Background: The UK General Medical Council has emphasized the lack of evidence on whether graduates from different UK medical schools perform differently in their clinical careers. Here we assess the performance of UK graduates who have taken MRCP(UK) Part 1 and Part 2, which are multiple-choice assessments, and PACES, an assessment using real and simulated patients of clinical examination skills and communication skills, and we explore the reasons for the differences between medical schools.

There exists an assumption that improving medical education will improve patient care. While seemingly logical, this premise has rarely been investigated. In this Invited Commentary, the authors propose the use of big data to test this assumption. The authors present a few example research studies linking education and patient care outcomes and argue that using big data may more easily facilitate the process needed to investigate this assumption. The authors also propose that collaboration is needed to link educational and health care data. They then introduce a grassroots initiative, inclusive of universities in one Canadian province and national licensing organizations that are working together to collect, organize, link, and analyze big data to study the relationship between pedagogical approaches to medical training and patient care outcomes. While the authors acknowledge the possible challenges and issues associated with harnessing big data, they believe that the benefits supersede these. There is a need for medical education research to go beyond the outcomes of training to study practice and clinical outcomes as well. Without a coordinated effort to harness big data, policy makers, regulators, medical educators, and researchers are left with sometimes costly guesses and assumptions about what works and what does not. As the social, time, and financial investments in medical education continue to increase, it is imperative to understand the relationship between education and health outcomes.

Automated essay scoring systems yield scores that consistently agree with those of human raters at a level as high, if not higher, as the level of agreement among human raters themselves. The system offers medical educators many benefits for scoring constructed-response tasks, such as improving the consistency of scoring, reducing the time required for scoring and reporting, minimising the costs of scoring, and providing students with immediate feedback on constructed-response tasks.

With the recent interest in competency-based education, educators are being challenged to develop more assessment opportunities. As such, there is increased demand for exam content development, which can be a very labor-intense process. An innovative solution to this challenge has been the use of automatic item generation (AIG) to develop multiple-choice questions (MCQs). In AIG, computer technology is used to generate test items from cognitive models (i.e. representations of the knowledge and skills that are required to solve a problem). The main advantage yielded by AIG is the efficiency in generating items. Although technology for AIG relies on a linear programming approach, the same principles can also be used to improve traditional committee-based processes used in the development of MCQs. Using this approach, content experts deconstruct their clinical reasoning process to develop a cognitive model which, in turn, is used to create MCQs. This approach is appealing because it: (1) is efficient; (2) has been shown to produce items with psychometric properties comparable to those generated using a traditional approach; and (3) can be used to assess higher order skills (i.e. application of knowledge). The purpose of this article is to provide a novel framework for the development of high-quality MCQs using cognitive models.

No abstract

Increasing physician and patient mobility has led to a move toward internationalization of standards for physician competence. The Institute for International Medical Education proposed a set of outcome-based standards for student performance, which were then measured using three assessment tools in eight leading schools in China: a 150-item multiple-choice examination, a 15-station OSCE and a 16-item faculty observation form. The purpose of this study was to empanel a group of experts to determine whether international student-level performance standards could be set. The IIME convened an international panel of experts in student education with specialty and geographic diversity. The group was split into two, with each sub-group establishing standards independently. After a discussion of the borderline student, the sub-groups established minimally acceptable cut-off scores for performance on the multiple-choice examination (Angoff and Hofstee methods), the OSCE station and global rating performance (modified Angoff method and holistic criterion reference), and faculty observation domains (holistic criterion reference). Panelists within each group set very similar standards for performance. In addition, the two independent parallel panels generated nearly identical performance standards. Cut-off scores changed little before and after being shown pilot data but standard deviations diminished. International experts agreed on a minimum set of competences for medical student performance. In addition, they were able to set consistent performance standards with multiple examination types. This provides an initial basis against which to compare physician performance internationally.

Previous research on AIG highlighted how this item development method can be used to produce high-quality stems and correct options for MCQ exams. The purpose of the current study was to describe, illustrate, and evaluate a method for modeling plausible but incorrect options. Evidence provided in this study demonstrates that AIG can produce psychometrically sound test items. More important, by adapting the distractors to match the unique features presented in the stem and correct option, the generation of MCQs using automated procedure has the potential to produce plausible distractors and yield large numbers of high-quality items for medical education.

No abstract