Telemedicine has not advanced at the same pace as IT and its own medical technologies. The long-awaited progress has
been hindered aggressively by security risks that came with innovative information and communication technologies. One major
technological factor to blame for this tardiness in telemedicine is its information security that lead to patients and doctors attrition and
hence system infeasibility. Given the great deal of uncertainties and ambiguities in the telemedicine environment, Bayesian reasoning
does not offer a sound approach to tackle all the security problems menacing telemedicine. For this article proposes an evidential
reasoning model to manage risks due to security uncertainties and ambiguities characterizing most telemedicine environments.
Dempster and Shafer Theory is used to process security management evidence for the purpose to forecast the overall security risks
associated with the continual feasibility of a telemedicine system. This article also provides a numerical example to demonstrate the
working of the proposed evidential reasoning model.
Published In:IJCSN Journal Volume 6, Issue 5
Date of Publication : October2017
Pages : 559-564
Tables : 02
Sofienne Mansouri : U of Tunis El-Manar, ISTMT, Lab of Biophysics and Medical Technologies, Tunisia.
Bel G Raggad : Seidenberg School of CS & IS, Pace U, New York, USA.
The article looked into the telemedicine tardiness in
following current advances in information and
telecommunication technologies and proposed an
evidential reasoning model to tackle its security
problems believed to be one of the major hinders of
telemedicine progress. Given the great deal of
uncertainties and ambiguities in the telemedicine
environment, Bayesian reasoning does not offer a sound
approach to tackle all those security problems menacing
telemedicine. We proposed an evidential reasoning
model to manage risks due to security uncertainties and
ambiguities characterizing most telemedicine
 Blavin, F., et al., Final Report: Lessons from the
Literature on Electronic Health Record
Implementation, U.S. Department of Health and
Human Services, http://www.healthit.gov/,
 Demiris G, and D. Tao, An analysis of the
specialized literature in the field of
telemedicine. Journal of telemedicine and
telecare. 11(6): 316-327, (2010).
 De Moor, G. et al., Using electronic health
records for clinical research: The case of the
EHR4CR project, Journal of Biomedical
Informatics, Vol 53, pp 162-173, (2015).
 Doherty, N., & H. Fulford, Aligning the
information security policy with the strategic
information a systems plan. Computers &
Security, 25, pp 55-63, (2006).
 Duan, L., Street W.N., and Xu, E., Healthcare
information systems: data mining methods in
the creation of a clinical recommender system,
Enterprise Information Systems, 5(2), 169-181,
 El Fadlya, A.et al., Electronic Healthcare
Record and Clinical Research in Cardiovascular
Radiology: HL7 CDA and CDISC ODM
Interoperability, AMIA 2007 Symposium
Proceedings, pp 216-220, (2007).
 John Craig, J., and V. Patterson, Introduction to
the practice of telemedicine, Journal of
Telemedicine and Telecare, 11(1), pp 1-9,
 Kiernan, TE. J. and Demaerschalk, B.M.,
Nursing Roles within a stroke Telemedicine
network, Journal of Central Nervous System
Disease, 2, 1–7, (2010).
 Paul K.J. Han, KJP, Klein, WMP, and Arora,
NK, Varieties of uncertainty in health care: a
conceptual taxonomy, Medical Decision
Making, 31(6), pp 828–838. (2011).
 Stoten, S., 2009. Health Policy Issue with the
Electronic Health Record, Online Journal of
Nursing Informatics (OJNI), 13(2), 1-14,
 Talmon J. AE., et al., STARE-HI: statement on
reporting of evaluation studies in health
informatics. International Journal of Medical
Informatics, 78(1), pp 1-9, (2009).
 Xu, E., Wermus, M., and D.B. Bauman,
Development of an integrated medical supply
information system, Enterprise Information
Systems, 5(3), pp 385-399, (2011).
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