Effect of Clinical Decision-Support Systems

TL;DR: The current status of CDSSs in oncology, the prospects and current challenges of BD analytics, and the promising role of integrated modern statistics and machine learning algorithms in predicting complex clinical endpoints, individualizing treatment rules, and optimizing dynamic personalized treatment regimens are presented.

TL;DR: There is variation in adoption of CDS tools across training levels, and attendees tended to accept the tool less but ordered more medications, which should be tailored to clinicians' training levels.

TL;DR: The barriers to widespread clinical application of WGS information are described, how clinical decision support (CDS) can be an important tool for overcoming these barriers, and clinical examples of how genome-enabled CDS can be used in the clinical setting are provided.

TL;DR: This work systematically review evidence on the costs and benefits associated with use of health information technology and to identify gaps in the literature in order to provide organizations, policymakers, clinicians, and consumers an understanding of the effect ofhealth information technology on clinical care.

TL;DR: Improvement in practitioner performance was associated with CDSSs that automatically prompted users compared with requiring users to activate the system and studies in which the authors were not the developers, as well as other factors.

TL;DR: Clinicians and other stakeholders should implement clinical decision support systems that incorporate these features whenever feasible and appropriate.

TL;DR: The CDSSs can enhance clinical performance for drug dosing, preventive care, and other aspects of medical care, but not convincingly for diagnosis.