Nurses working in acute care hospitals, particularly those without addiction and psychiatric services, may benefit from continuing education on this important topic. J Contin Nurs Educ. 2017;48(4):175-183.
Competency-based assessment seeks to align measures of performance directly with desired learning outcomes based upon the needs of patients and the healthcare system. Recognizing that assessment methods profoundly influence student motivation and effort, it is critical to measure all desired aspects of performance throughout an individual's medical training. The Accreditation Council for Graduate Medical Education (ACGME) defined domains of competency for residency; the subsequent Milestones Project seeks to describe each learner's progress toward competence within each domain. Because the various clinical disciplines defined unique competencies and milestones within each domain, it is difficult for undergraduate medical education to adopt existing GME milestones language. This paper outlines the process undertaken by one medical school to design, implement and improve competency milestones for medical students. A team of assessment experts developed milestones for a set of focus competencies; these have now been monitored in medical students over two years. A unique digital dashboard enables individual, aggregate and longitudinal views of student progress by domain. Validation and continuous quality improvement cycles are based upon expert review, user feedback, and analysis of variation between students and between assessors. Experience to date indicates that milestone-based assessment has significant potential to guide the development of medical students.
Purpose The expanded use of clinical tools that incorporate artificial intelligence (AI) methods has generated calls for specific competencies for effective and ethical use. This qualitative study used expert interviews to define AI-related clinical competencies for health care professionals. Method In 2021, a multidisciplinary team interviewed 15 experts in the use of AI-based tools in health care settings about the clinical competencies health care professionals need to work effectively with such tools. Transcripts of the semistructured interviews were coded and thematically analyzed. Draft competency statements were developed and provided to the experts for feedback. The competencies were finalized using a consensus process across the research team. Results Six competency domain statements and 25 subcompetencies were formulated from the thematic analysis. The competency domain statements are: (1) basic knowledge of AI: explain what AI is and describe its health care applications; (2) social and ethical implications of AI: explain how social, economic, and political systems influence AI-based tools and how these relationships impact justice, equity, and ethics; (3) AI-enhanced clinical encounters: carry out AI-enhanced clinical encounters that integrate diverse sources of information in creating patient-centered care plans; (4) evidence-based evaluation of AI-based tools: evaluate the quality, accuracy, safety, contextual appropriateness, and biases of AI-based tools and their underlying data sets in providing care to patients and populations; (5) workflow analysis for AI-based tools: analyze and adapt to changes in teams, roles, responsibilities, and workflows resulting from implementation of AI-based tools; and (6) practice-based learning and improvement regarding AI-based tools: participate in continuing professional development and practice-based improvement activities related to use of AI tools in health care. Conclusions The 6 clinical competencies identified can be used to guide future teaching and learning programs to maximize the potential benefits of AI-based tools and diminish potential harms.
Introduction Incidents of bias and microaggressions are prevalent in the clinical setting and are disproportionately experienced by racial minorities, women, and medical students. These incidents contribute to burnout. Published efforts to address these incidents are growing, but gaps remain regarding the long-term efficacy of these curricular models. We developed and longitudinally evaluated a workshop that taught medical students a framework to respond to incidents of bias or microaggressions. Methods In October 2019, 102 Vanderbilt core clerkship medical students participated in an hour-long, interactive, case-based workshop centered around the 3 D's response behavior framework: (1) direct, (2) distract, and (3) delegate. Participants were surveyed before and after the training, and both qualitative and quantitative data were collected. A refresher workshop was offered 8 months later, which added two additional D's: delay and display discomfort. Results After the workshop, respondents’ knowledge of the assessed topics improved significantly, as did their confidence in addressing both personally experienced and witnessed incidents. Respondents initially indicated a high likelihood of using response behaviors to address incidents. The workshop did not consistently modify behavioral responses to experienced or witnessed incidents. Ninety-one percent of respondents agreed the workshop was effective. Discussion This workshop provided an effective curriculum to sustainably improve participant knowledge and confidence in responding to incidents of bias and microaggressions. This resource can be adopted by educators at other institutions.
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