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Background Clinical decision support (CDS) tools—with easily understood and actionable information, at the point of care—are needed to help registered nurses (RNs) make evidence-based decisions. Not clear are the optimal formats of CDS tools. Thorough, preclinical testing is desirable to avoid costly errors associated with premature implementation in electronic health records. Objective The aims of this study were to determine feasibility of the protocol designed to compare multiple CDS formats and evaluate effects of numeracy and graph literacy on RN adoption of best practices and care planning time in a simulated environment. Methods In this pilot study, 60 RNs were randomly assigned to one of four CDS conditions (control, text, text + graph, and text + table) and asked to adjust the plan of care for two patient scenarios over three shifts. Fourteen best practices were identified for the two patients and sent as suggestions with evidence to the three CDS groups. Best practice adoption rates, care planning time, and their relationship to the RN's numeracy and graph literacy scores were assessed. Results CDS groups had a higher adoption rate of best practices ( p < .001) across all shifts and decreased care planning time in shifts 2 ( p = .01) and 3 ( p = .02) compared with the control group. Higher numeracy and graph literacy were associated with shorter care planning times under text + table ( p = .05) and text + graph ( p = .01) conditions. No significant differences were found between the three CDS groups on adoption rate and care planning time. Discussion This pilot study shows the feasibility of our protocol. Findings show preliminary evidence that CDS improves the efficiency and effectiveness of care planning decisions and that the optimal format may depend on individual RN characteristics. We recommend a study with sufficient power to compare different CDS formats and assess the impact of potential covariates on adoption rates and care planning time.

PURPOSE To develop methods for rapid and simultaneous design, testing, and management of multiple clinical decision support (CDS) features to aid nurse decision-making. METHODS We used quota sampling, think-aloud and cognitive interviews, and deductive and inductive coding of synchronized audio video data and archival libraries. FINDINGS Our methods and organizational tools allowed us to rapidly improve the usability, understandability, and usefulness of CDS in a generalizable sample of practicing nurses. CONCLUSIONS The method outlined allows the rapid integration of nursing terminology based electronic health record data into routine workflow and holds strong potential for improving patient outcomes. IMPLICATIONS FOR NURSING PRACTICE The methods and organizational tools for development of multiple CDS system features can be used to translate knowledge into practice.

We present findings of a comparative study of numeracy and graph literacy in a representative group of 60 practicing nurses. This article focuses on a fundamental concern related to the effectiveness of numeric information displayed in various features in the electronic health record during clinical workflow. Our findings suggest the need to consider numeracy and graph literacy when presenting numerical information as well as the potential for tailoring numeric display types to an individual's cognitive strengths.

PURPOSE To determine the acceptability, usefulness, and ease of use for four nursing clinical decision support interface prototypes. METHODS In a simulated hospital environment, 60 registered nurses (48 female; mean age = 33.7 ± 10.8; mean years of experience = 8.1 ± 9.7) participated in a randomized study with four study groups. Measures included acceptability, usefulness, and ease of use scales. FINDINGS Mean scores were high for acceptability, usefulness, and the ease of use for all four groups. Inexperienced participants (

Our long-term goal was to ensure nurse clinical decision support works as intended before full deployment in clinical practice. As part of a broader effort, this pilot project explored factors influencing acceptance/nonacceptance of eight clinical decision support suggestions displayed in an electronic health record-based nursing plan of care software prototype. A diverse sample of 21 nurses participated in this high-fidelity clinical simulation experience and completed a questionnaire to assess reasons for accepting/not accepting the clinical decision support suggestions. Of 168 total suggestions displayed during the experiment (eight for each of the 21 nurses), 123 (73.2%) were accepted, and 45 (26.8%) were not accepted. The mode number of acceptances by nurses was seven of eight, with only two of 21 nurses accepting all. The main reason for clinical decision support acceptance was the nurse's belief that the suggestions were good for the patient (100%), with other features providing secondary reinforcement. Reasons for nonacceptance were less clear, with fewer than half of the subjects indicating low confidence in the evidence. This study provides preliminary evidence that high-quality simulation and targeted questionnaires about specific clinical decision support selections offer a cost-effective means for testing before full deployment in clinical practice.

The investigation of underwater structures and natural features through Autonomous Underwater Vehicles (AUVs) is an expanding field with applications in archaeology, engineering, environmental sciences and astrobiology. Processing and analyzing the raw sonar data generated by automated surveys is challenging due to the presence of complex error sources like water chemistry, zero-depth variations, inertial navigation errors and multipath reflections. Furthermore, the complexity of the collected data makes it difficult to perform effective analysis on a standard display. Point clouds made up of hundreds of millions to billions of points are not uncommon. Highly interactive, immersive visualization is a desirable tool that researchers can use to improve the quality of a final sonar-based data product. In this paper we present a scalable toolkit for the processing and visualization of sonar point clouds on a cluster-based, large scale immersive visualization environment. The cluster is used simultaneously as a parallel processing platform that performs sonar beam-tracing of the source raw data, and as the rendering driver of the immersive display.

Constructing integrative visualizations that simultaneously cater to a variety of data types is challenging. Hybrid-reality environments blur the line between virtual environments and tiled display walls. They incorporate high-resolution, stereoscopic displays, which can be used to juxtapose large, heterogeneous datasets while providing a range of naturalistic interaction schemes. They thus empower designers to construct integrative visualizations that more effectively mash up 2D, 3D, temporal, and multivariate datasets.

In the domain of large-scale visualization instruments, hybrid reality environments (HREs) are a recent innovation that combines the best-in-class capabilities of immersive environments, with the best-in-class capabilities of ultra-high-resolution display walls. HREs create a seamless 2D/3D environment that supports both information-rich analysis as well as virtual reality simulation exploration at a resolution matching human visual acuity. Co-located research groups in HREs tend to work on a variety of tasks during a research session (sometimes in parallel), and these tasks require 2D data views, 3D views, linking between them and the ability to bring in (or hide) data quickly as needed. In this paper we present Omegalib, a software framework that facilitates application development on HREs. Omegalib is designed to support dynamic reconfigurability of the display environment, so that areas of the display can be interactively allocated to 2D or 3D workspaces as needed. Compared to existing frameworks and toolkits, Omegalib makes it possible to have multiple immersive applications running on a cluster-controlled display system, have different input sources dynamically routed to applications, and have rendering results optionally redirected to a distributed compositing manager. Omegalib supports pluggable front-ends, to simplify the integration of third-party libraries like OpenGL, OpenSceneGraph, and the Visualization Toolkit (VTK). We present examples of applications developed with Omegalib for the 74-megapixel, 72-tile CAVE2™ system, and show how a Hybrid Reality Environment proved effective in supporting work for a co-located research group in the environmental sciences.

In this paper we present our experience in designing and applying an evaluation protocol for assessing usability of a clinical decision support (CDS) system. The protocol is based on component-based usability testing, cognitive interviewing, and a rigorous coding scheme cross-referenced to a component library. We applied this protocol to evaluate alternate designs of a CDS interface for a nursing plan of care tool. The protocol allowed us to aggregate and analyze usability data at various granularity levels, supporting both validation of existing components and providing guidance for targeted redesign.