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Using Graph Transformations for Formalizing Prescriptions and Monitoring Adherence

  • Jens H. WeberEmail author
  • Simon Diemert
  • Morgan Price
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9151)

Abstract

Medication prescriptions are an important class of medical intervention orders. Their complexity ranges widely, depending on the nature of the patient’s condition and the prescribed substance(s). In today’s IT supported clinical environments, prescriptions are often authored electronically. Patient adherence to the prescribed medication regimen is a key determinant for the outcome of the intervention. Recently, an increasing number of information technologies are entering the consumer market with a goal to assist patients with adhering to their prescriptions. The effectiveness (and safety) of these technologies is limited to simplistic cases, however, because of the lack of a precise semantics for more complex prescription orders. To close this gap, we present an approach to formalize the meaning of medication prescriptions based on a graph-transformation system. This allows for more complex and variable prescriptions to be semantically coded and their adherence to be automatically monitored. Our work has been implemented within a prototypical prescribing tool and validated with domain experts.

Keywords

Graph Transformation Domain Specific Language Business Process Modeling Notation Prescription Order Computerize Provider Order Entry 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of VictoriaVictoriaCanada
  2. 2.Department of Family PracticeUniversity of British ColumbiaVancouverCanada

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