Visual exploration of large data sets
Communications of the ACM
Information Visualization: Perception for Design
Information Visualization: Perception for Design
An intelligent, interactive tool for exploration and visualization of time-oriented security data
Proceedings of the 3rd international workshop on Visualization for computer security
Grand challenges in clinical decision support
Journal of Biomedical Informatics
Aligning temporal data by sentinel events: discovering patterns in electronic health records
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Visual Analytics to Optimize Patient-Population Evidence Delivery for Personalized Care
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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Comparative Effectiveness Research (CER) is designed to provide research evidence on the effectiveness and risks of different therapeutic options on the basis of data compiled from subpopulations of patients with similar medical conditions. Electronic Health Record (EHR) system contain large volumes of patient data that could be used for CER, but the data contained in EHR system are typically accessible only in formats that are not conducive to rapid synthesis and interpretation of therapeutic outcomes. In the time-pressured clinical setting, clinicians faced with large amounts of patient data in formats that are not readily interpretable often feel 'information overload'. Decision support tools that enable rapid access at the point of care to aggregate data on the most effective therapeutic outcomes derived from CER would greatly aid the clinical decision-making process and individualize patient care. In this manuscript, we highlight the role that visual analytics can play in CER-based clinical decision support. We developed a 'VisualDecisionLinc' (VDL) tool prototype that uses visual analytics to provide summarized CER-derived data views to facilitate rapid interpretation of large amounts of data. We highlight the flexibility that visual analytics offers to gain an overview of therapeutic options and outcomes and if needed, to instantly customize the evidence to the needs of the patient or clinician. The VDL tool uses visual analytics to help the clinician evaluate and understand the effectiveness and risk of different therapeutic options for different subpopulations of patients.