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Multi-model-based interactive authoring environment for creating shareable medical knowledge.

Taqdir Ali1, Maqbool Hussain2, Wajahat Ali Khan1

  • 1Department of Computer Science and Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu, Yongin-si 446-701, Gyeonggi-do, Republic of Korea.

Computer Methods and Programs in Biomedicine
|September 2, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces the Intelligent-Knowledge Authoring Tool (I-KAT) to simplify the creation of shareable and interoperable knowledge for clinical decision support systems (CDSS). I-KAT significantly reduces complexity and errors, improving physician usability for smarter healthcare.

Keywords:
Arden SyntaxHL7 vMRInteroperable knowledgeKnowledge authoring toolMedical Logic Module (MLM)SNOMED CTShareable knowledge

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems

Background:

  • Current clinical decision support systems (CDSS) lack user-friendly interfaces for physicians to create and share knowledge.
  • Physician-friendly abstraction is needed to reduce the complexity of creating shareable and interoperable knowledge for CDSS workflows.
  • Existing systems present challenges in knowledge shareability and interoperability, hindering seamless integration into healthcare environments.

Purpose of the Study:

  • To develop a user-friendly authoring environment for physicians to create shareable and interoperable knowledge for CDSS.
  • To enhance the creation of Arden Syntax Medical Logic Modules (MLMs) for intelligent decision-making within CDSS.
  • To overcome the knowledge acquisition complexity associated with current CDSS technologies.

Main Methods:

  • Proposed the Intelligent-Knowledge Authoring Tool (I-KAT) to address limitations of existing systems.
  • Achieved shareability through a knowledge base of MLMs using Arden Syntax.
  • Enhanced interoperability using standard data models and terminologies, including virtual medical record (vMR) and Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT), through a semantic reconciliation model (SRM).

Main Results:

  • System-centric evaluation showed I-KAT supported 82.05% of requirements, significantly outperforming existing systems (35.89%).
  • User-centric evaluation revealed I-KAT creation time for MLMs was 15 times faster than existing systems.
  • Physician error rates were drastically reduced, with an average of one error per MLM using I-KAT compared to 13 errors with existing systems.

Conclusions:

  • The developed I-KAT provides a user-friendly authoring environment for creating shareable and interoperable knowledge for CDSS.
  • The system effectively overcomes knowledge acquisition complexity by integrating state-of-the-art decision support standards.
  • I-KAT enhances ease of use for physicians, promoting greater adoption and effectiveness of CDSS in clinical practice.