The PrescIT platform: An interoperable Clinical Decision Support System for ePrescription to Prevent Adverse Drug Reactions and Drug-Drug Interactions

Pantelis Natsiavas (), George Nikolaidis (), Jenny Pliatsika, Achilles Chytas, George Giannios, Haralampos Karanikas, Margarita Grammatikopoulou, Martha Zachariadou, Vlasios Dimitriadis, Spiros Nikolopoulos and Ioannis Kompatsiaris
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Pantelis Natsiavas: Institute of Applied Biosciences, Centre for Research and Technology Hellas
George Nikolaidis: Ergobyte SA
Jenny Pliatsika: Ergobyte SA
Achilles Chytas: Institute of Applied Biosciences, Centre for Research and Technology Hellas
George Giannios: Information Technologies Institute, Centre for Research and Technology Hellas
Haralampos Karanikas: University of Thessaly
Margarita Grammatikopoulou: Information Technologies Institute, Centre for Research and Technology Hellas
Martha Zachariadou: Ergobyte SA
Vlasios Dimitriadis: Institute of Applied Biosciences, Centre for Research and Technology Hellas
Spiros Nikolopoulos: Information Technologies Institute, Centre for Research and Technology Hellas
Ioannis Kompatsiaris: Information Technologies Institute, Centre for Research and Technology Hellas

Drug Safety, 2024, vol. 47, issue 10, No 8, 1059 pages

Abstract: Abstract Introduction Preventable medication errors have been proven to cause significant public health burden, and ePrescription is a key part of the process where medication errors and adverse effects could be prevented. Information systems and “intelligent” computational approaches could provide a valuable tool to prevent such errors with profound impact in clinical practice. Objectives The PrescIT platform is a Clinical Decision Support System (CDSS) that aims to facilitate the prevention of adverse drug reactions (ADRs) and drug-drug interactions (DDIs) in the phase of ePrescription in Greece. The proposed platform could be relatively easily localized for use in other contexts too. Methods The PrescIT platform is based on the use of Knowledge Engineering (ΚΕ) approaches, i.e., the use of Ontologies and Knowledge Graphs (KGs) developed upon openly available data sources. Open standards (i.e., RDF, OWL, SPARQL) are used for the development of the platform enabling the integration with already existing IT systems or for standalone use. The main KG is based on the use of DrugBank, MedDRA, SemMedDB and OpenPVSignal. In addition, the Business Process Management Notation (BPMN) has been used to model long-term therapeutic protocols used during the ePrescription process. Finally, the produced software has been pilot tested in three hospitals by 18 clinical professionals via in-person think-aloud sessions. Results The PrescIT platform has been successfully integrated in a transparent fashion in a proprietary Hospital Information System (HIS), and it has also been used as a standalone application. Furthermore, it has been successfully integrated with the Greek National ePrescription system. During the pilot phase, one psychiatric therapeutic protocol was used as a testbed to collect end-users’ feedback. Summarizing the feedback from the end-users, they have generally acknowledged the usefulness of such a system while also identifying some challenges in terms of usability and the overall user experience. Conclusions The PrescIT platform has been successfully deployed and piloted in real-world environments to evaluate its ability to support safer medication prescriptions.

Date: 2024
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DOI: 10.1007/s40264-024-01455-z

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