VisualDecisionLinc: a visual analytics approach for comparative effectiveness-based clinical decision support in psychiatry.

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.

Patient electronic health data-driven approach to clinical decision support.

This article presents a novel visual analytics (VA)-based clinical decision support (CDS) tool prototype that was designed as a collaborative work between Renaissance Computing Institute and Duke University. Using Major Depressive Disorder data from MindLinc electronic health record system at Duke, the CDS tool shows an approach to leverage data from comparative population (patients with similar medical profile) to enhance a clinicians' decision making process at the point of care. The initial work is being extended in collaboration with the University of North Carolina CTSA to address the key challenges of CDS, as well as to show the use of VA to derive insight from large volumes of Electronic Health Record patient data.

Mapping topics and topic bursts in PNAS.

Scientific research is highly dynamic. New areas of science continually evolve; others gain or lose importance, merge, or split. Due to the steady increase in the number of scientific publications, it is hard to keep an overview of the structure and dynamic development of one's own field of science, much less all scientific domains. However, knowledge of "hot" topics, emergent research frontiers, or change of focus in certain areas is a critical component of resource allocation decisions in research laboratories, governmental institutions, and corporations. This paper demonstrates the utilization of Kleinberg's burst detection algorithm, co-word occurrence analysis, and graph layout techniques to generate maps that support the identification of major research topics and trends. The approach was applied to analyze and map the complete set of papers published in PNAS in the years 1982-2001. Six domain experts examined and commented on the resulting maps in an attempt to reconstruct the evolution of major research areas covered by PNAS.