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An OMOP CDM-Based Relational Database of Clinical Research Eligibility Criteria.

ABSTRACT: Eligibility criteria are important for clinical research protocols or clinical practice guidelines for determining who qualify for studies and to whom clinical evidence is applicable, but the free-text format is not amenable for computational processing. In this paper, we described a practical method for transforming free-text clinical research eligibility criteria of Alzheimer's clinical trials into a structured relational database compliant with standards for medical terminologies and clinical data models. We utilized a hybrid natural language processing system and a concept normalization tool to extract medical terms in clinical research eligibility criteria and represent them using the OMOP Common Data Model (CDM) v5. We created a database schema design to store syntactic relations to facilitate efficient cohort queries. We further discussed the potential of applying this method to trials on other diseases and the promise of using it to accelerate clinical research with electronic health records.

SUBMITTER: Si Y

PROVIDER: S-EPMC5893219 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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An OMOP CDM-Based Relational Database of Clinical Research Eligibility Criteria.

Si Yuqi Y Weng Chunhua C

Studies in health technology and informatics 20170101

Eligibility criteria are important for clinical research protocols or clinical practice guidelines for determining who qualify for studies and to whom clinical evidence is applicable, but the free-text format is not amenable for computational processing. In this paper, we described a practical method for transforming free-text clinical research eligibility criteria of Alzheimer's clinical trials into a structured relational database compliant with standards for medical terminologies and clinical ...[more]

PMID: 29295240

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Project description:ObjectiveThe large-scale collection of observational data and digital technologies could help curb the COVID-19 pandemic. However, the coexistence of multiple Common Data Models (CDMs) and the lack of data extract, transform, and load (ETL) tool between different CDMs causes potential interoperability issue between different data systems. The objective of this study is to design, develop, and evaluate an ETL tool that transforms the PCORnet CDM format data into the OMOP CDM.MethodsWe developed an open-source ETL tool to facilitate the data conversion from the PCORnet CDM and the OMOP CDM. The ETL tool was evaluated using a dataset with 1000 patients randomly selected from the PCORnet CDM at Mayo Clinic. Information loss, data mapping accuracy, and gap analysis approaches were conducted to assess the performance of the ETL tool. We designed an experiment to conduct a real-world COVID-19 surveillance task to assess the feasibility of the ETL tool. We also assessed the capacity of the ETL tool for the COVID-19 data surveillance using data collection criteria of the MN EHR Consortium COVID-19 project.ResultsAfter the ETL process, all the records of 1000 patients from 18 PCORnet CDM tables were successfully transformed into 12 OMOP CDM tables. The information loss for all the concept mapping was less than 0.61%. The string mapping process for the unit concepts lost 2.84% records. Almost all the fields in the manual mapping process achieved 0% information loss, except the specialty concept mapping. Moreover, the mapping accuracy for all the fields were 100%. The COVID-19 surveillance task collected almost the same set of cases (99.3% overlaps) from the original PCORnet CDM and target OMOP CDM separately. Finally, all the data elements for MN EHR Consortium COVID-19 project could be captured from both the PCORnet CDM and the OMOP CDM.ConclusionWe demonstrated that our ETL tool could satisfy the data conversion requirements between the PCORnet CDM and the OMOP CDM. The outcome of the work would facilitate the data retrieval, communication, sharing, and analysis between different institutions for not only COVID-19 related project, but also other real-world evidence-based observational studies.

Dataset Information

An OMOP CDM-Based Relational Database of Clinical Research Eligibility Criteria.

Publications

An OMOP CDM-Based Relational Database of Clinical Research Eligibility Criteria.

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