Abstract Machine learning models may be integrated into clinical decision support (CDS)
systems to identify children at risk of specific diagnoses or clinical deterioration to provide …

PURPOSE To identify and quantify the barriers and facilitators to the use of clinical decision
support systems (CDSSs) by primary care professionals (PCPs). METHODS A mixed …

Artificial intelligence (AI) technology has been increasingly used in the implementation of
advanced Clinical Decision Support Systems (CDSS). Research demonstrated the potential …

Usability factors are a major obstacle to health information technology (IT) adoption. The
purpose of this paper is to review and categorize health IT usability study methods and to …

Many unintended and undesired consequences of Healthcare Information Technologies
(HIT) flow from interactions between the HIT and the healthcare organization's …

Background Workarounds circumvent or temporarily 'fix'perceived workflow hindrances to
meet a goal or to achieve it more readily. Behaviours fitting the definition of workarounds …

Background Recent advances in machine and deep learning based on an increased
availability of clinical data have fueled renewed interest in computerized clinical decision …

Background: eHealth is an application of information and communication technologies
across the whole range of functions that affect health. The benefits of eHealth (eg …

There is wide variability in the use and adoption of recommendations generated by
computerized clinical decision support systems (CDSSs) despite the benefits they may bring …

PURPOSE: Usability evaluations can improve the usability and workflow integration of
clinical decision support (CDS). Traditional usability testing using scripted scenarios with …