Semantic Medical Prescriptions – Towards Intelligent and Interoperable Medical Prescriptions. Khalili, A. & Sedaghati, B. In IEEE Seventh International Conference on Semantic Computing (ICSC 2013), pages 347-354, Sept, 2013. doi abstract bibtex Medication errors are the most common type of medical errors in health-care domain. The use of electronic prescribing systems (e-prescribing) have resulted in significant reductions in such errors. However, dealing with the heterogeneity of available information sources is still one of the main challenges of e-prescription systems. There already exists different sources of information addressing different aspects of pharmaceutical research (e.g. chemical, pharmacological and pharmaceutical drug data, clinical trials, approved prescription drugs, drugs activity against drug targets. etc.). Handling these dynamic pieces of information within current e-prescription systems without bridging the existing pharmaceutical information islands is a cumbersome task. In this paper we present semantic medical prescriptions which are intelligent e-prescription documents enriched by dynamic drug-related meta-data thereby know about their content and the possible interactions. Semantic prescriptions provide an interoperable interface which helps patients, physicians, pharmacists, researchers, pharmaceutical and insurance companies to collaboratively improve the quality of pharmaceutical services by facilitating the process of shared decision making. In order to showcase the applicability of semantic prescriptions we present an application called Pharmer. Pharmer employs datasets such as DBpedia, Drug Bank, Daily Med and RxNorm to automatically detect the drugs in the prescriptions and to collect multidimensional data on them. We evaluate the feasibility of the Pharmer by conducting a usability evaluation and report on the quantitative and qualitative results of our survey.
@INPROCEEDINGS{Pharmer-Khalili-2013,
author = {Khalili, Ali and Sedaghati, Bita},
title = {Semantic Medical Prescriptions -- Towards Intelligent and Interoperable
Medical Prescriptions},
booktitle = {IEEE Seventh International Conference on Semantic Computing (ICSC
2013)},
year = {2013},
pages = {347-354},
month = {Sept},
abstract = {Medication errors are the most common type of medical errors in health-care
domain. The use of electronic prescribing systems (e-prescribing)
have resulted in significant reductions in such errors. However,
dealing with the heterogeneity of available information sources is
still one of the main challenges of e-prescription systems. There
already exists different sources of information addressing different
aspects of pharmaceutical research (e.g. chemical, pharmacological
and pharmaceutical drug data, clinical trials, approved prescription
drugs, drugs activity against drug targets. etc.). Handling these
dynamic pieces of information within current e-prescription systems
without bridging the existing pharmaceutical information islands
is a cumbersome task. In this paper we present semantic medical prescriptions
which are intelligent e-prescription documents enriched by dynamic
drug-related meta-data thereby know about their content and the possible
interactions. Semantic prescriptions provide an interoperable interface
which helps patients, physicians, pharmacists, researchers, pharmaceutical
and insurance companies to collaboratively improve the quality of
pharmaceutical services by facilitating the process of shared decision
making. In order to showcase the applicability of semantic prescriptions
we present an application called Pharmer. Pharmer employs datasets
such as DBpedia, Drug Bank, Daily Med and RxNorm to automatically
detect the drugs in the prescriptions and to collect multidimensional
data on them. We evaluate the feasibility of the Pharmer by conducting
a usability evaluation and report on the quantitative and qualitative
results of our survey.},
doi = {10.1109/ICSC.2013.66},
keywords = {document handling;drugs;medical information systems;DBpedia dataset;Daily
Med dataset;Drug Bank dataset;Pharmer application;RxNorm dataset;drug
detection;dynamic drug-related meta data;e-prescription systems;electronic
prescription systems;health care domain;intelligent e-prescription
documents;interoperable interface;medication errors;pharmaceutical
information islands;pharmaceutical research;semantic medical prescriptions;usability
evaluation;Companies;Drugs;Ontologies;Resource description framework;Semantics;Semantic
prescription;e-health;e-prescription;semantic annotation}
}
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The use of electronic prescribing systems (e-prescribing) have resulted in significant reductions in such errors. However, dealing with the heterogeneity of available information sources is still one of the main challenges of e-prescription systems. There already exists different sources of information addressing different aspects of pharmaceutical research (e.g. chemical, pharmacological and pharmaceutical drug data, clinical trials, approved prescription drugs, drugs activity against drug targets. etc.). Handling these dynamic pieces of information within current e-prescription systems without bridging the existing pharmaceutical information islands is a cumbersome task. In this paper we present semantic medical prescriptions which are intelligent e-prescription documents enriched by dynamic drug-related meta-data thereby know about their content and the possible interactions. Semantic prescriptions provide an interoperable interface which helps patients, physicians, pharmacists, researchers, pharmaceutical and insurance companies to collaboratively improve the quality of pharmaceutical services by facilitating the process of shared decision making. In order to showcase the applicability of semantic prescriptions we present an application called Pharmer. Pharmer employs datasets such as DBpedia, Drug Bank, Daily Med and RxNorm to automatically detect the drugs in the prescriptions and to collect multidimensional data on them. 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