Authors:
Nekane Larburu
1
;
Naiara Muro
1
;
Iván Macía
1
;
Eider Sánchez
2
;
Hui Wang
3
;
John Winder
3
;
Jacques Boaud
4
and
Brigitte Séroussi
5
Affiliations:
1
Vicomtech-IK4 and Biodonostia, Spain
;
2
NARU, Spain
;
3
Ulster University, United Kingdom
;
4
AP-HP, DRCD, Sorbonne Universités, UPMC Univ. Paris 06, INSERM and Université Paris 13, France
;
5
Sorbonne Universités, UPMC Univ. Paris 06, INSERM, Université Paris 13, AP-HP, Hôpital Tenon and DSP, France
Keyword(s):
Evidence Based Medicine, Breast Cancer, Computer Interpretable Clinical Guidelines, CDSS.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Management
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Practice-based Research Methods for Healthcare IT
;
Society, e-Business and e-Government
;
Symbolic Systems
;
Web Information Systems and Technologies
Abstract:
Over the past years, clinical guidelines have increasingly become part of the clinical daily practice in order to
provide best available Evidence-Based-Medicine services. Hence, their formalization as computer
interpretable guidelines (CIG) and their implementation in clinical decision support systems (CDSSs) are
emerging to support clinicians in their decision making process and potentially improve medical outcomes.
However, guideline compliancy in the clinical daily practice is still “low”. Some of the reasons for such low
compliance rate are (i) lack of a complete guideline to cover special clinical cases (e.g. oncogeriatric cases),
(ii) absence of parameters that current guidelines do not consider (e.g. lifestyle) and (iii) absence of up-to-date
guidelines due to lengthy validation procedures. In this paper we present a novel method to build a CDSS
that, besides integrating CIGs, stores experts’ knowledge to enrich the CDSS and provide best support to
clinicians. The knowledge in
cludes new evidence collected over time by the systematic usage of CDSSs.
(More)