AI-ssessment: Towards Assessment As a Sociotechnical System for Learning

Competency-based medical education: The spark to ignite healthcare's escape fire

High-value care is what patients deserve and what healthcare professionals should deliver. However, it is not what happens much of the time. Quality improvement master Dr. Don Berwick argued more than two decades ago that American healthcare needs an escape fire, which is a new way of seeing and acting in a crisis situation. While coined in the U.S. context, the analogy applies in other Western healthcare contexts as well. Therefore, in this paper, the authors revisit Berwick's analogy, arguing that medical education can, and should, provide the spark for such an escape fire across the globe. They assert that medical education can achieve this by fully embracing competency-based medical education (CBME) as a way to place medicine's focus on the patient. CBME targets training outcomes that prepare graduates to optimize patient care. The authors use the escape fire analogy to argue that medical educators must drop long-held approaches and tools; treat CBME implementation as an adaptive challenge rather than a technical fix; demand genuine, rich discussions and engagement about the path forward; and, above all, center the patient in all they do.

The Application of Adaptive Minimum Match k-Nearest Neighbors to Identify At-Risk Students in Health Professions Education

INTRODUCTION

When learners fail to reach milestones, educators often wonder if any warning signs could have allowed them to intervene sooner. Machine learning can predict which students are at risk for failing a high-stakes certification examination. If predictions can be made well before the examination, educators can meaningfully intervene before students take the examination to reduce their chances of failing.

METHODS

The authors used already-collected, first-year student assessment data from 5 cohorts in a single Master of Physician Assistant Studies program to implement an "adaptive minimum match" version of the k-nearest neighbors algorithm using changing numbers of neighbors to predict each student's future examination scores on the Physician Assistant National Certifying Exam (PANCE). Validation occurred in 2 ways by using leave-one-out cross-validation (LOOCV) and by evaluating predictions in a new cohort.

RESULTS

"Adaptive minimum match" version of the k-nearest neighbors algorithm achieved an accuracy of 93% in LOOCV. "Adaptive minimum match" version of the k-nearest neighbors algorithm generates a predicted PANCE score for each student one year before they take the examination. Students are classified into extra support, optional extra support, or no extra support categories. Then, one year remains to provide appropriate support to each category of student.

DISCUSSION

Predictive analytics can identify at-risk students who might need additional support or remediation before high-stakes certification examinations. Educators can use the included methods and code to generate predicted test outcomes for students. The authors recommend that educators use predictive modeling responsibly and transparently, as one of many tools used to support students. More research is needed to test alternative machine learning methods across a variety of educational programs.