Contributions from the 2016 Literature on Clinical Decision Support

Area of Science:

• Medical Informatics
• Health Information Technology
• Artificial Intelligence in Healthcare

Background:

• Computerized Clinical Decision Support Systems (CDSSs) are crucial for improving healthcare delivery.
• The field of CDSSs is rapidly evolving with diverse research dimensions.
• Identifying and disseminating high-impact research is essential for advancing the field.

Purpose of the Study:

• To summarize and select the best research papers on CDSSs published in 2016.
• To provide an overview of recent advancements and challenges in the CDSS domain.
• To inform the Decision Support section of the IMIA yearbook.

Main Methods:

• A comprehensive literature review of two bibliographic databases.
• Identification of candidate best papers from retrieved literature.
• Peer review by external experts and final selection by the IMIA editorial team.

Main Results:

• Four best papers were selected from 1,145 retrieved articles.
• Selected papers cover machine learning for prediction, linked-data for interoperability, drug-drug interaction alert variations, and multi-guideline modeling.
• Research spans guideline-based, machine-learning-based, and knowledge-fusion-based CDSSs.

Conclusions:

• CDSS research is active, addressing complex areas like multi-guideline management and interoperability.
• Significant challenges remain in integrating CDSSs into the digital healthcare ecosystem.
• Further work is needed on evidence, dissemination, and adoption of CDSS technologies for safer healthcare.