Electronic Medical Records: Provotype visualisation maximises clinical usability

The Electronic Medical Record (EMR) is the essential tool of the clinical consultation, effectively replacing the paper medical record. Since its gradual adoption in the early 2000s there has been a failure to achieve even moderate levels of EMR usability in clinical settings, resulting in a negative impact on clinical care, time efficiency and patient safety. This research explores how deeper collaboration with clinical users through participatory design, drawing on the disciplines of visual design, user experience (UX) design and visual analytics, might offer a more effective approach to this important problem. The lead researcher for this project is both a practising doctor and design researcher. Usability of two commercial EMR interfaces in the field of sexual health is explored through a mixed method survey, with responses used to inform the design of an interface provotype. This in turn is evaluated through repeat survey and ‘test-drive’ talk-aloud workshops. Results from the survey on two commercial EMR interfaces (n=49) revealed deep dissatisfaction particularly around issues of navigation, flow of consultation, frustration, safety, time-dependent and time-independent data, data complexity and data salience. Comparative provotype evaluation (n=63) showed that clinically-relevant visualisation offers marked gains in clinical usability and performance. This research argues for a re-imagining of the way we look at medical data during the clinical consultation so that the affordances and benefits of the digital format can be exploited more fully. It highlights the value of combining participatory design with visualisation to embed explicit, experiential and even tacit clinical knowledge into the EMR interface

Methods for visual mining of genomic and proteomic data atlases

<p>Abstract</p> <p>Background</p> <p>As the volume, complexity and diversity of the information that scientists work with on a daily basis continues to rise, so too does the requirement for new analytic software. The analytic software must solve the dichotomy that exists between the need to allow for a high level of scientific reasoning, and the requirement to have an intuitive and easy to use tool which does not require specialist, and often arduous, training to use. Information visualization provides a solution to this problem, as it allows for direct manipulation and interaction with diverse and complex data. The challenge addressing bioinformatics researches is how to apply this knowledge to data sets that are continually growing in a field that is rapidly changing.</p> <p>Results</p> <p>This paper discusses an approach to the development of visual mining tools capable of supporting the mining of massive data collections used in systems biology research, and also discusses lessons that have been learned providing tools for both local researchers and the wider community. Example tools were developed which are designed to enable the exploration and analyses of both proteomics and genomics based atlases. These atlases represent large repositories of raw and processed experiment data generated to support the identification of biomarkers through mass spectrometry (the PeptideAtlas) and the genomic characterization of cancer (The Cancer Genome Atlas). Specifically the tools are designed to allow for: the visual mining of thousands of mass spectrometry experiments, to assist in designing informed targeted protein assays; and the interactive analysis of hundreds of genomes, to explore the variations across different cancer genomes and cancer types.</p> <p>Conclusions</p> <p>The mining of massive repositories of biological data requires the development of new tools and techniques. Visual exploration of the large-scale atlas data sets allows researchers to mine data to find new meaning and make sense at scales from single samples to entire populations. Providing linked task specific views that allow a user to start from points of interest (from diseases to single genes) enables targeted exploration of thousands of spectra and genomes. As the composition of the atlases changes, and our understanding of the biology increase, new tasks will continually arise. It is therefore important to provide the means to make the data available in a suitable manner in as short a time as possible. We have done this through the use of common visualization workflows, into which we rapidly deploy visual tools. These visualizations follow common metaphors where possible to assist users in understanding the displayed data. Rapid development of tools and task specific views allows researchers to mine large-scale data almost as quickly as it is produced. Ultimately these visual tools enable new inferences, new analyses and further refinement of the large scale data being provided in atlases such as PeptideAtlas and The Cancer Genome Atlas.</p

Broadening Access to Large Online Databases by Generalizing Query Previews

Companies, government agencies, and other types of organizations are making their large databases available to the world over the Internet. Current database front-ends do not give users information about the distribution of data. This leads many users to waste time and network resources posing queries that have either zero-hit or mega-hit result sets. Query previews form a novel visual approach for browsing large databases. Query previews supply data distribution information about the database that is being searched and give continuous feedback about the size of the result set for the query as it is being formed. On the other hand, query previews use only a few pre-selected attributes of the database. The distribution information is displayed only on these attributes. Unfortunately, many databases are formed of numerous relations and attributes. This paper introduces a generalization of query previews. We allow users to browse all of the relations and attributes of a database using a hierarchical browser. Any of the attributes can be used to display the distribution information, making query previews applicable to many public online databases. (Also cross-referenced as UMIACS-TR-2000-32) (Also cross-referenced as HCIL-TR-2000-14

Interactive Pattern Search in Time Series

The need for pattern discovery in long time series data led researchers to develop algorithms for similarity search. Most of the literature about time series focuses on algorithms that index time series and bring the data into the main storage, thus providing fast information retrieval on large time series. This paper reviews the state of the art in visualizing time series, and focuses on techniques that enable users to visually and interactively query time series. Then, it presents TimeSearcher 2, a tool that enables users to explore multidimensional data using synchronized tables and graphs with overview+detail, filter the time series data to reduce the scope of the search, select an existing pattern to find similar occurrences, and interactively adjust similarity parameters to narrow the result set. This tool is an extension of previous work, TimeSearcher 1, which uses graphical timeboxes to interactively query time series data