| dc.creator | Natsiavas P., Stavropoulos T.G., Pliatsios A., Karanikas H., Gavriilidis G.I., Dimitriadis V.K., Nikolaidis G., Nikolopoulos S., Skapinakis P., Thireos E., Kompatsiaris I. | en |
| dc.date.accessioned | 2023-01-31T09:40:03Z | |
| dc.date.available | 2023-01-31T09:40:03Z | |
| dc.date.issued | 2021 | |
| dc.identifier | 10.3233/SHTI210358 | |
| dc.identifier.isbn | 9781643681856; 9781643681849 | |
| dc.identifier.uri | http://hdl.handle.net/11615/77117 | |
| dc.description.abstract | Clinical Decision Support Systems (CDSS) could play a prominent role in preventing Adverse Drug Reactions (ADRs) especially when integrated in larger healthcare systems (e.g. Electronic Health Record - EHR systems, Hospital Management Systems - HMS, e-Prescription systems etc.). This poster presents an approach to model Therapeutic Prescription Protocols (TPPs) via the Business Process Management Notation (BPMN), as part of the e-Prescription CDSS developed in the context of the PrescIT project. © 2021 European Federation for Medical Informatics (EFMI) and IOS Press. © 2021 European Federation for Medical Informatics (EFMI) and IOS Press. All rights reserved. | en |
| dc.language.iso | en | en |
| dc.source | Public Health and Informatics: Proceedings of MIE 2021 | en |
| dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107238271&doi=10.3233%2fSHTI210358&partnerID=40&md5=acad08a1e81cf2855f5cc7c930119515 | |
| dc.subject | adverse drug reaction | en |
| dc.subject | clinical decision support system | en |
| dc.subject | computer system | en |
| dc.subject | health care delivery | en |
| dc.subject | human | en |
| dc.subject | prescription | en |
| dc.subject | Computer Systems | en |
| dc.subject | Decision Support Systems, Clinical | en |
| dc.subject | Delivery of Health Care | en |
| dc.subject | Drug-Related Side Effects and Adverse Reactions | en |
| dc.subject | Humans | en |
| dc.subject | Prescriptions | en |
| dc.subject | IOS Press | en |
| dc.title | Using business process management notation to model therapeutic prescription protocols: The prescit approach | en |
| dc.type | bookChapter | en |
