OBJECTIVES: To systematically describe the resources available on preventing, detecting, and reversing prescribing cascades using a scoping review methodology. MEASUREMENTS: We searched Medline, EMBASE, Psy-chINFO, CINAHL, Cochrane Library, and Sociological Abstracts from inception until July 2017. Other searches (Google Scholar, hand searches) and expert consultations were performed for resources examining how to prevent, detect, or reverse prescribing cascades. We used these three categories along the prescribing continuum as an organizing framework to categorize and synthesize resources. RESULTS: Of 369 resources identified, 58 met inclusion criteria; 29 of these were categorized as preventing, 20 as detecting, and 9 as reversing prescribing cascades. Resources originated from 14 countries and mostly focused on older adults. The goal of preventing resources was to educate and increase general awareness of the concept of prescribing cascades as a way to prevent inappropriate prescribing and to illustrate application of the concept to specific drugs (e.g., anticholinergics) and conditions (e.g., inflammatory bowel disease). Detecting resources included original investigations or case reports that identified prescribing cascades using health administrative data, patient cohorts, and novel sources such as social media. Reversing prescribing cascade resources focused on the medication review process and deprescribing initiatives.
CONCLUSION:Prescribing cascades are a recognized problem internationally. By learning from the range of resources to prevent, detect, and reverse prescribing cascades, this review contributes to improving drug prescribing, especially in older adults.
Background: There has been growing acknowledgement that undergraduate medical education (UME) must play a formal role in instructing future physicians on the promises and limitations of artificial intelligence (AI), as these tools are integrated into medical practice.
Methods:We conducted an exploratory survey of medical students' knowledge of AI, perceptions on the role of AI in medicine, and preferences surrounding the integration of AI competencies into medical education. The survey was completed by 321 medical students (13.4% response rate) at four medical schools in Ontario.Results: Medical students are generally optimistic regarding AI's capabilities to carry out a variety of healthcare functions, from clinical to administrative, with reservations about specific task types such as personal counselling and empathetic care. They believe AI will raise novel ethical and social challenges. Students are concerned about how AI will affect the medical job market, with 25% responding that it was actively impacting their choice of specialty. Students agree that medical education must do more to prepare them for the impact of AI in medicine (79%), and the majority (68%) believe that this training should begin at the UME level.Conclusions: Medical students expect AI will be widely integrated into healthcare and are enthusiastic to obtain AI competencies in undergraduate medical education.
There is an almost complete lack of sex-specific reporting of data in clinical trials for dementia drug therapies, and no sex-specific reporting of adverse events. Sex-specific reporting of data should be required in drug trials to increase research value and ultimately inform more tailored prescribing for older adults.
Restrictions imposed by the COVID-19 pandemic have required medical educators to reimagine almost every aspect of undergraduate medical training, including curriculum delivery and assessments in a short timeline. In this personal view article, executive members of the University of Toronto medical student government and Faculty leads of pre-clerkship and clerkship education highlight five practical ways in which a student-Faculty partnership enabled the rapid and smooth adaptation of curricula during the COVID-19 pandemic. These included involving students as partners in decision making to contribute learner perspectives early, agile and collaborative meeting structures, frequent and consistent communication with the student body, providing learners with Faculty perspectives from the frontlines, and striking a balance in the level of feedback collected from students. These strategies may be of utility to medical administrators, educators, and student leaders in future crises affecting medical learners.
The COVID-19 pandemic has disrupted healthcare processes substantially including medical education, necessitating several changes along the spectrum of medical training. While this crisis presents major challenges to medical education, it is also an immense opportunity for innovation. In this commentary, Canadian medical students cast a spotlight on four domains of Canadian medical education which have seen substantial changes during the COVID-19 pandemic: medical school admissions, pre-clerkship content delivery, virtual care and telemedicine curricula, and the residency matching process. Using the 10 recommendations noted in the Association of Faculties of Medicine of Canada (AFMC) 2010 Future of Medical Education in Canada report as a guiding framework, we discuss why these changes represent key steps forward that should be preserved in medical education beyond the pandemic, and advocate for a continuous quality improvement approach to evaluate and implement these innovations.
BackgroundThere has been growing acknowledgement that undergraduate medical education (UME) must play a formal role in instructing future physicians on the promises and limitations of artificial intelligence (AI), as these tools are integrated into medical practice.MethodsWe conducted an exploratory survey of medical students’ knowledge of AI, perceptions on the role of AI in medicine, and preferences surrounding the integration of AI competencies into medical education. The survey was completed by 321 medical students (13.4% response rate) at four medical schools in Ontario.ResultsMedical students are generally optimistic regarding AI’s capabilities to carry out a variety of healthcare functions, from clinical to administrative, with reservations about specific task types such as personal counselling and empathetic care. They believe AI will raise novel ethical and social challenges. Students are concerned about how AI will affect the medical job market, with 25% responding that it was actively impacting their choice of specialty. Students agree that medical education must do more to prepare them for the impact of AI in medicine (79%), and the majority (68%) believe that this training should begin at the UME level.ConclusionsMedical students expect AI will be widely integrated into healthcare and are enthusiastic to obtain AI competencies in undergraduate medical education.
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