Comparative effectiveness research: a report from the institute of medicine. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The authors declare that they have no competing interests. ![]() These data governance processes were discussed in great detail and established as described with legal and regulatory officials from the university and data owners. For development and testing, several data owners provided fully identified patient data to the software developers, under HIPAA BAAs between the data owners and the university (also covering contractors). Data owners signed a legal document (“Master Consortium Agreement”) developed in collaboration with university legal counsel and each organization’ own legal counsel, specifying conditions under which the ETL tool is implemented, maintained, and used to produce research data sets within the research network. Any data released for research would require separate IRB approval and HIPAA data use agreements with data owners. The IRB approved an “infrastructure protocol”, indicating that the infrastructure was an approved mechanism for producing and transmitting HIPAA limited data sets for future research. Ethical and legal approvals for this work are as follows: 1. ![]() The ETL tool is implemented behind data owners’ firewalls, or behind firewalls of those with whom data owners have HIPAA Business Associate Agreements (BAAs). The work described in this paper describes ETL infrastructure development, testing, and implementation, but does not describe any research using this infrastructure. This approach offers a solution that lowers technical barriers that prevent data partners from participating in research data networks, and therefore, promotes the advancement of comparative effectiveness research using secondary electronic health data.ĭata harmonization Distributed research networks Electronic health records Extraction Rule-based ETL Transformation and loading.Įthics approval and consent to participate The results showed that ETL rule composition methods and the D-ETL engine offer a scalable solution for health data transformation via automatic query generation to harmonize source datasets.ĭ-ETL supports a flexible and transparent process to transform and load health data into a target data model. Processing workflows are directed by the ETL specifications guideline, developed by ETL designers with extensive knowledge of the structure and semantics of health data (i.e., "health data domain experts") and target common data model.ĭ-ETL was implemented to perform ETL operations that load data from various sources with different database schema structures into the Observational Medical Outcome Partnership (OMOP) common data model. This approach provides the flexibility required for the ETL of heterogeneous data, variations in semantic expertise, and transparency of transformation logic that are essential to implement ETL conventions across clinical research sharing networks. We designed and implemented a health data transformation and loading approach, which we refer to as Dynamic ETL (Extraction, Transformation and Loading) (D-ETL), that automates part of the process through use of scalable, reusable and customizable code, while retaining manual aspects of the process that requires knowledge of complex coding syntax. The expertise and scalable solutions needed to transform data to conform to network requirements are beyond the scope of many health care organizations and there is a need for practical tools that lower the barriers of data contribution to clinical research networks. ![]() Participating in multi-site clinical research networks requires EHR data to be restructured and transformed into a common format and standard terminologies, and optimally linked to other data sources. Electronic health records (EHRs) contain detailed clinical data stored in proprietary formats with non-standard codes and structures.
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