Yearb Med Inform 2017; 26(01): 133-138
DOI: 10.15265/IY-2017-031
Section 5: Decision Support
Georg Thieme Verlag KG Stuttgart

Contributions from the 2016 Literature on Clinical Decision Support

V. Koutkias
1  Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thermi, Thessaloniki, Greece
J. Bouaud
2  AP-HP, Department of Clinical Research and Innovation, Paris, France
3  INSERM, Sorbonne Université, UPMC Univ Paris 06, Université Paris 13, Sorbonne Paris Cité, UMRS 1142, LIMICS, Paris, France
Section Editors of the IMIA Yearbook Section on Decision Support› Author Affiliations
Further Information

Publication History

Publication Date:
11 September 2017 (online)



Objectives: To summarize recent research and select the best papers published in 2016 in the field of computerized clinical decision support for the Decision Support section of the IMIA yearbook.

Methods: A literature review was performed by searching two bibliographic databases for papers related to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved papers that were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and section editor evaluation.

Results: Among the 1,145 retrieved papers, the entire review process resulted in the selection of four best papers. The first paper describes machine learning models used to predict breast cancer multidisciplinary team decisions and compares them with two predictors based on guideline knowledge. The second paper introduces a linked-data approach for publication, discovery, and interoperability of CDSSs. The third paper assessed the variation in high-priority drug-drug interaction (DDI) alerts across 14 Electronic Health Record systems, operating in different institutions in the US. The fourth paper proposes a generic framework for modeling multiple concurrent guidelines and detecting their recommendation interactions using semantic web technologies.

Conclusions: The process of identifying and selecting best papers in the domain of CDSSs demonstrated that the research in this field is very active concerning diverse dimensions, such as the types of CDSSs, e.g. guideline-based, machine-learning-based, knowledge-fusion-based, etc., and addresses challenging areas, such as the concurrent application of multiple guidelines for comorbid patients, the resolution of interoperability issues, and the evaluation of CDSSs. Nevertheless, this process also showed that CDSSs are not yet fully part of the digitalized healthcare ecosystem. Many challenges remain to be faced with regard to the evidence of their output, the dissemination of their technologies, as well as their adoption for better and safer healthcare delivery.