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Area of Science:

  • Health Informatics
  • Data Management
  • Clinical Research

Background:

  • Contributing health data to networks requires Extract, Transform, Load (ETL) processes to convert local data into a Common Data Model (CDM).
  • These ETL processes demand substantial investment in technical resources, specialized skills, and programming expertise.
  • A classification of ETL challenges can identify resource needs and lower barriers for less-technical data partners.

Purpose of the Study:

  • To survey and classify the Extract, Transform, Load (ETL) challenges faced by data partners in clinical data research networks (CDRNs) and registries.
  • To develop a framework for categorizing these challenges to aid in resource allocation and network participation.

Main Methods:

  • Key-informant interviews with data partner representatives were conducted to identify Extract, Transform, Load (ETL) challenges.
  • A workshop with diverse network stakeholders, including data partners, researchers, and policy experts, was held to vet the identified challenges.
  • A five-point Likert scale was used by participants to rate the importance of each challenge.

Main Results:

  • Twenty-four technical Extract, Transform, Load (ETL) challenges were identified and rated as important or very important by participants.
  • A framework was developed to categorize these ETL challenges based on ETL phases, themes, and levels of data network participation.

Conclusions:

  • Addressing Extract, Transform, Load (ETL) technical challenges necessitates significant investment in information technology and human resources.
  • Identifying and categorizing these challenges can optimize resource allocation, reduce entry barriers for new data partners, and enhance network inclusivity.
  • This framework provides valuable guidance for data partners navigating the complexities of contributing data to networks.