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Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/27383
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DC FieldValueLanguage
dc.contributor.authorKhatami, Alireza-
dc.contributor.authorHolbrook, Anne-
dc.contributor.authorLevinson, Anthony J-
dc.contributor.authorKeshavjee, Karim-
dc.date.accessioned2022-02-24T21:17:46Z-
dc.date.available2022-02-24T17:09:34Z-
dc.date.available2022-02-24T21:17:46Z-
dc.date.issued2022-
dc.identifier.urihttp://hdl.handle.net/11375/27383-
dc.description.abstractPrescribing skills are a crucial competency in medical practice considering the increasing numbers of medications available and the increasingly complex patients with multiple diseases faced in clinical practice. Medical students need to become proficient in these skills during training, as required by medical licensing colleges. Not only is teaching the fundamentals of safe and cost-effective prescribing to medical students challenging but evaluating their prescribing skills by faculty members is difficult and time consuming. Covid-19 has accelerated the interest in clinically relevant online exams, including automated assessment of short answer style questions. The goal of this project was to design a software to automate the assessment of learners’ prescriptions written during low stakes formative assessments. After establishing the components of a legal prescription with multiple medications, and identifying the sources of errors in prescribing and prescribing assessment, we designed and validated an architecture and developed a prototype for automated parsing of learner prescriptions.en_US
dc.language.isoenen_US
dc.subjectmedical informaticsen_US
dc.subjectprescription writingen_US
dc.subjectsoftware architectureen_US
dc.subjectonline assessmenten_US
dc.subjectprescribing skillsen_US
dc.titleSoftware Architecture for Automated Assessment of Prescription Writingen_US
dc.typeArticleen_US
dc.typeReporten_US
dc.typeSoftwareen_US
dc.contributor.departmentMedicineen_US
Appears in Collections:Medicine Publications

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Prescription Parser Marking Software EFMI paper to MacSphere 24Feb2022.pdf
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