SUPPORTING POLICY DEFINITION
IN THE E-HEALTH DOMAIN
A QCA based Method
Paolo Spagnoletti, Valentina Albano, Attilio Caccetta, Rocco Tarquini and Alessandro D’Atri
CeRSI-LUISS, Guido Carli University, via Alberoni 7, Rome, Italy
Keywords: eHealth policy, Qualitative comparative analysis, Set-theoretic method.
Abstract: eHealth is broadly considered as a promising strategy to improve the economic sustainability and quality of
the healthcare service provision in Europe. Nevertheless, despite the enthusiastic declarations of eHealth
potential, the adoption of IT in health care has progressed very slowly. A critical factor, not deeply
addressed in literature, is related to the process of prioritization of the eHealth solution to adopt, in presence
of financial constrains, external and internal pressure from a wide range of heterogeneous stakeholders, and
conflicting information on different technological solutions. In this paper we introduce a method supporting
policy definition in the eHealth domain. This method is based on a qualitative comparative analysis (QCA)
of best practices and previous experiences performed through the lens of an analytic framework whose
dimensions and categories are well situated in the eHealth context. This method could support policy-
makers in the identification of the properties and characteristics of innovative projects at European level and
to analyze the gap between the international scenario and the local context in order to understand trends and
dynamics of development, to evaluate the best opportunities for innovation and, therefore, to assign
priorities for the next investments by respecting the constraints of available resources.
1 INTRODUCTION
The European Commission with the eHealth Action
Plan (EC, 2004) and the recent Digital Agenda for
Europe (EC, 2010), has recognized to eHealth a
pivotal role with respect to the present and future
socio-economic and financial challenges faced by
national healthcare authorities in Europe.
Recent studies show that healthcare systems need
first to deal with the population ageing, having a
direct impact on changing disease composition, due
to the rise of chronic diseases and to the increasing
demand for health and social services (Pomerleau et
al., 2008). At the same time, there is a continual
growth of citizens/patients’ expectations, regarding
access to better information, better expertise, better
quality of medical services, latest treatments, safer
care and support in long term care and independent
living, as well as support in their lifestyle
management (EC, 2007). These challenges together
with others, such as the cross-border healthcare (for
the growing patient mobility), are leading to a
substantial increase in healthcare expenditure
(RAND and Capgemini, 2010). In this scenario,
exacerbated by the increasing financial constraints
incumbent upon healthcare providers (EC, 2004),
the exploitation of the enormous potential of eHealth
services and solutions becomes necessary to
improve overall healthcare delivery.
Mitchell (2000), refers to eHealth as an umbrella
term, describing the combined use of electronic
communication and information technology in the
health sector, and also the use of digital data -
transmitted, stored and retrieved electronically - for
clinical, educational and administrative purposes,
both at the local site and at distance. It is widely
believed that e-Health can address many of the
problems currently faced by the health care systems,
improving quality of care, increasing efficiency of
healthcare work, assuring healthcare services more
accessible and better effectiveness of medical
interventions and patient care (Stroetmann et al.,
2006). Nevertheless, despite the enthusiastic
declarations of eHealth potential, the adoption of IT
has been much slower in health care than it has been
in other industries such as banking and
manufacturing (Simon et al., 2007; Bates, 2005).
Cost is often cited as the primary reason of the slow
343
Spagnoletti P., Albano V., Caccetta A., Tarquini R. and D’Atri A..
SUPPORTING POLICY DEFINITION IN THE E-HEALTH DOMAIN - A QCA based Method.
DOI: 10.5220/0003173103430350
In Proceedings of the International Conference on Health Informatics (HEALTHINF-2011), pages 343-350
ISBN: 978-989-8425-34-8
Copyright
c
 2011 SCITEPRESS (Science and Technology Publications, Lda.)

rate of eHealth adoption, followed by the lack of
methods for evaluate the effective benefits provided
to the stakeholders (cost saving, improved patient
satisfaction, operating efficiencies, quality of care,
and patient safety), and privacy and security
concerns (Dixon, 2007).
Another critical factor, not deeply addressed in
literature, is related to the process of prioritization of
the eHealth solution to adopt, in presence of
financial constrains. Faced with continuous streams
of new technological solutions put forth by
stakeholders, decision makers are faced with many
external and internal challenges (i.e. external
pressure from patients requiring more transparency,
internal pressure on the decision process by
physicians and healthcare managers, and conflicting
information on different technological solutions).
This raises the need for the development of decision-
making methods and tools supporting policy makers
dealing with these issues.
In this paper we introduce a method supporting
policy definition in the eHealth domain. This
method is based on the comparative analysis of best
practices and previous experiences performed
through the lens of an analytic framwork whose
dimensions and categories are well situated in the
eHealth context. The contribution is organized as
follows: in the next paraghaph we provide an
overview of the main factors of complexity
influencing the definition process of eHealth
policies. Then in section 3 a new method supporting
the decision makers will be presented. In section 4
we provide an example of application of the method,
based on data collected from an EU eHealth project
database. Suggestions for future research conclude
the paper in section 5.
2 DECISION MAKING
IN THE E-HEALTH DOMAIN
Making investment decisions in the eHealth domain
is a critical task.
First, eHealth is an interdisciplinary area. It
needs efforts and contributions from areas of trust,
ethical, juridical, economic, political, informatics,
and methodologies. As a matter of example,
Personal Data Protection legislation and standards
pose some issues when applied to cross-regional
interoperability. The successful implementation of
eHealth project cannot be achieved without joint
efforts from several disciplines (IANIS, 2007).
Second, eHealth is now on the governmental
agenda of all EU Members States (EC, 2009). This
high attention on eHealth investments has created a
strong European eHealth market with a very wide
range of applications for all needs (Gartner, 2008). It
is expected to reach more than EUR 15 million by
2012, with a compounded annual growth rate of
2.9% (RAND and Capgemini, 2010).
Third, the essence of eHealth is that it should
facilitate the transforming of healthcare processes
for the benefit of patients and the healthcare system
(IANIS, 2007). To realize this essence, the decision
maker is surrounded by a wide variety of solutions
that can support all types of health services: health
promotion, diagnosis, therapy, rehabilitation or long-
term care. eHealth can also underpin support
activities like management and administration,
logistics and supply of health-related goods,
facilities management as well as public health,
continued medical education, or medical research
and clinical trials (EC, 2009). The choice among all
these ways to improve and change healthcare,
depends on the main priorities that have been
identified. Both the potential benefits and the needs
to be met are many and often eHealth solutions
influence a number of these simultaneously
(Stroetmann et al., 2006). The priority, for instance,
may be to meet the needs of patients/citizens
focusing on objectives such as equal access,
timeliness of care, safety, quality information, cross-
border healthcare, effectiveness of care,
empowerment, etc. As an alternative, is it possible to
assign priority in supporting the operational
processes of healthcare professionals by focusing on
objectives such as data sharing among healthcare
organisations, cost-cutting strategies, selecting
necessary services, addressing the shortages in
qualified staff, etc.
Finally, in Europe healthcare is either a national
or a regional responsibility. In the same way, the use
of eHealth applications differs from nation to nation
and from region to region (IANIS, 2007). In this
context, characterized by different factors, it is
difficult to identify best practices which are
universally applicable, but only good practices that
can be a success under different circumstances (EC,
2009). Furthermore, decision makers could take
advantage from the availability of methods for
selecting optimal eHealth applications with respect
to expected benefits and risks (Rigby, 2006).
In order to deal with these challenges a method
to support decision makers in making their choices
about eHealth investments by taking into account the
above mentioned priorities, benefits, problems and
potentials, is under development. This method is
based on the qualitative comparative analysis (QCA)
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of a set of eHealth projects both internationally and
locally.
The method has practical implications for
eHealth decision makers, by supporting them in the
identification of the properties and characteristics of
innovative projects at European level and to analyze
the gap between the international scenario and the
local context. In this way it is possible to understand
trends and dynamics of development, to evaluate the
best opportunities for innovation and, therefore, to
assign priorities for the next investments by
respecting the constraints of available resources.
3 THE POLICY DEFINITION
METHOD
The method we propose can support the policy
definition process through an analysis of previous
eHealth experiences. It is based on the application of
a data analytic strategy known as qualitative
comparative analysis, or QCA. It refers to the
analysis of dichotomous social data reflecting the
memberships of cases in conventional, crisp sets. In-
depth discussions of this method can be found in
Ragin (1987, 2000). In order to perform the above
mentioned comparative analysis among eHealth
initiatives, we base our work on the application of
set-theoretic methods for studying cases as
configurations. According with Ragin (1987, 2006),
set-theoretic methods differ from conventional,
variable-based approaches in that they do not
disaggregate cases into independent, analytically
separate aspects but, instead, treat configurations as
different types of cases. To examine these different
configurations of attributes, set-theoretic methods
use Boolean algebra, a notational system that
permits the algebraic manipulation of logical
statements (Fiss 2007). This allows simplifying the
complexity of causal relationships by reducing them
to primitive expressions and formulating more
succinct Boolean statements. Moreover, whenever
both the number of categories and the number of
cases are small, is it useful to display graphically a
Boolean data set through Venn diagrams.
Such an approach in many ways offers a better
fit with a configurational understanding of eHealth
initiatives and also allows for a sophisticated
assessment of how different causes combine to
affect relevant outcomes such as for instance project
success.
According with the objectives of this work, we
propose the use of set-theoretic methods to examine
how different elements characterizing eHealth
projects, combine rather than compete to produce an
outcome. From the perspective of decision makers,
this approach contributes to move beyond simple
contingency approaches where either environmental
or technology related aspects are considered as a
source for making decisions. In fact, also in the
eHealth domain, most organisations face multiple
contingencies, such as previously adopted strategies
and structures, activities, and technologies, with
significant interdependencies among these
contingencies (Galunic & Eisenhardt, 1994).
Furthermore, these multiple contingencies may
present contradictory requirements for strategy and
structure (Miller 1992, Fiss 2007).
Moreover, set-theoretic methods allow
performing a qualitative comparative analysis when
the number of cases is too small for many
conventional statistical analyses such as between ten
and fifty cases (Fiss 2007). Thus, it results
appropriate for comparing eHealth implementation
projects at a local level, such as for instance within a
regional area or within a single country.
The first step of our QCA based method
consisted in identifying the unit of analysis and then
defining the analytical framework based on
dimensions and categories (sets) through which
cases will be classified. This analytical framework
has been defined through a conceptual analysis
performed by researchers and domain experts. The
next section presents the output of this activity.
3.1 The Analytical Framework
The unit of analysis we considered in our method
corresponds to a single eHealth project implemented
in a given area. With respect to the QCA method,
each project represents a case and a set of classes
have been defined in order to allow the researchers
to classify cases. A first distinction provided by the
taxonomy is related to the “target patients” to be
addressed and the “organizational choices” faced by
the decision maker. With respect to the
“organizational choice” class, the taxonomy
provides 34 categories grouped along 5 main
dimensions (Table 1).
The first dimension is related to the relationship
supported by the ICT system. The categories
associated with this dimensions correspond to all the
possible pairs (including reflective pairs) of subjects
that have been identified: Patient/Citizen;
Professional, Administrator, Manager).
The second dimension is related to the phase of
assistance socio-medical process where the ICT
SUPPORTING POLICY DEFINITION IN THE E-HEALTH DOMAIN - A QCA based Method
345

system operates: Prevention, Access, Treatment,
Monitoring and control.
The third dimension is the type of ICT system
implemented. Categories proposed in (EC, 2007, p.
10) have been adopted:
 Clinical Information Systems: a) Specialised
tools for health professionals within care institutions
(e.g. Radiology or Nursing Information Systems) b)
Tools for primary care and/or for outside the care
institutions (e.g. General Practitioner or Pharmacy
Information Systems).
 Secondary Usage Non-Clinical Systems: a)
systems for health education and health promotion
of patients/citizens (e.g. health portals, online health
information services); b) specialised systems for
researchers and public health data collection and
analysis (e.g. bio statistical programs for infectious
diseases, drug development); c) support systems
such as supply chain management, billing systems,
administrative and management systems.
 Telemedicine: personalised health systems and
services (e.g. remote patient monitoring, tele-
consultation).
 Integrated Health Clinical Information Network:
distributed electronic health record systems and
associated services such as e-prescriptions or e-
referrals.
The fourth dimension is the level of supported
cooperation. The categories associated with this
dimensions are:
 Intra-organizational: automation of a single
activity or integration of activities within the
healthcare process.
 Inter-organizational: collaboration between
multiple healthcare providers (network); between
different types of public and private institutions
(Public Private Networks); exchange of healthcare
clinical data between patients and physicians, nurses
and other specialists (clinical based patient’s
participation); exchange of non-clinical information
such as quality of services, good practices, etc.
within a particular community of patients/citizens
(Info based patient’s participation).
With respect to the “target patient” class, the
categories are listed in Table 2 under three
dimensions: Risk categories, Chronic patients,
Others.
Table 1: The “organizational choices” taxonomy.
Organizational choices
Dimensions Categories
Relationship supported
Patient/C.-Patient/Citizen
HC Professional-HC Prof.
Administrator-Admin
Manager-Manager
Patient/C.-Professional
Patient/C.-Administrator
Professional-Administrator
Professional-Manager
Manager-Administrator
Phase of
socio-
medical
process
Prevention
Sensitisation campaigns
Management vaccines
Screening
Access
Emergency
Specialist visits
Hospitalisation
Drugs-prosthesis
Social services
Home care
Treatment
Diagnosis
Therapy
Socio-medical assessment
Assistance intervention
Monitoring
and control
Clinical monitoring data
Administrative monitoring
data
Type of ICT system
Clinical IS
Secondary Usage
Non-Clinical Systems
Telemedicine
Integrated Health Clinical
Information Network
Level of
supported
cooperation
Intra-org.
Automation
Integration
Inter-org.
Network
Public Private Networks
Clinical based patient’s
participation
Info based patient’s
participation
Table 2: The “target patients” taxonomy.
Target patients
Dimensions Categories
Risk categories
Elderly
Maternity
Drug addiction
Mental diseases
Chronic patients
Amyotrophic lateral
sclerosis
Diabetes
Alzheimer
Tumours
Others
Acute cases
Others
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3.2 The Comparative Method
Once the final objective of the analysis has been
defined, researchers will select within the above
mentioned taxonomy one outcome variable and a
subset of categories to be used as contingency
factors of the analysis. Furthermore, a selection of
cases (eHealth experiences) which are relevant with
respect to investigation goals will be identified and
analyzed. This is done by interpreting case data and
by filling with 1 and 0 values a matrix where rows
correspond to cases and columns correspond to the
contingency factors and to the outcome. Such values
represent the membership or the non membership of
each case to the corresponding category
respectively.
The truth table obtained through this process,
will be further analyzed in order to identify
configurations of contingency factors affecting the
outcome. This can be done with the support of
software packages implementing one of the possible
algorithms for the analysis of crisp data sets.
For the purpose of this paper we propose to
adopt a software tool for small number analysis
which allows the graphical representation of cases
on a Venn diagram with up to five independent
conditions (Cronqvist 2005).
Our assumption is that by graphically positioning
“good practice” cases on a map with possible
configurations of contingency factors, the decision
maker can better identify which policy can lead to
the desired outcome.
4 EXAMPLE OF APPLICATION
In this section we aim to provide a brief example of
application for the above mentioned method, based
on the comparative analysis of a set of European
initiatives considered “good practices” in eHealth. In
order to collect information on the characteristics of
these good practices, we refer to a public available
online database created in the context of an initiative
of the European Commission (Good eHealth, EC
2008). The Good eHealth initiative is a three-year
study (from 2006 to 2008) which has been financed
under the former Modinis programme in the
Directorate-General of Information Society and
Media. The objectives of this study are to i) identify
good practices and their associated benefits; ii)
develop and implement proven approaches to wider
dissemination and transfer real-life experiences; iii)
stimulate and foster accelerated take-up of e-Health
by addressing the common challenges of e-Health
and lessons learned. These objectives are in line with
the purposes of our example which aims to show
how a decision maker can be supported in defining
its eHealth policy.
Among the 132 solutions which were listed in
the database at the time of data collection, 94 cases
were certified as “quality reviewed cases”. The
project website describes in detail the selection
process through which cases have been analyzed by
the expert panel.
A common template is used for presenting cases
on the website in order to provide a minimum
amount of information for each good practice. The
average size of the overall case description is about
2000 words.
For the purposes of this paper, a team of domain
experts - researchers and consultants – have
classified the 94 “quality reviewed cases” using the
categories of the above mentioned taxonomy. The
outcome of this phase represents the crisp data set
that will be further processed.
Moreover, the researchers have collected in a
separate table some general information about the
projects such as the name, the starting date, the
country, some comments, and references to further
documentation. These additional data are useful to
support the selection process of cases by reducing
the data set to a small number of cases.
Let now suppose that a decision maker wants to
understand how to deal with chronic diseases (such
as diabetes, cancer, Alzaimer or cardiovascular
disease). These pathologies represent the most
common cause of mortality or disability throughout
the world (WHO, 2005) and are responsible for
almost the 70% of healthcare expenditures (Mongan
et al., 2008). In this scenario a comprehensive and
integrated action for chronic care management has
been defined as “vital investments” (WHO 2005).
Among the policy definition issues in the domain of
chronic care management, a decision maker must
choose in which type of ICT systems to invest and
which should be the boundaries of the cooperative
environment (e.g. medical department, hospital,
territorial Healthcare network…). In fact, assuming
that an effective management of chronic conditions
requires a coordinated and proactive organization of
care involving a multiplicity and variety of players
and both clinical and administrative acts, a possible
investigation can be related to the type of ICT
systems implemented (widely recognized as critical
coordination mechanisms) and to the actual level of
integration supported (intra-organization vs inter-
organization) expressed in the proposed taxonomy
under the “level of supported cooperation” dimen-
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347

sion.
With this aim, by selecting projects with CHR=1
(Chronic patients) from the crisp data set, we obtain
a subset of 23 cases. In fact, since we are analyzing
good practice cases, it is possible to assume that
whenever the CHR value is 1, the project has
effectively achieved the goal of addressing the needs
of chronic diseases.
As explained before, a possible set of elements
influencing the capability to address chronic
diseases needs are the inter-organizational character
of the initiative and the type of ICT systems
implemented. The corresponding sets which have
been considered in this example are: INT (inter-
organizational), CIS (Clinical IS), SUNCS
(Secondary Usage Non-Clinical Systems), TLM
(Telemedicine), and IHCIN (Integrated Health
Clinical Information Network). These sets can be
considered as contingency factors having an impact
on the CHR outcome.
By using the Quine algorithm on the Tosmana
1.3.1 software package, is it possible to calculate the
truth table (Table 3) and to graphically visualize the
distribution of cases on a Venn diagram (Figure 1).
Each area in the diagram represents a possible
combination of the selected contingency factors. For
instance, area 11111 refers to inter-organizational
projects (INT=1) in which all the four categories of
ICT systems are implemented (CIS=1, SUNCS=1,
TLM=1, IHCIN=1). In our example, case 83 fits
with these characteristics and can be deeply
analyzed in order to increase the knowledge about
hints and issues for these types of projects. It
corresponds to the “DITIS: Network for Home
HealthCare Collaboration” project, developed in
Cyprus between 1999 and 2003 when routine
operations started.
According with the project description, “the
main purpose of DITIS is to provide continuity of
care by supporting the operation of virtual
collaborative healthcare teams that care for a single
patient at home but do neither normally nor easily
come together. Its objectives include: immediate and
effective treatment of symptoms based on informed
decisions possible through the instant access to the
EHR by other care professionals; improved cost
effectiveness through effective communication and
coordination of healthcare teams and reduction of
bureaucratic overhead; improved quality of life for
chronic patients and their families.”
Figure 1: Venn diagram.
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Table 3: Truth table.
CASE ID
INT CIS SUNCS TLM
IHCIN
CASE 1,CASE 59,CASE 64,CASE 98 0 1 1 0 0
CASE 146,CASE 91 1 1 0 1 1
CASE 69,CASE 46,CASE 47 1 0 1 1 0
CASE 5,CASE 50,CASE 139 1 1 0 1 0
CASE 20,CASE 142,CASE 36 1 0 1 0 1
CASE 68 0 0 1 0 0
CASE 83 1 1 1 1 1
CASE 35 1 0 0 1 0
CASE 41 1 1 1 0 1
CASE 140 0 0 0 1 0
Furthermore, the following Boolean statement
represents the minimization of the previous cases
through which it is possible to further analyze by the
means of multiple-case studies cases represented by
each factor:
INT{1}*SUNCS{0}*TLM{1}+
CIS{0}*TLM{1}*IHCIN{0}+
INT{0}*SUNCS{1}*TLM{0}*IHCIN{0}+
INT{1}*CIS{1}*TLM{1}*IHCIN{1}+
CIS{1}*SUNCS{0}*TLM{1}*IHCIN{1}+
INT{1}*SUNCS{1}*TLM{0}*IHCIN{1}INT{
1}*SUNCS{0}*TLM{1}+
CIS{0}*TLM{1}*IHCIN{0}+
INT{0}*SUNCS{1}*TLM{0}*IHCIN{0}+
CIS{1}*SUNCS{0}*TLM{1}*IHCIN{1}+
INT{1}*SUNCS{1}*TLM{0}*IHCIN{1}+
INT{1}*CIS{1}*SUNCS{1}*IHCIN{1}
5 CONCLUSIONS
In this paper we have introduced a method
supporting policy definition in the eHealth domain.
We have also provided an example of application of
this method based on empirical data collected from
EU sources. The example has shown the potential of
the method in supporting a decision maker willing to
understand whether to invest in inter or intra-
organizational projects and which combinations of
ICT systems can be effective.
The following steps of the research will consist
in the validation of the method involving e-health
policy decision makers and in developing new
version of the method based on fuzzy logic analysis
techniques.
REFERENCES
Bates, D. W. 2005. “Physicians and Ambulatory
Electronic Health Records”. Health Affairs
Cronqvist, L. 2005. Tosmana – Tool for small-n analysis.
Version 1.201. Marburg:University of Marburg.
http://www.tosmana.net/ [cited 2010-10-18].
Dixon B. E. 2007. A roadmap for the adaptation of
eHealth. E-Service Journal.
European Commission, 2004. eHealth - making healthcare
better for European citizens: An action plan for a
European eHealth Area. Brussels, 30 April 2004.
European Commission, 2007. eHealth Taskforce report
2007-Accelerating the development of the ehealth
market in Europe, Luxembourg: Office for Official
Publications of the European Communities.
European Commission, 2008 Information Society and
Media Directorate-Genaral. Expert Impact Assessment
http://kb.good-ehealth.org/search.do.
European Commission, 2009. Good eHealth Report-
eHealth in Action Good Practice in European
Countries, Luxembourg: Office for Official
Publications of the European Communities.
European Commission, COM, 2010. 245 final. A Digital
Agenda for Europe, Brussels, 19 May 2010.
Galunic, D. C., and Eisenhardt, K. M. 1994. Reviewing the
strategy-structure-performance paradigm. Research in
Organizational Behavior, 16: 215–255.
Gartner, 2008. Hype Cycle for Healthcare Provider
Applications and Systems, Stamford.
IANS, 2007. Guide to Regional Good Practice eHealth,
Brussels, eris@-The European Regional Information
Society Association.
Miller, D. 1992. Environmental fit versus internal fit.
Organization Science, 3: 159–178.
Mitchell J., 2000. Increasing the cost-effectiveness of
telemedicine by embracing e-health. J Telemed
Telecare
Mongan, J. J. Ferris, T. G. Lee, T. H. 2008. Options for
Slowing the Growth of Health Care Costs. The New
England Journal of Medicine, 358(14):1509-1514
Pomerleau, J., Knai C. and Nolte E., 2008. The burden of
chronic disease in Europe in Nolte, E. and McKee,
SUPPORTING POLICY DEFINITION IN THE E-HEALTH DOMAIN - A QCA based Method
349

M., 2008, Caring for people with chronic diseases: a
health system perspective, Maidenhead, Open
University Press, pp. 15–43.
Ragin, C. C. 1987. The comparative method: Moving
beyond qualitative and quantitative strategies.
Berkeley: University of California Press.
Ragin, C. C. 2006. Set relations in social research:
Evaluating their consistency and courage. Political
Analysis, 14: 291–310.
RAND Europe and Capgemini Consulting, 2010. Business
Models for eHealth, Cambridge: final report prepared
for ICT for the Health Unit, DG Information Society
and Media European Commission.
Rigby M., 2006. Essential prerequisites to the safe and
effective widespread roll-out of e-working in
healthcare. International Journal of Medical
Informatics 75(2): 138-147.
Simon, S.R., Kaushal, R., Cleary, P. D., Jenter, C. A.,
Volk, L. A., Poon, E. G., et al. 2007 . “Correlates of
Electronic Health Record Adoption in Office
Practices: A Statewide Survey” Journal of the
American Medical Informatics Association
Stroetmann, K. A., Jones, T. Dobrev, A. and Veli, N. O.,
2006. eHealth is worth it: the economic benefits of
implemented eHealth solutions at ten European sites,
Luxembourg: Office for Official Publications of the
European Communities, European Commission.
World Health Organization, 2005, Preventing Chronic
Disease: a vital investment. WHO Press, Geneva.
HEALTHINF 2011 - International Conference on Health Informatics
350