Identifying clinical/translational research cohorts: ascertainment via querying an integrated multi-source database

Hurdle, John F.; Haroldsen, Stephen C.; Hammer, Andrew; Spigle, Cindy; Fraser, Alison; Mineau, Geraldine P.; Courdy, Samir

doi:10.1136/amiajnl-2012-001050

Cited by 27 publications

(18 citation statements)

References 22 publications

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“…Stanford University Medical Center has developed an anonymous patient cohort discovery tool (the Stanford Translational Research Integrated Database Environment [STRIDE]) that queries an EDW used by two hospitals and associated clinics [4,20]. STRIDE is a stand-alone Java application featuring a drag-and-drop interface.…”

Section: Discussionmentioning

confidence: 99%

“…However, such an interface must ensure a consistent environment for data exploration and related activities by users and provide a robust framework for complex query concepts. Despite the importance of this topic, issues surrounding the design of a user-friendly query interface, query path, and underlying application architecture have received scant attention in the literature, likely reflecting the wide heterogeneity of available methods and technical frameworks [4–7]. …”

Section: Introductionmentioning

confidence: 99%

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Modular design, application architecture, and usage of a self-service model for enterprise data delivery: The Duke Enterprise Data Unified Content Explorer (DEDUCE)

Horvath

Rusincovitch

Brinson

et al. 2014

Journal of Biomedical Informatics

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modular design, application architecture, and usage of a self-service model for enterprise data delivery: The Duke Enterprise Data Unified Content Explorer (DEDUCE)

Horvath

Rusincovitch

Brinson

et al. 2014

Journal of Biomedical Informatics

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“…Since human intermediaries are expensive and time-consuming, self-service query aids have been pursued in many institutions in recent years [41, 51, 73, 97, 101, 113, 123, 125, 127, 128]. Some are form-based, while others support queries in natural language [42, 51].…”

Section: Channelmentioning

confidence: 99%

“…The Informatics for Integrating Biology and the Bedside (i2b2) project represents the most widely adopted self-service EHR data retrieval system. The system’s terminology explorer and query builder allow the user to search the terminology for applicable terms and build cohorts using a frame system with Boolean constraints [52, 82, 88, 97, 113, 123–128]. Deshmukh et al studied the various types of data requests these applications were able to resolve.…”

Section: Channelmentioning

confidence: 99%

Facilitating biomedical researchers’ interrogation of electronic health record data: Ideas from outside of biomedical informatics

Hruby

Matsoukas

Cimino

et al. 2016

Journal of Biomedical Informatics

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Electronic health records (EHR) are a vital data resource for research uses, including cohort identification, phenotyping, pharmacovigilance, and public health surveillance. To realize the promise of EHR data for accelerating clinical research, it is imperative to enable efficient and autonomous EHR data interrogation by end users such as biomedical researchers. This paper surveys state-of-art approaches and key methodological considerations to this purpose. We adapted a previously published conceptual framework for interactive information retrieval, which defines three entities: user, channel, and source, by elaborating on channels for query formulation in the context of facilitating end users to interrogate EHR data. We show the current progress in biomedical informatics mainly lies in support for query execution and information modeling, primarily due to emphases on infrastructure development for data integration and data access via self-service query tools, but has neglected user support needed during iteratively query formulation processes, which can be costly and error-prone. In contrast, the information science literature has offered elaborate theories and methods for user modeling and query formulation support. The two bodies of literature are complementary, implying opportunities for cross-disciplinary idea exchange. On this basis, we outline the directions for future informatics research to improve our understanding of user needs and requirements for facilitating autonomous interrogation of EHR data by biomedical researchers. We suggest that cross-disciplinary translational research between biomedical informatics and information science can benefit our research in facilitating efficient data access in life sciences.

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“…An important initial step in these studies is case identification, i.e., the identification of patients who have the event of interest. Case identification is particularly challenging when using EMRs because data in the EMRs are not collected for this purpose [4]. Ideally, case identification is done on data that have been coded explicitly and correctly with a structured terminology such as the International Classification of Diseases version 9 (ICD-9).…”

Section: Introductionmentioning

confidence: 99%

Improving sensitivity of machine learning methods for automated case identification from free-text electronic medical records

Afzal

Schuemie

Blijderveen

et al. 2013

BMC Med Inform Decis Mak

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BackgroundDistinguishing cases from non-cases in free-text electronic medical records is an important initial step in observational epidemiological studies, but manual record validation is time-consuming and cumbersome. We compared different approaches to develop an automatic case identification system with high sensitivity to assist manual annotators.MethodsWe used four different machine-learning algorithms to build case identification systems for two data sets, one comprising hepatobiliary disease patients, the other acute renal failure patients. To improve the sensitivity of the systems, we varied the imbalance ratio between positive cases and negative cases using under- and over-sampling techniques, and applied cost-sensitive learning with various misclassification costs.ResultsFor the hepatobiliary data set, we obtained a high sensitivity of 0.95 (on a par with manual annotators, as compared to 0.91 for a baseline classifier) with specificity 0.56. For the acute renal failure data set, sensitivity increased from 0.69 to 0.89, with specificity 0.59. Performance differences between the various machine-learning algorithms were not large. Classifiers performed best when trained on data sets with imbalance ratio below 10.ConclusionsWe were able to achieve high sensitivity with moderate specificity for automatic case identification on two data sets of electronic medical records. Such a high-sensitive case identification system can be used as a pre-filter to significantly reduce the burden of manual record validation.

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Identifying clinical/translational research cohorts: ascertainment via querying an integrated multi-source database

Abstract: This query tool was designed to provide results as pre-research so that institutional review board approval would not be required. This architecture uses well-described technologies that should be within the reach of most institutions.

Cited by 27 publications

References 22 publications

Modular design, application architecture, and usage of a self-service model for enterprise data delivery: The Duke Enterprise Data Unified Content Explorer (DEDUCE)

Modular design, application architecture, and usage of a self-service model for enterprise data delivery: The Duke Enterprise Data Unified Content Explorer (DEDUCE)

Facilitating biomedical researchers’ interrogation of electronic health record data: Ideas from outside of biomedical informatics

Improving sensitivity of machine learning methods for automated case identification from free-text electronic medical records

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