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A Framework for Classification of Electronic Health Data Extraction-Transformation-Loading Challenges in Data Network Participation

Authors:

Toan C. Ong ,

University of Colorado, Anschutz Medical Campus
About Toan C.
PhD
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Rosina Pradhananga,

Academy Health
About Rosina
MPH
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Erin G. Holve,

Department of Health Care Finance, Government of D.C.
About Erin G.
PhD
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Michael G. Kahn

University of Colorado, Anschutz Medical Campus
About Michael G.
MD, PhD
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Abstract

Background: Contributing health data to national, regional, and local networks or registries requires data stored in local systems with local structures and codes to be extracted, transformed, and loaded into a standard format called a Common Data Model (CDM). These processes called Extract, Transform, Load (ETL) require data partners or contributors to invest in costly technical resources with specialized skills in data models, terminologies, and programming. Given the wide range of tasks, skills, and technologies required to transform data into a CDM, a classification of ETL challenges can help identify needed resources, which in turn may encourage data partners with less-technical capabilities to participate in data-sharing networks.

Methods: We conducted key-informant interviews with data partner representatives to survey the ETL challenges faced in clinical data research networks (CDRNs) and registries. A list of ETL challenges, organized into six themes was vetted during a one-day workshop with a wide range of network stakeholders including data partners, researchers, and policy experts.

Results: We identified 24 technical ETL challenges related to the data sharing process. All of these ETL challenges were rated as “important” or “very important” by workshop participants using a five point Likert scale. Based on these findings, a framework for categorizing ETL challenges according to ETL phases, themes, and levels of data network participation was developed.

Conclusions: Overcoming ETL technical challenges require significant investments in a broad array of information technologies and human resources. Identifying these technical obstacles can inform optimal resource allocation to minimize the barriers and cost of entry for new data partners into extant networks, which in turn can expand data networks’ inclusiveness and diversity. This paper offers pertinent information and guiding framework that are relevant for data partners in ascertaining challenges associated with contributing data in data networks.

How to Cite: Ong TC, Pradhananga R, Holve EG, Kahn MG. A Framework for Classification of Electronic Health Data Extraction-Transformation-Loading Challenges in Data Network Participation. eGEMs (Generating Evidence & Methods to improve patient outcomes). 2017;5(1):10. DOI: http://doi.org/10.13063/egems.1295
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Published on 13 Jun 2017.
Peer Reviewed

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