Putting undergraduate medical students in AI-CDSS designers’ shoes: An innovative teaching method to develop digital health critical thinking

Tsopra, Rosy; Peiffer-Smadja, Nathan; CHARLIER, Caroline; CAMPEOTTO, Florence; LEMOGNE, Cédric; RUSZNIEWSKI, Philippe; VIVIEN, Benoît; BURGUN, Anita

doi:10.1016/j.ijmedinf.2022.104980

Cited by 13 publications

(4 citation statements)

References 42 publications

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“…In this regard, pharmacists should integrate computerisation into their training in order to acquire knowledge in clinical informatics 35. These initiatives for training programmes in health informatics and AI fundamentals, described in the study of Tsopra et al , have been highly valued by undergraduate medical students and also by senior clinicians 36…”

Section: Discussionmentioning

confidence: 99%

Using machine learning or deep learning models in a hospital setting to detect inappropriate prescriptions: a systematic review

Johns,

Alkanj,

Beck

et al. 2023

Eur J Hosp Pharm

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ObjectivesThe emergence of artificial intelligence (AI) is catching the interest of hospital pharmacists. A massive collection of health data is now available to train AI models and hold the promise of disrupting codes and practices. The objective of this systematic review was to examine the state of the art of machine learning or deep learning models that detect inappropriate hospital medication orders.MethodsA systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. MEDLINE and Embase databases were searched from inception to May 2023. Studies were included if they reported and described an AI model intended for use by clinical pharmacists in hospitals. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).Results13 articles were selected after review: 12 studies were judged to have high risk of bias; 11 studies were published between 2020 and 2023; 8 were conducted in North America and Asia; 6 analysed orders and detected inappropriate prescriptions according to patient profiles and medication orders; and 7 detected specific inappropriate prescriptions, such as detecting antibiotic resistance, dosage abnormality in prescriptions, high alert drugs errors from prescriptions or predicting the risk of adverse drug events. Various AI models were used, mainly supervised learning techniques. The training datasets used were very heterogeneous; the length of study varied from 2 weeks to 7 years and the number of prescription orders analysed went from 31 to 5 804 192.ConclusionsThis systematic review points out that, to date, few original research studies report AI tools based on machine or deep learning in the field of hospital clinical pharmacy. However, these original articles, while preliminary, highlighted the potential value of integrating AI into clinical hospital pharmacy practice.

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Section: Discussionmentioning

confidence: 99%

Using machine learning or deep learning models in a hospital setting to detect inappropriate prescriptions: a systematic review

Johns,

Alkanj,

Beck

et al. 2023

Eur J Hosp Pharm

View full text Add to dashboard Cite

show abstract

“…Also, pharmacists should integrate computerization to their trainings in order to understand and acquire knowledge in clinical informatics to use such tools and be aware of the limits and the absolute need of an expert validation of the outcome (34). These initiatives for training programs in health informatics and AI fundamentals, described in the study of Tsopra et al, have been highly valued by undergraduate medical students but also by senior clinicians (35). Developers must also grasp the issues faced and tasks completed by the pharmacists: defining the requirements, the choice of the model and the dataset are the basis for developing functional AI-models.…”

Section: Discussionmentioning

confidence: 99%

Using Machine Learning or Deep Learning Models in a Hospital Setting to Detect Inappropriate Prescriptions: A Systematic Review

Johns

Godet

Alkanj

et al. 2023

Preprint

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Objectives: The emergence of artificial intelligence (AI) is catching the interest of hospitals pharmacists. Massive collection of pharmaceutical data is now available to train AI models and hold the promise of disrupting codes and practices. The objective of this systematic review was to examine the state of the art of machine learning or deep learning models that detect inappropriate hospital medication orders. Methods: A systematic review was conducted according to the PRISMA statement. PubMed and Cochrane database were searched from inception to May 2023. Studies were included if they reported and described an AI model intended for use by clinical pharmacists in hospitals. Results: After reviewing, thirteen articles were selected. Eleven studies were published between 2020 and 2023; eight were conducted in North America and Asia. Six analyzed orders and detected inappropriate prescriptions according to patient profiles and medication orders, seven detected specific inappropriate prescriptions. Various AI models were used, mainly supervised learning techniques. Conclusions: This systematic review points out that, to date, few original research studies report AI tools based on machine or deep learning in the field of hospital clinical pharmacy. However, these original articles, while preliminary, highlighted the potential value of integrating AI into clinical hospital pharmacy practice.

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“…Це спонукає студентів формулювати свої знання, розмірковувати про своє навчання і створювати особисте уявлення про нього. Використання стратегій навчання, орієнтованих на студента, сприяє оптимальному розвитку мислення, а також залученню студентів до їх прогресу [13]. Наприклад, навчальна програма, створена на основі аналізу кейсів, заохочує студентів бути активними в конструюванні своїх знань, одночасно розвиваючи свої навички, і при цьому синтезувати, ідентифікувати та вирішувати проблеми.…”

Section: Innovative Teaching Methods In Medical Education In Ukraine:...unclassified

Інноваційні Методи Викладання В Медичній Освіті України: Упровадження Та Результати

Герасименко,

Совірда,

Тюпа

2024

Перспективи та інновації науки

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Журнал «Перспективи та інновації науки» (Серія «Педагогіка», Серія «Психологія», Серія «Медицина») № 3(37) 2024143 innovations and innovative teaching methods in medical education in Ukraine. The results of the study were formed using a literature analysis method. The study results highlighted the incentives and barriers to innovation, described their impact on education and clinical practice, identified strategies that lead to innovation development, and established various levers that ensure their sustainability. Among the innovative teaching methods for future healthcare professionals, the following were highlighted: clinical practice, problem-based learning, interactive technologies, innovative management, leadership, and organizational factors. It can be concluded that the results of implementing innovative teaching methods in medical education in Ukraine are already showing positive results. Students become more active participants in the learning process, the quality of their education and training improves, and their motivation for independent learning increases. The implementation of innovative methods contributes to the improvement of the quality of medical education and the preparation of future professionals for the challenges they will face in medical practice.

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Putting undergraduate medical students in AI-CDSS designers’ shoes: An innovative teaching method to develop digital health critical thinking

Cited by 13 publications

References 42 publications

Using machine learning or deep learning models in a hospital setting to detect inappropriate prescriptions: a systematic review

Using machine learning or deep learning models in a hospital setting to detect inappropriate prescriptions: a systematic review

Using Machine Learning or Deep Learning Models in a Hospital Setting to Detect Inappropriate Prescriptions: A Systematic Review

Інноваційні Методи Викладання В Медичній Освіті України: Упровадження Та Результати

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