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Amplifying Domain Expertise in Clinical Data Pipelines.

Protiva Rahman1, Arnab Nandi1, Courtney Hebert1

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Digitizing health records enables data-driven decision support. This study focuses on amplifying domain expertise by improving how healthcare professionals interact with data throughout the pipeline.

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Area of Science:

  • Health Informatics
  • Human-Computer Interaction
  • Data Science

Background:

  • Digitization of health records facilitates data-driven algorithms for clinical decision support.
  • Current data interaction tools primarily serve data scientists, neglecting domain experts' unique needs.
  • Effective system design for domain experts requires optimizing their data interaction for high-impact tasks and reduced completion times.

Purpose of the Study:

  • To address the gap in data interaction tools for healthcare domain experts.
  • To propose methods for amplifying domain expertise within data analysis pipelines.
  • To provide a framework for designing better systems for clinical data utilization.

Main Methods:

  • Literature review across database, human-computer interaction, and visualization fields.
  • Identification of challenges and solutions at each stage of the data pipeline (curation, cleaning, analysis).
  • Development of a taxonomy for expertise amplification: summarization, guidance, interaction, and acceleration.

Main Results:

  • Identified significant challenges in enabling meaningful data interaction for domain experts.
  • Proposed a novel taxonomy for expertise amplification tailored to domain experts.
  • Demonstrated the practical application of the taxonomy through a case study.

Conclusions:

  • Optimizing data interaction for domain experts is crucial for effective clinical decision support.
  • The proposed expertise amplification taxonomy offers a valuable framework for system design.
  • Further research and development are needed to create tools that truly amplify clinical domain expertise.