Even though data integration has been there for long,
integrating e-health data remains an open research challenge.
Many studies have discussed data integration approaches in
isolation, causing a disjoint of information flows and narrowing
the understanding of alternative solutions in the cause of
choosing an appropriate approach for integrating e-Health data.
This problem can be solved by undertaking a comprehensive
review on different approaches and techniques in data integration
as well as paradigm in e-Health frameworks. The main
contribution of this paper is a narrative description of most
widely used integration architectures as well as comparative
analysis of each of them. The review reveals that despite the
business needs of integration approach, each has limiting factors
based on the data size, unstructured or structured nature of data
set as well as business agility of the organization. Moreover, it is
found that some technologies were not designed for data
integration but for document management or information
integration. It is concluded that data integration approaches and
the field in which it applies mostly, is widely spread that always
results in integration problem. Also, it is noted that designing a
framework for integrating e-Health data using web service
approach in a distributed network characterized with short lived
connection is still a challenge.
Vitalis Ndume : School of Computational, Communication Science and Engineering,
Nelson Mandela-African Institute of Science and Technology, Arusha, Tanzania
Yaw Nkansah-Gyekye : School of Computational, Communication Science and Engineering,
Nelson Mandela-African Institute of Science and Technology, Arusha, Tanzania
Jesuk Ko : Department of Healthcare Management, Gwangju University
Gwangju, Korea
Heterogeneous data
distributed architecture
integration techniques
e-Health paradigm
It is concluded that data integration is a primary concern
with combining data residing at different sources, and
providing users with a unified view of the data. The
integration may also be concerned with managing the
relationship of data from different independent systems.
The degree to which the data are coupled may vary
depending on the organization business needs. On one
side, a need for tightly integrated solution is required while
on the other side loosely integrated data model is preferred
depending on the organization agility. Therefore, a
practitioner should consider the need for better
conceptualization and method for implementing partial
integration of their data between and within the
organization. This can be achieved by using an
architecture which is loosely integrated while enforcing
standardization. Additionally, choosing the right approach
for data integration requires a practitioner to consider the
IT infrastructure, better conceptualization and method for
implementing partial integration of their data.
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