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Software Architecture for Automated Assessment of Prescription Writing.
Khatami, Alireza; Holbrook, Anne; Levinson, Anthony J; Keshavjee, Karim.
  • Khatami A; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada.
  • Holbrook A; Division of Clinical Pharmacology & Toxicology, McMaster University, Hamilton, Canada.
  • Levinson AJ; Division of e-learning Innovation, McMaster University, Hamilton, Canada.
  • Keshavjee K; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada.
Stud Health Technol Inform ; 294: 780-784, 2022 May 25.
Article in English | MEDLINE | ID: covidwho-1865442
ABSTRACT
Prescribing 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. The COVID-19 pandemic 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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Type of study: Experimental Studies / Prognostic study Limits: Humans Language: English Journal: Stud Health Technol Inform Journal subject: Medical Informatics / Health Services Research Year: 2022 Document Type: Article Affiliation country: SHTI220583

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Type of study: Experimental Studies / Prognostic study Limits: Humans Language: English Journal: Stud Health Technol Inform Journal subject: Medical Informatics / Health Services Research Year: 2022 Document Type: Article Affiliation country: SHTI220583