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.