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A Clinical Decision Support System for Chronic Venous Insufficiency

[+] Author Affiliations
Chiara Calamanti, Annalisa Cenci, Michele Bernardini, Emanuele Frontoni, Primo Zingaretti

Universita Politecnica delle Marche, Ancona, Italy

Paper No. DETC2017-68016, pp. V009T07A004; 8 pages
  • ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 9: 13th ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications
  • Cleveland, Ohio, USA, August 6–9, 2017
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5823-3
  • Copyright © 2017 by ASME


Earlier diagnosis plays a pivotal role in clinical applications, since it can strongly reduce the incidence and impact of many diseases and, consequently, the reduction of health care costs. This last aspect depends strongly from right therapy prescriptions, especially when there are various opportunities. Within this context, Clinical Decision Support Systems (CDSS) could bring several benefits. In this paper, we propose a CDSS with the aim of improving the clinician practice based on recommendations, assessment of the patient and screening of patients with risk factors to prevent chronic venous insufficiency (CVI) complications. The proposed CDSS is implemented in the Nu.Sa. cloud system, which involves thousands of italian General Practitioners (GPs) collecting data (EHR data, personal data, patient’s medical history) from millions of patients. The proposed architecture is designed to collect data from a distributed scenario where GPs are collecting clinical history and pharmacy or second level hospitals gather data from medical devices connected to the cloud over a standard data architecture. We show that exploiting the integration of the medical device VenoScreen Plus with the patient EHR, this CDSS is capable to improve preventive care, to enhance clinical performance, to influence clinical decision making and to significantly improve the decision quality levering on data driven approach.

Copyright © 2017 by ASME



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