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Yearb Med Inform. 2018 Aug;27(1):122-128. doi: 10.1055/s-0038-1641222. Epub 2018 Aug 29.

Contributions from the 2017 Literature on Clinical Decision Support.

Author information

1
Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thermi, Thessaloniki, Greece.
2
Assistance Publique-Hôpitaux de Paris, Delegation for Clinical Research and Innovation, Paris, France.
3
Sorbonne Université, Université Paris 13, Sorbonne Paris Cité, INSERM, UMR_S 1142, LIMICS, Paris, France.

Abstract

OBJECTIVES:

 To summarize recent research and select the best papers published in 2017 in the field of computerized clinical decision support for the Decision Support section of the International Medical Informatics Association (IMIA) yearbook.

METHODS:

 A literature review was performed by searching two bibliographic databases for papers referring to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved bibliographic records, which 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 the section editors' evaluation.

RESULTS:

 Among the 1,194 retrieved papers, the entire review process resulted in the selection of four best papers. The first paper studies the impact of recency and of longitudinal extent of electronic health record (EHR) datasets used to train a data-driven predictive model of inpatient admission orders. The second paper presents a decision support tool for surgical team selection, relying on the history of surgical team members and the specific characteristics of the patient. The third paper compares three commercial drug-drug interaction knowledge bases, particularly against a reference list of highly-significant known interactions. The fourth paper focuses on supporting the diagnosis of postoperative delirium using an adaptation of the "anchor and learn" framework, which was applied in unstructured texts contained in EHRs.

CONCLUSIONS:

The conducted review illustrated also this year that research in the field of CDSS is very active. Of note is the increase in publications concerning data-driven CDSSs, as revealed by the review process and also reflected by the four papers that have been selected. This trend is in line with the current attention that "Big Data" and data-driven artificial intelligence have gained in the domain of health and CDSSs in particular.

PMID:
30157515
PMCID:
PMC6115238
DOI:
10.1055/s-0038-1641222
[Indexed for MEDLINE]
Free PMC Article

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