System for context-specific visualization of clinical practice guidelines (GuLiNav): Concept and software implementation

Fortmann, Jonas ; Lutz, Marlene ; Spreckelsen, Cord

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URN: urn:nbn:de:bsz:180-madoc-626768
Document Type: Article
Year of publication: 2022
The title of a journal, publication series: JMIR Formative Research
Volume: 6
Issue number: 6, e28013
Page range: 1-13
Place of publication: Toronto
Publishing house: JMIR Publications
ISSN: 2561-326X
Publication language: English
Institution: Business School > Data Science in the Economic and Social Sciences (Strohmaier, 2022-)
Pre-existing license: Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 330 Economics
610 Medicine and health
Keywords (English): clinical practice guideline , clinical decision support system , decision support techniqaues , computer-assisted decision making , guideline erepresentation , workflow control patterns , workflow , clinical , decision making , support systems , software
Abstract: Background: Clinical decision support systems often adopt and operationalize existing clinical practice guidelines leading to higher guideline availability, increased guideline adherence, and data integration. Most of these systems use an internal state-based model of a clinical practice guideline to derive recommendations but do not provide the user with comprehensive insight into the model. Objective: Here we present a novel approach based on dynamic guideline visualization that incorporates the individual patient’s current treatment context. Methods: We derived multiple requirements to be fulfilled by such an enhanced guideline visualization. Using business process and model notation as the representation format for computer-interpretable guidelines, a combination of graph-based representation and logical inferences is adopted for guideline processing. A context-specific guideline visualization is inferred using a business rules engine. Results: We implemented and piloted an algorithmic approach for guideline interpretation and processing. As a result of this interpretation, a context-specific guideline is derived and visualized. Our implementation can be used as a software library but also provides a representational state transfer interface. Spring, Camunda, and Drools served as the main frameworks for implementation. A formative usability evaluation of a demonstrator tool that uses the visualization yielded high acceptance among clinicians. Conclusions: The novel guideline processing and visualization concept proved to be technically feasible. The approach addresses known problems of guideline-based clinical decision support systems. Further research is necessary to evaluate the applicability of the approach in specific medical use cases.

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