Precision Education: The Future of Lifelong Learning in Medicine.

ABSTRACT: The goal of medical education is to produce a physician workforce capable of delivering high-quality equitable care to diverse patient populations and communities. To achieve this aim amidst explosive growth in medical knowledge and increasingly complex medical care, a system of personalized and continuous learning, assessment, and feedback for trainees and practicing physicians is urgently needed. In this perspective, the authors build on prior work to advance a conceptual framework for such a system: precision education (PE).PE is a system that uses data and technology to transform lifelong learning by improving personalization, efficiency, and agency at the individual, program, and organization levels. PE "cycles" start with data inputs proactively gathered from new and existing sources, including assessments, educational activities, electronic medical records, patient care outcomes, and clinical practice patterns. Through technology-enabled analytics , insights are generated to drive precision interventions . At the individual level, such interventions include personalized just-in-time educational programming. Coaching is essential to provide feedback and increase learner participation and personalization. Outcomes are measured using assessment and evaluation of interventions at the individual, program, and organizational levels, with ongoing adjustment for repeated cycles of improvement. PE is rooted in patient, health system, and population data; promotes value-based care and health equity; and generates an adaptive learning culture.The authors suggest fundamental principles for PE, including promoting equity in structures and processes, learner agency, and integration with workflow (harmonization). Finally, the authors explore the immediate need to develop consensus-driven standards: rules of engagement between people, products, and entities that interact in these systems to ensure interoperability, data sharing, replicability, and scale of PE innovations.

Utilizing Natural Language Processing of Narrative Feedback to Develop a Predictive Model of Pre-Clerkship Performance: Lessons Learned.

Background: Natural language processing is a promising technique that can be used to create efficiencies in the review of narrative feedback to learners. The Feinberg School of Medicine has implemented formal review of pre-clerkship narrative feedback since 2014 through its portfolio assessment system but this process requires considerable time and effort. This article describes how natural language processing was used to build a predictive model of pre-clerkship student performance that can be utilized to assist competency committee reviews. Approach: The authors took an iterative and inductive approach to the analysis, which allowed them to identify characteristics of narrative feedback that are both predictive of performance and useful to faculty reviewers. Words and phrases were manually grouped into topics that represented concepts illustrating student performance. Topics were reviewed by experienced reviewers, tested for consistency across time, and checked to ensure they did not demonstrate bias. Outcomes: Sixteen topic groups of words and phrases were found to be predictive of performance. The best-fitting model used a combination of topic groups, word counts, and categorical ratings. The model had an AUC value of 0.92 on the training data and 0.88 on the test data. Reflection: A thoughtful, careful approach to using natural language processing was essential. Given the idiosyncrasies of narrative feedback in medical education, standard natural language processing packages were not adequate for predicting student outcomes. Rather, employing qualitative techniques including repeated member checking and iterative revision resulted in a useful and salient predictive model.

A Qualitative Analysis of Narrative Preclerkship Assessment Data to Evaluate Teamwork Skills.

Construct: Students entering the health professions require competency in teamwork. Background: Although many teamwork curricula and assessments exist, studies have not demonstrated robust longitudinal assessment of preclerkship students' teamwork skills and attitudes. Assessment portfolios may serve to fill this gap, but it is unknown how narrative comments within portfolios describe student teamwork behaviors. Approach: We performed a qualitative analysis of narrative data in 15 assessment portfolios. Student portfolios were randomly selected from 3 groups stratified by quantitative ratings of teamwork performance gathered from small-group and clinical preceptor assessment forms. Narrative data included peer and faculty feedback from these same forms. Data were coded for teamwork-related behaviors using a constant comparative approach combined with an identification of the valence of the coded statements as either "positive observation" or "suggestion for improvement." Results: Eight codes related to teamwork emerged: attitude and demeanor, information facilitation, leadership, preparation and dependability, professionalism, team orientation, values team member contributions, and nonspecific teamwork comments. The frequency of codes and valence varied across the 3 performance groups, with students in the low-performing group receiving more suggestions for improvement across all teamwork codes. Conclusions: Narrative data from assessment portfolios included specific descriptions of teamwork behavior, with important contributions provided by both faculty and peers. A variety of teamwork domains were represented. Such feedback as collected in an assessment portfolio can be used for longitudinal assessment of preclerkship student teamwork skills and attitudes.

What Is the Relationship Between a Preclerkship Portfolio Review and Later Performance in Clerkships?

PURPOSE: Medical educators struggle to find effective ways to assess essential competencies such as communication, professionalism, and teamwork. Portfolio-based assessment provides one method of addressing this problem by allowing faculty reviewers to judge performance, as based on a longitudinal record of student behavior. At the Feinberg School of Medicine, the portfolio system measures behavioral competence using multiple assessments collected over time. This study examines whether a preclerkship portfolio review is a valid method of identifying problematic student behavior affecting later performance in clerkships. METHOD: The authors divided students into two groups based on a summative preclerkship portfolio review in 2014: students who had concerning behavior in one or more competencies and students progressing satisfactorily. They compared how students in these groups later performed on two clerkship outcomes as of October 2015: final grades in required clerkships, and performance on a clerkship clinical composite score. They used Mann-Whitney tests and multiple linear regression to examine the relationship between portfolio review results and clerkship outcomes. They used USMLE Step 1 to control for knowledge acquisition. RESULTS: Students with concerning behavior preclerkship received significantly lower clerkship grades than students progressing satisfactorily (P = .002). They also scored significantly lower on the clinical composite score (P < .001). Regression analysis indicated concerning behavior was associated with lower clinical composite scores, even after controlling for knowledge acquisition. CONCLUSIONS: The results show a preclerkship portfolio review can identify behaviors that impact clerkship performance. A comprehensive portfolio system is a valid way to measure behavioral competencies.

Feasibility and Outcomes of Implementing a Portfolio Assessment System Alongside a Traditional Grading System.

PURPOSE: Portfolios are a powerful tool to collect and evaluate evidence of medical students' competence across time. However, comprehensive portfolio assessment systems that are implemented alongside traditional graded curricula at medical schools in the United States have not been described in the literature. This study describes the development and implementation of a longitudinal competency-based electronic portfolio system alongside a graded curriculum at a relatively large U.S. medical school. METHOD: In 2009, the authors developed a portfolio system that served as a repository for all student assessments organized by competency domain. Five competencies were selected for a preclerkship summative portfolio review. Students submitted reflections on their performance. In 2014, four clinical faculty members participated in standard-setting activities and used expert judgment and holistic review to rate students' competency achievement as "progressing toward competence," "progressing toward competence with some concern," or "progressing toward competence pending remediation." Follow-up surveys measured students' and faculty members' perceptions of the process. RESULTS: Faculty evaluated 156 portfolios and showed high levels of agreement in their ratings. The majority of students achieved the "progressing toward competence" benchmark in all competency areas. However, 31 students received at least one concerning rating, which was not reflected in their course grades. Students' perceptions of the system's ability to foster self-assessment were mixed. CONCLUSIONS: The portfolio review process allowed faculty to identify students with a concerning rating in a behavioral competency who would not have been identified in a traditional grading system. Identification of these students allows for intervention and early remediation.

Development of a metacognitive effort construct of empathy during clinical training: a longitudinal study of the factor structure of the Jefferson Scale of Empathy.

Empathy is crucial for effective clinical care but appears to decline during undergraduate medical training. Understanding the nature of this decline is necessary for addressing it. The Jefferson Scale of Empathy (JSE) is used to measure medical students' clinical empathy attitudes. One recent study described a 3-factor model of the JSE. This model was found in responses from matriculating medical students, but little is known about how the factor structure of the scale changes during clinical training. The Learning Environment Study is a longitudinal prospective study of two cohorts from 28 medical schools. At matriculation and at the end of each subsequent year, students self-reported clinical empathy attitudes using the JSE. Data from 4,797 students were randomly partitioned for exploratory (EFA) and confirmatory factor analyses using responses from preclinical and clinical years of medical school. Five models were compared for confirmatory factor analysis: two null models for control, the recent 3-factor model, and the two models resulting from the EFAs of preclinical and clinical year responses. Preclinical year responses yielded a 3-factor model similar to the recent 3-factor model. Clinical year responses yielded a 4-factor model ("feelings," "importance," "ease," and "metacognitive effort") suggesting changes in the structure of clinical empathy attitudes over time. Metacognitive effort showed the largest decline over time. The model is a better fit for both preclinical and clinical responses and may provide more insight into medical students' clinical empathy attitudes than other models. The emergence of metacognitive effort in the clinical years suggests empathy may become more nuanced for students after clinical exposure and may account for much of the observed decline in clinical empathy attitudes.