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Experiences of Creating Computable Knowledge Tutorials Using HL7 Clinical Quality Language.

Elisavet Andrikopoulou1, Philip Scott2

  • 1University of Portsmouth.

Studies in Health Technology and Informatics
|September 8, 2022
PubMed
Summary
This summary is machine-generated.

Developing reusable computable knowledge artifacts is key for improving healthcare systems. This work explores creating tutorials and use cases for the HL7 Clinical Quality Language to enable better clinical decision-making.

Keywords:
Clinical Quality Languageartefactcomputable knowledgetutorial

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Area of Science:

  • Health Informatics
  • Clinical Decision Support
  • Knowledge Engineering

Background:

  • Computable knowledge artifact development is often fragmented, leading to single-use solutions.
  • Libraries of computable knowledge artifacts can enhance Learning Health Systems.
  • Improved innovation and clinical decision-making are potential benefits.

Purpose of the Study:

  • To discuss the process of creating use cases for computable knowledge artifacts.
  • To develop tutorial materials for understanding dataset-outcome interactions.
  • To demonstrate the use of HL7 Clinical Quality Language for reusable code artifacts.

Main Methods:

  • Development of practical use cases for computable knowledge artifacts.
  • Creation of educational materials and tutorials.
  • Application of HL7 Clinical Quality Language for artifact generation.

Main Results:

  • A framework for developing reusable computable knowledge artifacts.
  • Tutorials explaining dataset-outcome interactions.
  • Demonstration of HL7 Clinical Quality Language for creating shareable code.

Conclusions:

  • Reusable computable knowledge artifacts can significantly benefit Learning Health Systems.
  • Educational resources are crucial for enabling clinicians and students to utilize these artifacts.
  • HL7 Clinical Quality Language provides a standardized approach for developing such artifacts.