drug discovery, no patient data is involved. Also, issues of technical and semantic interoperability have already been successfully addressed. Since the focus of attention is chemical substances, semantic issues are less of a problem. 3.2. MammoGrid The scope of the MammoGrid service is defined as the opportunity to receive real time second opinion via image sharing, to conduct eTraining via real case discussions in a virtual classroom, to receive computer aided support via automatic detection and
highlighted high-risk zones (CADe), to consult forum decision support and multidisciplinary advice, and to conduct statistically significant epidemiology analyses. 3.2.1. Economic Performance The benefits of the service are the increased accuracy of radiological diagnosis and, associated with this, avoided unnecessary biopsies. Measured through the reduction of errors, better services lead to direct gains for citizens, comprising 98% of the total value of positive impacts. Health professionals can use their time more efficiently when requesting a second opinion. The Computer-Aided Detection mechanism helps them to focus on particular features of a mammogram, notably areas of micro-calcifications, which are associated with high risk of breast cancer. Third party payers, which include insurance companies and public health authorities, benefit through the avoided costs of unnecessary biopsies and the avoided life cycle costs of breast cancer treatment. Public health authorities benefit from more targeted screening programmes as the epidemiological feature of Mammogrid delivers more insights into the nature of the disease and allows identifying those most at risk. The costs are composed mainly of the resources used for development, set-up and maintenance of the system. Increased healthcare costs due to early intervention in case of cancer detection are also taken into account. A long period of cumulating costs without benefits comprises the FP5 project’s acquisition starting in 2001, the costs associated with the development of the application, which were highest between 2001 and 2005 (estimated at €2.5 million), and the preparation for market release by Mammogrid+ (budgeted at some €1 million). As soon as Mammogrid goes into routine service in clinical settings in 2010, annual benefits are substantial and immediately outweigh costs. More importantly, as shown on Figure 4, the present value of cumulative benefits is expected to exceed the present value of cumulative costs in 2011, only one year into routine operation. This indicates that the €4 million investment is more than justified by the gains. 27500 25000 22500 20000 € 000s
17500 15000 12500 10000 7500 5000 2500 0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Present value of cumulative costs
Present value of cumulative benefits
Figure 4. Estimated present value of cumulative costs and benefits from MammoGrid.
3.2.2. Business Aspects Maat GKnowledge, a Spanish company specialising in the delivery of technology consulting services, outsourcing and process re-engineering, has taken the initiative to coordinate the development and deployment of Mammogrid as a commercial product. Provision will be in cooperation with other partners from the development phase. Market release is planned for 2010. Here we focus primarily on the business aspects of the MammoGrid service as defined above. However, Maat GKnowledge plans to provide more profitable services to the health IT industry by exploiting the network effects associated with Mammogrid. The MammoGrid service is thus not designed to yield substantial profits itself. Target Customers Customers are divided into two main types – sites and satellites. The difference lies in the hard- and software equipment required, as well as the enabled functionalities. Sites have full read and write access, whereas satellites have read-only rights. Sites include large and specialised public or private hospitals, and university hospitals. Initial satellite targets are independent specialists’ practices, and small clinics. The healthcare IT industry also comprises a customer group. However, the services offered to that group are not the Mammogrid services utilising a HealthGrid. MaatGKnowledge is planning to provide support to application development for them. Value proposition / Utility The value proposition for hospitals is based on the potential for improved efficiency, patient safety, and faster implementation and compliance with evidence based guidelines. The latter may increasingly become necessary as guidelines are converted into regulatory requirements. As a by-product of the digitalisation of radiological images, less staff will be involved in clerical statistical and epidemiological work. Administrative effort for handling film-based mammograms is reduced. More fundamentally, the risk of errors in mammogram analysis is reduced thanks to the CADe and 2nd opinion features of Mammogrid. The benefit for the IT companies lies in market stimulation. Healthcare providers are their direct customers. By offering products certified as interoperable with the Mammogrid network, they increase the added value of their products. Revenue Model Hospitals pay a one-off membership fee of € 20 000 each, which covers the delivered hardware and set-up costs. This contribution cannot, and is not intended to lead to a profit for the Mammogrid service provider. The business model is built on the network effects associated with providing healthcare providers with low-cost access to large amounts of data and disparate expertise, and using the established network for providing profitable services to the IT industry. Operational services to healthcare providers are free of charge, which is important in terms of acceptance. Figure 5 shows a simple profit-and-loss calculation for Maat GKnowledge as provider of MammoGrid, according to three revenue scenarios: one-off membership fees, renewal of equipment by customers, and repeat membership subscription.
Cumulative Profit / Loss; in Euro
100000 80000 60000 40000 20000 0 2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
-20000 -40000 -60000 -80000 PV cumulative adjusted profit Profit with replacement paid by HPOs
Profit with membership renewal
Figure 5. MammoGrid+: Profit and loss excluding replacement expenditure.
The break even point for the commercial version of Mammogrid depends on the modalities of covering obsolescence costs. If the connected hospitals pay a renewable membership fee (e.g. €20.000 every 5 years), the service can operate at a profit very shortly after introduction in 2010. In the scenario where users cover the costs themselves, the service is operating around the break-even point in 2013 only. The respective dates rest on the assumption that the service will start as planned from 2010 onwards with 6 hospitals, and eventually grow to some 15 hospitals and 6 satellites. The estimates include risk and optimism bias. This result is extremely sensitive to the assumptions made and should be treated with the respective caution. Challenges Challenges to the sustainability of the initiative are related to ongoing tests in clinical settings and the resolution of open questions concerning the use of intellectual property developed during the first Mammogrid project phase (2001-2005). The open outcome of the current validation in clinical settings is the main hurdle to sustainable deployment from a business perspective (assuming that the technology is sufficiently developed by late 2009). The whole business case is built on the assumption that end users utilise the service and that the impact on quality, accuracy, and speed of image analysis is positive. The tests in three hospitals in the Extremadura region, Spain, delivering results on this challenge will continue till 2010.
4. Conclusion 4.1. Summary To sum up, the investigation of international HealthGrid projects regarding business models and sustainability uncovers a sceptical short term perspective with reasons for
more optimism in the longer run. It is highly unlikely that sustainable commercial grid applications will enter the health field in the next 5 to 10 years. Even beyond that period, public involvement will be necessary and should be expected. The economic case for HealthGrid is robust, which supports the above claim and is also the basis for optimism. The current process of shaping a legal and regulatory foundation for the use of ICT in different fields of life, including research, health, and healthcare, will lower the hurdles related to uncertainly about data protection and security, IPR, and ethical issues [10,11,12]. Usability and acceptability issues will be more difficult to solve yet are by no means unmanageable. The positive examples presented in this paper show that the most significant hurdle to sustainability – the discrepancy between social benefits and private incentives – can be solved by sound business models. The analysis illustrates a scenario in which there is potential for business cases for commercial provision of HealthGrid services, which is an encouraging result. The fruits of efforts in this direction, however, will take a long time to ripe and deliver the longed-for sustainability of HealthGrids. The two cases, WISDOM and MammGrid, were not chosen at random, but because they were identified as particularly promising. Even so, the business cases have proven to be extremely sensitive to assumptions. Also, it has to be stressed that the investments by the providers do not include the considerable contributions towards development costs financed by the research funds for grids as a promising technology development. Without these funds, the business cases would have been – at best – extremely weak. 4.2. Outlook The topic of HealthGrids should be pursued further because of the substantial potential for net gains to society at large. Today’s achievements in the field may seem modest, yet they are significant given how recent the ideas of using grids in life sciences on a large scale are. These current achievements should be seen as a good foundation upon which to build further activities. The research presented in this paper draws attention to a few crucial lessons learnt. The first is that the technology part, challenging in its own right, is neither the biggest hurdle, nor the biggest advantage of HealthGrids. The organisational issues of collaboration between organisation, countries, and people need to be addressed with all strength in order to ensure a self-sustained HealthGrid services market. On the side of advantages, HealthGrid services need to offer more than simply computational power. Content-specific value added is the only way of distinguishing a HealthGrid service from any other computing power offer on the market. Second, a practical consideration of leadership and commitment should be taken into account. By their nature, HealthGrids involve multiple organisations working together. This may result in lack of a formal entity providing the HealthGrid service to third parties. Such an entity also has the function of ensuring long-term commitment of partners. The establishment of such an entity increases the probability of success, as can be seen in the two examples presented in this paper. Whereas this challenge is one of the main hurdles to WISDOM, MammoGrid has already moved further under the leadership of Maat GKnowledge. Last but not least, as with other new technologies, the initial IT research-oriented project-based activity should slowly be converted into long-term investment in healthcare and medical research. However, this does not mean that Grid research
projects have no place in future developments of sustainable HealthGrids. The examples of infrastructures set up by such projects and utilised in the two presented case studies are a good example. Closing the gap between the social and economic potential for society at large and the business incentives of individuals requires a wise use of public funds in correcting this market failure. There is already a significant amount of experience that can support developments of existing and new HealthGrid activities. These lessons are critical in the effort to bring HealthGrids to a sustainable and self-financed level. In turn, only secured financing and sustainability of HealthGrids can ensure that the full benefit for society materialises, and so the socio-economic potential is indeed realised.
5. Acknowledgements This work has been carried out with funding by TMF e.V. and the valuable support of the German MediGRID project, funded by the German Federal Ministry of Education and Research (BMBF), FKZ 01AK803A. The authors gratefully acknowledge the constructive input of all interview partners.
References [1] MediGrid Project Website: http://www.medigrid.de/index.htm [2] Investigation of HealthGrid projects across the European Union and in the USA regarding business models and sustainability: Part 1 – Market Study, Telematikplattform für Medizinische Forschungsnetze (TMF) e.V., Berlin 2007, http://www.tmf-ev.de/healthgrids/HealthGridMarketPart1.pdf [3] K. A. Stroetmann, A. Dobrev, V. N. Stroetmann, The business case and sustainability of HealthGrid solutions, MediGRID-Workshop at HealthGrid Conference 2008, June 2-4, Chicago IL, USA, http://www.medigrid.de/u_veranst/080602_HealthGrid_2008/244%20Stroetmann%20HealthGrid20080602.pdf [4] WISDOM Initiative Website: http://wisdom.healthgrid.org/index.php?id=64 [5] F. Estrella, R. McClatchey, D. Rogulin, The MammoGrid Virtual Organisation - Federating Distributed Mammograms, http://arxiv.org/ftp/cs/papers/0507/0507042.pdf [6] K. A. Stroetmann, T. Jones, A. Dobrev, V. N. Stroetmann, eHealth is Worth it - The economic benefits of implemented eHealth solutions at ten European sites, European Communities, 2006, 56 pp. http://www.ehealth-impact.org/download/documents/ehealthimpactsept2006.pdf [7] eHealth IMPACT, Final report on method and tools, Online:, http://www.ehealthimpact.org/download/documents/D4_1_Final_report_on_methods_and_tools_ext.pdf [8] S. Scholz, M.H. Breitner and M. Blaurock, A Sustainable Business Model Approach for Grid Computing – And a Life Sciences Example in: Birchler M. et al., Editors, Multikonferenz Wirtschaftsinformatik 2008, GITO-Verlag, Berlin (2008), pp. 293-294. [9] S. Scholz, M.H. Breitner, S.C. Semler and M. Blaurock, Business Models for Grid Computing in Life Science: An Approach to Identifying Health Grid Revenue, Mednet 2007, Leipzig, (2007), Online: http://www.mednet2007.com/pdfs/fullpaper/0708MednetProceedingTMFUniHan.pdf. [10] SHARE the journey – A European Healthgrid Roadmap, European Communities, 2008, 36 pp. http://ec.europa.eu/information_society/activities/health/docs/publications/200810share-roadmap.pdf [11] T. Solomonides et al., The SHARE Road Map: Healthgrids for Biomedical Research and Healthcare, in Studies in Health Technology and Informatics, Volume 138, Global Healthgrid: e-Science Meets Biomedical Informatics - Proceedings of HealthGrid 2008, T. Solomonides et al. Editors, IOS Press (2008), pp. 238-278 [12] eHealth priorities and strategies in European countries, European Communities, 2007, 98 pp. http://www.ehealth-era.org/documents/2007ehealth-era-countries.pdf
