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. Georg Thieme Verlag KG Stuttgart.
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. Georg Thieme Verlag KG Stuttgart.
Authors: Soufiane Lilih; Marieke Pereboom; Ruud T M van der Hoeven; Aukje K Mantel-Teeuwisse; Matthijs L Becker Journal: Int J Med Inform Date: 2016-10-04 Impact factor: 4.046
Authors: Taxiarchis Botsis; Christopher Jankosky; Deepa Arya; Kory Kreimeyer; Matthew Foster; Abhishek Pandey; Wei Wang; Guangfan Zhang; Richard Forshee; Ravi Goud; David Menschik; Mark Walderhaug; Emily Jane Woo; John Scott Journal: J Biomed Inform Date: 2016-07-28 Impact factor: 6.317
Authors: Mette Heringa; Hidde Siderius; Annemieke Floor-Schreudering; Peter A G M de Smet; Marcel L Bouvy Journal: J Am Med Inform Assoc Date: 2016-04-23 Impact factor: 4.497
Authors: Mary G Amato; Alejandra Salazar; Thu-Trang T Hickman; Arbor Jl Quist; Lynn A Volk; Adam Wright; Dustin McEvoy; William L Galanter; Ross Koppel; Beverly Loudin; Jason Adelman; John D McGreevey; David H Smith; David W Bates; Gordon D Schiff Journal: J Am Med Inform Assoc Date: 2017-03-01 Impact factor: 4.497
Authors: Dustin S McEvoy; Dean F Sittig; Thu-Trang Hickman; Skye Aaron; Angela Ai; Mary Amato; David W Bauer; Gregory M Fraser; Jeremy Harper; Angela Kennemer; Michael A Krall; Christoph U Lehmann; Sameer Malhotra; Daniel R Murphy; Brandi O'Kelley; Lipika Samal; Richard Schreiber; Hardeep Singh; Eric J Thomas; Carl V Vartian; Jennifer Westmorland; Allison B McCoy; Adam Wright Journal: J Am Med Inform Assoc Date: 2017-03-01 Impact factor: 4.497