Medical Students' Exposure to Ethics Conflicts in Clinical Training: Implications for Timing UME Bioethics Education.

While there is significant consensus that undergraduate medical education (UME) should include bioethics training, there is widespread debate about how to teach bioethics to medical students. Educators disagree about course methods and approaches, the topics that should be covered, and the effectiveness and metrics for UME ethics training. One issue that has received scant attention is the timing of bioethics education during medical training. The existing literature suggests that most medical ethics education occurs in the pre-clinical years. Follow-up studies indicate that medical students in their clinical rotations have little recall or ability to apply ethics concepts that were learned in their pre-clinical training. Trainees also report a desire for medical ethics to be taught in the context of practical application, which would suggest that the timing of pre-clinical ethics education is flawed. However, moving bioethics training to the clinical years should not be assumed to be the solution to the problems of recall and theory application. We argue that the effectiveness of timing bioethics education will depend on when medical students witness or experience particular categories of ethical dilemmas during their training. Our overarching hypothesis is that ethics education will be most effective when the bioethics training on a particular topic correlates to experiential exposure to that ethical issue. The purpose of our current study was to describe medical students exposure to particular categories of ethical conflicts, dilemmas, or issues. Our results may help bioethics educators better strategize about the most effective timing of medical ethics training in UME.

The tipping point: patients predisposed to Clostridium difficile infection and a hospital antimicrobial stewardship programme.

BACKGROUND: The incidence and severity of Clostridium difficile infection (CDI) have increased in recent years. Predictive models may help to identify at-risk patients before the onset of infection. Early identification of high-risk patients could help antimicrobial stewardship (AMS) programmes and other initiatives to better prevent C. difficile in these patients. AIM: To develop a predictive model that identifies patients at high risk for CDI at the time of hospitalization. This approach to early identification was evaluated to determine if it could improve upon a pre-existing AMS programme. METHODS: Logistic regression and receiver operating characteristic (ROC) curve analyses were used to develop an analytic model to predict risk for CDI at the time of hospitalization in a retrospective cohort of inpatients. The model was validated in a prospective cohort. Concurrence between the model's risk predictions and a pre-existing AMS programme was assessed. FINDINGS: The model identified 55% of patients who later tested positive as being at high risk for CDI at the time of admission. One in every 32 high-risk patients with potentially modifiable antimicrobial risk factors tested positive for CDI. Half (53%) tested positive before meeting the risk criteria for the hospital's AMS programme. CONCLUSION: Analytic models can identify most patients prospectively at the time of admission who later test positive for C. difficile. This approach to early identification may help AMS programmes to pursue susceptibility testing and modifications to antimicrobial therapies at an earlier stage in order to better prevent CDI.