Natural Language Search Interfaces: Health Data Needs Single-Field Variable Search.

BACKGROUND: Data discovery, particularly the discovery of key variables and their inter-relationships, is key to secondary data analysis, and in-turn, the evolving field of data science. Interface designers have presumed that their users are domain experts, and so they have provided complex interfaces to support these "experts." Such interfaces hark back to a time when searches needed to be accurate first time as there was a high computational cost associated with each search. Our work is part of a governmental research initiative between the medical and social research funding bodies to improve the use of social data in medical research. OBJECTIVE: The cross-disciplinary nature of data science can make no assumptions regarding the domain expertise of a particular scientist, whose interests may intersect multiple domains. Here we consider the common requirement for scientists to seek archived data for secondary analysis. This has more in common with search needs of the "Google generation" than with their single-domain, single-tool forebears. Our study compares a Google-like interface with traditional ways of searching for noncomplex health data in a data archive. METHODS: Two user interfaces are evaluated for the same set of tasks in extracting data from surveys stored in the UK Data Archive (UKDA). One interface, Web search, is "Google-like," enabling users to browse, search for, and view metadata about study variables, whereas the other, traditional search, has standard multioption user interface. RESULTS: Using a comprehensive set of tasks with 20 volunteers, we found that the Web search interface met data discovery needs and expectations better than the traditional search. A task × interface repeated measures analysis showed a main effect indicating that answers found through the Web search interface were more likely to be correct (F1,19=37.3, P<.001), with a main effect of task (F3,57=6.3, P<.001). Further, participants completed the task significantly faster using the Web search interface (F1,19=18.0, P<.001). There was also a main effect of task (F2,38=4.1, P=.025, Greenhouse-Geisser correction applied). Overall, participants were asked to rate learnability, ease of use, and satisfaction. Paired mean comparisons showed that the Web search interface received significantly higher ratings than the traditional search interface for learnability (P=.002, 95% CI [0.6-2.4]), ease of use (P<.001, 95% CI [1.2-3.2]), and satisfaction (P<.001, 95% CI [1.8-3.5]). The results show superior cross-domain usability of Web search, which is consistent with its general familiarity and with enabling queries to be refined as the search proceeds, which treats serendipity as part of the refinement. CONCLUSIONS: The results provide clear evidence that data science should adopt single-field natural language search interfaces for variable search supporting in particular: query reformulation; data browsing; faceted search; surrogates; relevance feedback; summarization, analytics, and visual presentation.

The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud.

The Taverna workflow tool suite (http://www.taverna.org.uk) is designed to combine distributed Web Services and/or local tools into complex analysis pipelines. These pipelines can be executed on local desktop machines or through larger infrastructure (such as supercomputers, Grids or cloud environments), using the Taverna Server. In bioinformatics, Taverna workflows are typically used in the areas of high-throughput omics analyses (for example, proteomics or transcriptomics), or for evidence gathering methods involving text mining or data mining. Through Taverna, scientists have access to several thousand different tools and resources that are freely available from a large range of life science institutions. Once constructed, the workflows are reusable, executable bioinformatics protocols that can be shared, reused and repurposed. A repository of public workflows is available at http://www.myexperiment.org. This article provides an update to the Taverna tool suite, highlighting new features and developments in the workbench and the Taverna Server.

Patient-centric implementation of an electronic medication management system at a tertiary hospital in Western Sydney.

BACKGROUND: Traditional implementations of electronic medication management (EMM) systems have involved two common formats - a 'big bang' approach on the day of go-live, or a phased ward-by-ward approach over months. OBJECTIVE: To describe the patient-centric roll-out, a novel implementation model in converting from paper to EMM. METHOD: This model iteratively converted a large tertiary teaching hospital to electronic from paper medication charts, commencing the roll-out in the emergency department (ED). The tenet of 'one patient, one chart' was maintained with new patients commenced on EMM, while existing inpatients were maintained on paper charts until their discharge. In the second week, all other intake points commenced patients on EMM, and in the third week, all remaining patients were manually converted to EMM. The implementation was assessed with training completion rates, staff satisfaction surveys, focus group interviews and incident logs. RESULTS: At go-live, 79% of doctors, 68% of nurses and 90% of pharmacists were trained in the EMM system. The ED converted to electronic prescribing within 24 hours; by day 20, all patients were on EMM. Two hundred and thirty issues were logged, none critical, of which 22 were escalated. Of the 51,063 medications administered, there were 13 EMM-related clinical incidents including three double dosing errors, none of which led to an adverse event or death. Overall, 77% of staff surveyed were satisfied with the EMM implementation. CONCLUSIONS: The patient-centric roll-out model represents an innovative and safe approach with a single medication chart reducing transcription and improved medication safety for the patient and the organisation.