Use of the i2b2 research query tool to conduct a matched case–control clinical research study: advantages, disadvantages and methodological considerations

Johnson, Emilie K.; Broder‐Fingert, Sarabeth; Tanpowpong, Pornthep; Bickel, Jonathan; Lightdale, Jenifer R.; Nelson, Caleb P.

doi:10.1186/1471-2288-14-16

Cited by 8 publications

(6 citation statements)

References 16 publications

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“… 6 – 11 Studies using EHR data have enabled investigators to examine the impact of diagnostic and therapeutic interventions in diverse real-world clinical settings. 12 – 18 …”

Section: Background and Significancementioning

confidence: 99%

A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data

Kahn¹,

Callahan²,

Barnard³

et al. 2016

eGEMs

357

431

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Objective:Harmonized data quality (DQ) assessment terms, methods, and reporting practices can establish a common understanding of the strengths and limitations of electronic health record (EHR) data for operational analytics, quality improvement, and research. Existing published DQ terms were harmonized to a comprehensive unified terminology with definitions and examples and organized into a conceptual framework to support a common approach to defining whether EHR data is ‘fit’ for specific uses.Materials and Methods:DQ publications, informatics and analytics experts, managers of established DQ programs, and operational manuals from several mature EHR-based research networks were reviewed to identify potential DQ terms and categories. Two face-to-face stakeholder meetings were used to vet an initial set of DQ terms and definitions that were grouped into an overall conceptual framework. Feedback received from data producers and users was used to construct a draft set of harmonized DQ terms and categories. Multiple rounds of iterative refinement resulted in a set of terms and organizing framework consisting of DQ categories, subcategories, terms, definitions, and examples. The harmonized terminology and logical framework’s inclusiveness was evaluated against ten published DQ terminologies.Results:Existing DQ terms were harmonized and organized into a framework by defining three DQ categories: (1) Conformance (2) Completeness and (3) Plausibility and two DQ assessment contexts: (1) Verification and (2) Validation. Conformance and Plausibility categories were further divided into subcategories. Each category and subcategory was defined with respect to whether the data may be verified with organizational data, or validated against an accepted gold standard, depending on proposed context and uses. The coverage of the harmonized DQ terminology was validated by successfully aligning to multiple published DQ terminologies.Discussion:Existing DQ concepts, community input, and expert review informed the development of a distinct set of terms, organized into categories and subcategories. The resulting DQ terms successfully encompassed a wide range of disparate DQ terminologies. Operational definitions were developed to provide guidance for implementing DQ assessment procedures. The resulting structure is an inclusive DQ framework for standardizing DQ assessment and reporting. While our analysis focused on the DQ issues often found in EHR data, the new terminology may be applicable to a wide range of electronic health data such as administrative, research, and patient-reported data.Conclusion:A consistent, common DQ terminology, organized into a logical framework, is an initial step in enabling data owners and users, patients, and policy makers to evaluate and communicate data quality findings in a well-defined manner with a shared vocabulary. Future work will leverage the framework and terminology to develop reusable data quality assessment and reporting methods.

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“… 6 – 11 Studies using EHR data have enabled investigators to examine the impact of diagnostic and therapeutic interventions in diverse real-world clinical settings. 12 – 18 …”

Section: Background and Significancementioning

confidence: 99%

A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data

Kahn¹,

Callahan²,

Barnard³

et al. 2016

eGEMs

357

431

View full text Add to dashboard Cite

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“…User-centered design process [27] and agile development [14] methodologies are used for the development of query interface. Currently, very few interfaces are in use directly by the investigators [4], [17]. Features of query interface design that facilitate its use by investigators include efficient query construction, obtaining counts in real time, query sharing and reuse, and data exploration.…”

Section: Discussionmentioning

confidence: 99%

“…It provides access to users to build queries to make a clinical profile and construct a study cohort based on EMR data by stringing series of search elements together. Nevertheless, i2b2 has its own limitations [4]. It cannot provide any data beyond the number of patients matching the search criteria and is incapable of running time-based queries, e.g., number of admissions in a month or events that happen before and after a clinical event or a laboratory result within a certain time period.…”

Section: Meteor: An Enterprise Health Informatics Environment To Suppmentioning

confidence: 99%

METEOR: An Enterprise Health Informatics Environment to Support Evidence-Based Medicine

Puppala

Chen

et al. 2015

IEEE Trans. Biomed. Eng.

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The twin pressures of cost containment in the healthcare market and new federal regulations and policies have led to the prioritization of the meaningful use of electronic health records in the United States. EDW and SIA layers on top of EDW are becoming an essential strategic tool to healthcare institutions and integrated delivery networks in order to support evidence-based medicine at the enterprise level.

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“…A growing consideration is research based on either extracting EHR information, or clinical interventions that add software or activate unused functionality within an EHR. However, only a few newly developed tools can work across software platforms [ 9 ]. Furthermore, until recently issues with interoperability have stymied efforts to extract data in toto from similar delivery sites because the extraction protocol may not work across the different EHRs of those sites.…”

Section: Introductionmentioning

confidence: 99%

Mapping Multi-Site Clinic Workflows to Design Systems-Enabled Interventions

Coronado

Retecki

Petrik

et al. 2017

eGEMs

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Objective:Variations in processes for different clinics and health systems can dramatically change the way preventive interventions are implemented. We present a method for documenting these variations using workflow diagrams and demonstrate how understanding workflow aided an electronic health record (EHR) embedded colorectal cancer screening intervention.Materials and Methods:We mapped variation in processes for ordering and documenting fecal testing, current colonoscopy, prior colonoscopies, and pathology results. This work was part of a multi-site cluster-randomized pragmatic trial to test a mailed approach to offering fecal testing at 26 safety net clinics (in eight organizations) in Oregon and Northern California. We created clinic-specific workflow diagrams and then distilled them into consolidated diagrams that captured the variations.Results:Clinics had varied practices for storing and using information about colorectal cancer screening. Developing workflow diagrams of key processes enabled clinics to find optimal ways to send fecal test kits to patients due for screening. The workflows informed the rollout of new EHR tools and identified best practices for data capture.Discussion:Diagramming workflows can have great utility when implementing and refining EHR tools for clinical practice, especially when doing so across multiple clinical sites. The process of developing the workflows uncovered successful practice recommendations and revealed limitations and potential effects of a research intervention.Conclusion:Our method of documenting clinical process variation might inform other EHR-powered, multi-site research and can improve data feedback from EHR systems to clinical caregivers.

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Use of the i2b2 research query tool to conduct a matched case–control clinical research study: advantages, disadvantages and methodological considerations

Cited by 8 publications

References 16 publications

A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data

A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data

METEOR: An Enterprise Health Informatics Environment to Support Evidence-Based Medicine

Mapping Multi-Site Clinic Workflows to Design Systems-Enabled Interventions

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