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

LeafAI: query generator for clinical cohort discovery rivaling a human programmer

OBJECTIVE

Identifying study-eligible patients within clinical databases is a critical step in clinical research. However, accurate query design typically requires extensive technical and biomedical expertise. We sought to create a system capable of generating data model-agnostic queries while also providing novel logical reasoning capabilities for complex clinical trial eligibility criteria.

MATERIALS AND METHODS

The task of query creation from eligibility criteria requires solving several text-processing problems, including named entity recognition and relation extraction, sequence-to-sequence transformation, normalization, and reasoning. We incorporated hybrid deep learning and rule-based modules for these, as well as a knowledge base of the Unified Medical Language System (UMLS) and linked ontologies. To enable data-model agnostic query creation, we introduce a novel method for tagging database schema elements using UMLS concepts. To evaluate our system, called LeafAI, we compared the capability of LeafAI to a human database programmer to identify patients who had been enrolled in 8 clinical trials conducted at our institution. We measured performance by the number of actual enrolled patients matched by generated queries.

RESULTS

LeafAI matched a mean 43% of enrolled patients with 27 225 eligible across 8 clinical trials, compared to 27% matched and 14 587 eligible in queries by a human database programmer. The human programmer spent 26 total hours crafting queries compared to several minutes by LeafAI.

CONCLUSIONS

Our work contributes a state-of-the-art data model-agnostic query generation system capable of conditional reasoning using a knowledge base. We demonstrate that LeafAI can rival an experienced human programmer in finding patients eligible for clinical trials.

Classifying Clinical Trial Eligibility Criteria to Facilitate Phased Cohort Identification Using Clinical Data Repositories

A major challenge in using electronic health record repositories for research is the difficulty matching subject eligibility criteria to query capabilities of the repositories. We propose categories for study criteria corresponding to the effort needed for querying those criteria: "easy" (supporting automated queries), mixed (initial automated querying with manual review), "hard" (fully manual record review), and "impossible" or "point of enrollment" (not typically in health repositories). We obtained a sample of 292 criteria from 20 studies from ClinicalTrials.gov. Six independent reviewers, three each from two academic research institutions, rated criteria according to our four types. We observed high interrater reliability both within and between institutions. The analysis demonstrated typical features of criteria that map with varying levels of difficulty to repositories. We propose using these features to improve enrollment workflow through more standardized study criteria, self-service repository queries, and analyst-mediated retrievals.

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

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.

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

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.