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Clinical Guidelines as Executable and Interactive Workflows with FHIR-Compliant Health Data Input Using GLEAN

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Artificial Intelligence in Medicine (AIME 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13263))

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Abstract

By computerizing paper-based clinical guidelines on diagnosing and treating illnesses, knowledge-driven Clinical Decision Support (CDS) can issue salient and timely recommendations in line with the latest evidence. To access up-to-date patient health data, such CDS require interoperability with Electronic Health Records (EHR). The GLEAN model supports knowledge-based CDS by (a) encoding the guideline decision logic using Task Network Models (TNM) based on an extensible Finite State Machine (FSM); and (b) associating clinical tasks with HL7 FHIR resources that offer interoperability with FHIR-compliant EHR. In this demo, we show an online visualization tool that explains GLEAN CIG as visual and interactive workflows. Clinicians can dynamically submit HL7 FHIR patient data using the tool to drive the traversal of the workflow.

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Authors and Affiliations

  1. Dalhousie University, Halifax, NS, B3H 4R2, Canada

    William Van Woensel, Samina Abidi & Syed Sibte Raza Abidi

  2. Nova Scotia Health, Dalhousie University, Halifax, NS, Canada

    Karthik Tennankore & George Worthen

  3. Kidney Research Institute Nova Scotia (KRINS), Halifax, NS, Canada

    Karthik Tennankore

Authors
  1. William Van Woensel
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  2. Samina Abidi
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  3. Karthik Tennankore
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  4. George Worthen
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  5. Syed Sibte Raza Abidi
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Corresponding author

Correspondence to William Van Woensel .

Editor information

Editors and Affiliations

  1. University of Minnesota, Minneapolis, MN, USA

    Martin Michalowski

  2. Dalhousie University, Halifax, NS, Canada

    Syed Sibte Raza Abidi

  3. Dalhousie University, Halifax, NS, Canada

    Samina Abidi

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Van Woensel, W., Abidi, S., Tennankore, K., Worthen, G., Abidi, S.S.R. (2022). Clinical Guidelines as Executable and Interactive Workflows with FHIR-Compliant Health Data Input Using GLEAN. In: Michalowski, M., Abidi, S.S.R., Abidi, S. (eds) Artificial Intelligence in Medicine. AIME 2022. Lecture Notes in Computer Science(), vol 13263. Springer, Cham. https://doi.org/10.1007/978-3-031-09342-5_43

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  • DOI: https://doi.org/10.1007/978-3-031-09342-5_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09341-8

  • Online ISBN: 978-3-031-09342-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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