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Background: In July 2015, Iran Food and Drug Administration convened a multi-stakeholder workgroup (workgroup) to help develop recommendations for electronic prescribing implementation in Iran., Objectives: In general, the consensus of the workgroup was to focus on solutions that incrementally reduce the burden on patients, providers, and payers, and require minimal rework by using national standards that have already been used for Health Information Interchange. We used a road mapping method which includes a number of systematic steps and is adapted from the standard scientific method. Medical Informatics Experts Developed protocols for Scoping Reviews, Systematic reviews and Health Technology Assessment study and then collected evidence from peer-reviewed scholarly journal publications and gray literature. Health Insurance companies representatives and Electronic Prescribing pilot studies executives were asked to report their experiences in the case of e-prescribing., Results: After five meetings, by comparing and contrasting the national and international evidence, the recommendations were finalized in expert panels. In this paper, we report recommendations from this roadmap.

Background: electronic prescription is shown to have many benefits in terms of reducing medication errors, improving patient safety, productivity, and resource management, but it may cause new errors and physician frustration if not designed and implemented properly. Improving usability and user-centered design is essential for physicians' adoption., Objectives: To enhance the efficiency of the e-prescribing system by reducing the risk of inappropriate selection of the medication and also to reduce the prescribing time and effort to reach the desired drug., Methods: Important data fields for predicting medications were determined through interviews with pharmacists. Among those, fields which were available in a claims dataset of 16 million prescriptions were extracted and were used to develop a neural network model to be used by a recommender system that displays the most probable medications on top of the drop-down list in the e-prescription application., Results: Offline and field evaluations both showed that this model could improve performance., Conclusion: smart recommenders systems can improve e-prescription usability, safety, and enhanced physicians' adoption.