The DEDUCE Guided Query tool: Providing simplified access to clinical data for research and quality improvement

Duke Health Technology Solutions, Duke University Health System, 2424 Erwin Road, Durham, NC 27705, USA.
Journal of Biomedical Informatics (Impact Factor: 2.19). 12/2010; 44(2):266-76. DOI: 10.1016/j.jbi.2010.11.008
Source: PubMed


In many healthcare organizations, comparative effectiveness research and quality improvement (QI) investigations are hampered by a lack of access to data created as a byproduct of patient care. Data collection often hinges upon either manual chart review or ad hoc requests to technical experts who support legacy clinical systems. In order to facilitate this needed capacity for data exploration at our institution (Duke University Health System), we have designed and deployed a robust Web application for cohort identification and data extraction--the Duke Enterprise Data Unified Content Explorer (DEDUCE). DEDUCE is envisioned as a simple, web-based environment that allows investigators access to administrative, financial, and clinical information generated during patient care. By using business intelligence tools to create a view into Duke Medicine's enterprise data warehouse, DEDUCE provides a Guided Query functionality using a wizard-like interface that lets users filter through millions of clinical records, explore aggregate reports, and, export extracts. Researchers and QI specialists can obtain detailed patient- and observation-level extracts without needing to understand structured query language or the underlying database model. Developers designing such tools must devote sufficient training and develop application safeguards to ensure that patient-centered clinical researchers understand when observation-level extracts should be used. This may mitigate the risk of data being misunderstood and consequently used in an improper fashion.

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Available from: Howard Shang, Feb 07, 2015
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    • "A series of extract-transform-load (ETL) processes integrate data from source systems to ensure consistency and quality and to minimize redundancy. The EDW is supported by a team of more than 25 staff, and has been described previously [8] "
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    ABSTRACT: Purpose: Data generated in the care of patients are widely used to support clinical research and quality improvement, which has hastened the development of self-service query tools. User interface design for such tools, execution of query activity, and underlying application architecture have not been widely reported, and existing tools reflect a wide heterogeneity of methods and technical frameworks. We describe the design, application architecture, and use of a self-service model for enterprise data delivery within Duke Medicine. Methods: Our query platform, the Duke Enterprise Data Unified Content Explorer (DEDUCE), supports enhanced data exploration, cohort identification, and data extraction from our enterprise data warehouse (EDW) using a series of modular environments that interact with a central keystone module, Cohort Manager (CM). A data-driven application architecture is implemented through three components: an application data dictionary, the concept of "smart dimensions", and dynamically-generated user interfaces. Results: DEDUCE CM allows flexible hierarchies of EDW queries within a grid-like workspace. A cohort "join" functionality allows switching between filters based on criteria occurring within or across patient encounters. To date, 674 users have been trained and activated in DEDUCE, and logon activity shows a steady increase, with variability between months. A comparison of filter conditions and export criteria shows that these activities have different patterns of usage across subject areas. Conclusions: Organizations with sophisticated EDWs may find that users benefit from development of advanced query functionality, complimentary to the user interfaces and infrastructure used in other well-published models. Driven by its EDW context, the DEDUCE application architecture was also designed to be responsive to source data and to allow modification through alterations in metadata rather than programming, allowing an agile response to source system changes.
    Journal of Biomedical Informatics 12/2014; 52. DOI:10.1016/j.jbi.2014.07.006 · 2.19 Impact Factor
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    • "Quantin et al. [49] Digital signature Not indicated Not indicated Not indicated Not indicated Authors design a search engine for a distributed database between health care institutions Horvath et al. [41] DEDUCE authenticates using Microsoft Windows Server 2003 (Redmond, WA, USA) Active Directory accounts, as these are employees' primary means of accessing workstations and clinical applications RBAC. When logging into the system, users must choose one of four user role types: "
    Dataset: YJBIN1973
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    • "Originally built as an administrative and financial database, the DSR holds 14 years of demographic, diagnostic and procedure data on over 3.8 million patients seen at Duke Medical Hospital, Durham Regional Hospital, and over 100 outpatient clinics in the Duke University Health System. The data have been deployed for secondary use in numerous research studies and quality improvement initiatives (Horvath et al., 2011). "
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    ABSTRACT: We develop a spatial Poisson hurdle model to explore geographic variation in emergency department (ED) visits while accounting for zero inflation. The model consists of two components: a Bernoulli component that models the probability of any ED use (i.e., at least one ED visit per year), and a truncated Poisson component that models the number of ED visits given use. Together, these components address both the abundance of zeros and the right-skewed nature of the nonzero counts. The model has a hierarchical structure that incorporates patient- and area-level covariates, as well as spatially correlated random effects for each areal unit. Because regions with high rates of ED use are likely to have high expected counts among users, we model the spatial random effects via a bivariate conditionally autoregressive (CAR) prior, which introduces dependence between the components and provides spatial smoothing and sharing of information across neighboring regions. Using a simulation study, we show that modeling the between-component correlation reduces bias in parameter estimates. We adopt a Bayesian estimation approach, and the model can be fit using standard Bayesian software. We apply the model to a study of patient and neighborhood factors influencing emergency department use in Durham County, North Carolina.
    Journal of the Royal Statistical Society Series A (Statistics in Society) 02/2013; 176(2):389-413. DOI:10.1111/j.1467-985X.2012.01039.x · 1.64 Impact Factor
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